Proceeding

Proceeding

Green Technology and Sustainability in Highway Engineering’ Ensuring Sustainable Moisture Damage Performance of Asphalt Paving Materials Professor Gordon Airey Nottingham Transportation Engineering Centre University of Nottingham United Kingdom Abstract Various approaches exist to tackle the issue of sustainability in construction and transportation in order to provide a better future for mankind. In terms of highway and pavement engineering these approaches can take the form of sustainable use of natural resources, the re-use and recycling of road materials as well as other waste construction materials and by-products and finally the development of green technologies and sustainable, environmentallyfriendly materials or processes. However, sustainability also involves the optimum use and improved durability of standard pavement materials and highway engineering designs with moisture damage of asphalt pavements still remaining as a major cause of premature failure of road pavements with its related cost implications in terms of both user costs and capital investment costs. Unfortunately, the mechanisms associated with moisture damage are not well understood due to the complex nature of the phenomenon and therefore an empirical approach is traditionally used to evaluate the potential of moisture damage in asphalt mixtures. In general, moisture damage of asphalt mixtures is associated with the adhesive and cohesive properties of the material and how the presence of water affects these mechanisms. Although mechanical test procedures exist to quantify the moisture damage of asphalt mixtures, they do not measure the fundamental material properties related to adhesion and cohesion and therefore can experience inaccuracies when correlated with actual field performance. This keynote presentation provides a detailed account of the successfully combination of a moisture damage assessment procedure with two fundamental concepts of thermodynamic surface free energy and adhesion fracture energy measurements to better understand and predict the resistance to moisture damage of asphalt mixture pavement materials. These approaches and techniques provide valuable insight into the effective sustainable performance of our highway network.

Innovative and Sustainable Construction Technology. Prof. Dr. Muhd. Zaimi Abd Majid Dean of Construction Research Alliance, Universiti Teknologi Malaysia Abstract Sustainable agenda for future generation is critical in maintaining a liveable world. The world has been plagued with problems such as pollution, carbon emissions, depleting resources that can significantly influence our economy and society. Construction industry is one of the sectors that contribute to the above problems. The expenditure on construction sector is within the range of 5% to 10% of Gross World Product (GWP). Hence, there is a need to create and develop innovative tools for construction activities in order to reduce the impact. This paper discussed several innovative R&D products developed by the construction research team over one and half decades at the Universiti Teknologi Malaysia. The tools have been developed in order to engage and encourage the practices in sustaining the environment, economy and resources. Several challenges such as carbon emissions, wastage, depletion of resources been recognized.

Controlled Blasting in Urban Development Areas Mr. Shahar Effendi Abdullah Azizi State Director, Department of Mineral and Geoscience Malaysia, Johor Abstract Blasting in urban development areas such for construction preparatory work sites, tunnel, roads and so forth is very highly risky and dangerous activity. As such it has to be carried out with full responsibility and carried out in a controlled and systematic manner so as to ensure the safety and wellbeing of the people and also to protect against any damages to properties within its vicinity. This presentation will briefly outlined the role carried out by the Department of Mineral and Geosciences in rendering advice to local authorities in ensuring that blasting work in development areas are carried out in a controlled and professional manner.

Towards Sustainable Engineering Education; The Role of Bung Hatta University Prof. Dr. Niki Lukviarman, SE, Akt., MBA. Rector, Universitas Bung Hatta Indonesia Abstract This presentation is about the role of Bung Hatta University in providing higher educational services particularly within the Sumatra region from the view of engineering education. In the advancement of global technology engineers plays major role in global development towards alleviating societal life. The presentation describes major projects related to engineering issues (a) the Indian Ocean project to optimize resource potentials of the Indian Ocean, (b) the bamboo project as an alternative building materials to minimize deforestation activities in Indonesia, (c) mass transportation problems in West Sumatra region to anticipate future traffic jam, (d) environmental sustainability issues related to CO2 emission reduction program for building and urban planning, and (e) risk management disaster program for West Sumatra region as the vulnerable region for the natural disaster such as earthquake and tsunami. The proposed projects are the focus of research conducted at Bung Hatta University, which highlights the role of the Bung Hatta University in scientific world.

Sustainable Transportation Systems in Malaysia: Issues and Possible Strategies Assoc. Prof. Dr. Othman Che Puan Universiti Teknologi Malaysia Abstract Transportation sector, on one end, is one of the main contributors to the economic growth of a country. Such a sector is the mean for mobility of global population. On the other end, it has been regarded as one of the sectors that consumed most of the energy in Malaysia. Such a system, if not properly designed and built, can have adverse effects on environmental and social aspects. This presentation highlights the issues and strategies that can be considered in developing transportation systems to fulfil the sustainable transport agenda.

Photogrammetry – A Tool for Civil Engineers Assoc. Prof. Dr. M ushairry Mustaffar Universiti Teknologi Malaysia Abstract In its broadest term, photogrammetry is a technique of obtaining three-dimensional dimensions from a series of overlapping two-dimensional images. Developed in the early XIXth century for mapping applications, photogrammetry made use of conventional photographs and specially designed mechanical instruments in order to re-create the three-dimensional model of the scene. New photogrammetric methods, image mediums and instrumentation developed in recent years paved new applications other than mapping. One such application is in the field of civil engineering application. This paper discusses the current state of photogrammetry in civil engineering applications. Specific examples in highway and traffic engineering, geotechnical engineering and building & structures as alternative or supplementary methods will be presented. It is hoped that, this paper would help to explain the role of photogrammetry and how the civil engineers could reap its advantages in alleviating civil engineering problems.

ORGANISING COMMITTEE PATRONS Prof. Ir Dr. Wahid Omar (Vice Chancellor of UTM) Dato’ Ir. Hj Ismail Bin Md Salleh (Director General of LLM) ADVISORS Prof. Dr. Shahrin bin Mohammad (Dean of Faculty of Civil Engineering) Prof. Dr. Muhd Zaimi Abd.Majid (Research Dean of Construction Research Alliance) Prof. Ir. Dr. Mahmood Md. Tahir (Director, Construction Research Centre) CHAIRMAN Prof. Dr. Salihuddin Radin Sumadi Prof. Dr. Mohd Rosli Hainin DEPUTY CHAIRMAN Assoc. Prof. Dr. Rosli Mohamad Zin Assoc. Prof. Dr. Edy Tonnizam Mohammad SECRETARIES Dr. Ahmad Safuan A. Rashid Dr. Shek Poi Ngian ORGANISING COMMITTEE Mdm. Fauziah Kasim Dr. Rini Asnida Abdullah Dr. Nor Zurairahetty Binti Mohd Yunus Dr. Sitti Asmah Hassan Dr. Norhidayah Abdul Hassan Dr. Mohd Yunus Ishak Dr. Abdullah Zawawi Awang Dr. Ahmad Kueh Beng Hong Mr. Mohd Affendi Ismail Ms. Siti Asiah Tukirin Ms. Fathiah Abd. Nasir

CONFERENCE INTERNATIONAL SCIENTIFIC COMMITTEE

Prof. Dr. Kenichi Soga, University of Cambridge, United Kingdom

Prof. Roger Plank, Sheffield University, United Kingdom

Prof. Dr. Gordon Dan Airey, University of Nottingham, United Kingdom

Prof. Jang-Ho Jay Kim, Yonsei University, South Korea

Prof. M. Mahendran,Queensland University of Technology, Australia

Dr. Anis Saggaff, Sriwijaya University, Indonesia

Dr. Darius Wanatowski, University of Nottingham, United Kingdom

Prof. Jean-Paul Lebet, Ecole polytechnique fédérale de Lausanne (EPFL)

Prof. Mohan Kumaraswamy, University of Hong Kong

Dr. Ir. Eko Alvares Z,MSA, Universitas Bung Hatta, Indonesia

Prof. Ronald Mccaffer, University of Loughborough, United Kingdom

Dr. Ir. Henry Nasution, M.T, Universitas Bung Hatta, Indonesia

Prof Chimay Anumba, The Pennsylvania State University, U.S.A

Ir. Hendri Warman, MSCE, Universitas Bung Hatta, Indonesia

Prof Miroslaw Skibniewski, University of Maryland, U.S.A

Ir. Wardi, M. Si, Universitas Bung Hatta, Indonesia

Yulcherlina, S.T, M.T., Universitas Bung Hatta, Indonesia

Martalius Peli, S.T, MSc, Universitas Bung Hatta, Indonesia

Event Management UTMSPACE

ACKNOWLEDGEMENT The Organizing Committee of the GEOCON2013 would like to express our sincere gratitude to the following for their help, support and generous contribution: Y.B. Datuk Dr. Abu Bakar bin Mohamad Diah Deputy Minister of Ministry of Science, Technology and Innovation (MOSTI) YBhg, Prof. Ir. Dr. Wahid Omar Vice Chancellor Universiti Teknologi Malaysia Construction Research Alliance Universiti Teknologi Malaysia Faculty of Civil Engineering Universiti Teknologi Malaysia Lembaga Lebuhraya Malaysia (LLM) Universitas Bung Hatta, Indonesia Universiti Islam Sultan Agung, Indonesia Universiti Sriwijaya, Indonesia Menteri Besar Johor, Malaysia Chancellory Universiti Teknologi Malaysia Infra Desa Johor Selia Selenggara Sdn. Bhd. UEM Sunrise Berhad Iskandar Regional Development Authority (IRDA) Presenters and Participants and to all who have contributed directly or indirectly to make this event a success.

ID125 MANAGING THE IMPACTS OF FOREX FLUCTUATIONS ON CONSTRUCTION BUSINESS PERFORMANCE: AN ORGANISATION CAPABILITIES PERSPECTIVE M. A. Mohamed1, B. Trigunarsyah2, M. Teo3 PhD Student, School of Civil Engineering and Built Environment, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia1 E-mail: [email protected] Associate Professor, School of Civil Engineering and Built Environment, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia2 E-mail: 1 [email protected] Lecturer, School of Civil Engineering and Built Environment, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia E-mail: 3 [email protected] Construction projects are a high risk business activity. When undertaking projects in an international context, it is further complicated by the risk of fluctuations in the foreign exchange rates (FOREX). Construction business performance is affected by these fluctuations. They affect progress and cause delays, which in turn create problems for subcontractors, namely cost overruns, disputes, arbitration, total abandonment and litigation. FOREX fluctuations also cause the price of raw materials to increase, leading the cost overruns. Managing FOREX risk is critical and past research have focused on the need for adequate insurance, careful planning and management, and foreign exchange futures hedging to overcome issues that have been caused by the FOREX risk. Analysis of FOREX risk in international construction business usually focused only on issues at the project level. There is currently a lack of understanding of Organisational Capabilities (OC) that is required to manage the impacts of FOREX risk, which when examined, are seen in isolation. This paper attempts to bridge the gap by discussing on managing the impacts of FOREX fluctuations on the international construction business, focusing on the OC perspective. Key words: Foreign Exchange Rate (FOREX), Organisational Capabilities (OC), Business Performance, Construction Business

Introduction Implementing construction projects in a foreign country is a high risk business activity (Zhang, 2011). Foreign exchange rate (FOREX) risk is considered as significant challenge in the international construction business, as business is strongly affected by these fluctuations (Dikme et al., 2007; Han et al., 2010; Kim et al., 2009; Ling and Hoi, 2006). When undertaking international projects, construction organisations must take into account the substantial risks related to FOREX fluctuations that affect their business performances. The financial situation of construction organisations can be adversely affected when the currency of exchange rates fluctuate (Ling & Hoi, 2006). It was found that one of the predominant causes of delay for international construction projects is financial difficulties experienced by the construction organisations (Ismail et al., 2012), which were caused by fluctuations in FOREX. This in turn created some other problems, namely cost overrun, disputes, arbitration, total abandonment and litigation. Not only fluctuations in FOREX cause the price of raw materials to increase but they are also the important cause of cost overruns in projects (Fidan &

Dikmen, 2011). Therefore, fluctuations of FOREX is a real challenge for construction organisations doing business in overseas markets (Ofori, 2000). However, the impacts of FOREX risk on the construction business are still not well managed (Ehrlich et al., 2012). A literature review has shown that most attempts to analyse the risks to the construction business due to FOREX fluctuations has focused mainly on issues at the project level, rather than at the organisation level (Yee & Cheah, 2006). When the focus was on the latter, it was in terms of only one capability either financial capability, procurement capability, marketing capability, operational capability or technological capability. Past studies (Bing & Tiong, 1999; Dobrzykowski, 2012; Morgan, 2009; Nath et al., 2010; Wang et al., 2006; Zou et al., 2009) reflect this tendency. It means that the focus should go beyond mitigation itself; it should also ask whether organisations have relevant capabilities to implement the mitigation measures required across related areas of Organisational Capabilities (OC). This paper aims to better understand in managing the impacts of FOREX fluctuations on the construction business performance, and also to identify the OC that are required to manage the risk. This is a part of study to develop a framework of OC and business performance to mitigate the impact of FOREX fluctuations. Based on the previous research, the discussion in this paper has been divided into three main sections: (1) international construction business; (2) impacts of FOREX and; (3) managing the impacts of FOREX fluctuations. International Construction Business Nowadays, the international construction market is worth an estimate of US$7.5 trillion. By 2020, it is estimated to be US$12.7 trillion that is an increasing of 70% of growth. Global Construction Perspectives and Oxford Economics reported that emerging construction markets in the Asia Pacific will grow by an estimated 125% by 2020. As some domestics market shrink, the growth of international construction has created many opportunities for construction organisations. These organisations are increasingly looking for opportunities to have international projects as it helps to further expand their business and in the same time challenge themselves in the international construction markets. As the opportunities are increased, the risk in international construction are also increased significantly (Han et al.).This business is influenced by different kind of risks such as economic, politic, society, legal and culture (Gunhan & Arditi, 2005; Han et al., 2007). In international construction business, economic risks can be classified as any circumstances that relate to materials supply, labour supply, equipment availability, inflations, tariffs, fiscal policies and FOREX (Wang et al., 2000). Ling and Hoi (2006) mentioned that FOREX fluctuations are a part of specific examples of unique risks beside the other typical risks faced by the contractors who undertook projects in India. Ling and Lim (2007) further suggest that out of the nine identified economic and financial risks; fluctuations in FOREX is one of the most significant risks that affect foreign construction organisations operating in China’s construction industry. These situations mentioned are examples that show FOREX fluctuations are significant risks that give impacts to the construction business performance (Gunhan & Arditi, 2005). Impacts of FOREX Fluctuations on the Construction Business Fluctuations in FOREX impact construction organisations directly in their foreign exchange exposure. To illustrate, when an organisation is involved in international

construction business, not only the FOREX fluctuations are likely to jeopardize the project’s finances, but at the same time, they affect the organisation’s financial situation. This happens because the overall expenses and income for a particular project is payable in the local currency, whereas, the loan repayments may be made in a foreign currency. This means that a fall in exchange rates could be very dangerous for the project as well as for the construction organisations. This in turn has a significant effect on the organisations’ fundamental financial structure, reducing its market value or profit margins, or potentially disrupting any ongoing and future project (Eiteman et al.,2006). Fluctuation in FOREX modifies the profitability of trade and investment deals (Kapila & Hendrickson, 2001). In contrast, Ling and Lim (2007) state that risks arising from fluctuations in FOREX are not likely to occur and are not severe. In their study, experts representing Singapore’s construction organisations that conduct construction business in China believed that their profits would not be drastically affected. However, these findings are mainly associated with the unique country, specific benefits of working on projects in China because of the China government’s tight exchange rate controls. Clearly, it is important to manage FOREX risks, as these risks may cause negative impacts on cash flow, endanger a project’s viability and limit profitability, mainly for construction organisations which are involved in projects abroad (Xenidis and Angelides, 2005). However, a large number of construction organisations continue to underestimate the risks and neglect to put in place measures to manage the impacts of FOREX fluctuations (Ehrlich et al., 2012). In this case, the real barrier can be the lack of OC, because implementing the mitigation action to manage the impacts of FOREX fluctuations requires these OCs (Eiteman et al., 2006). Managing the impacts of FOREX fluctuations FOREX risk might exist within any situation in the construction business. The impacts of this risk are not only at the project level but also significantly to the construction organisations as well. There is a need for appropriate measures to mitigate the impacts of FOREX risk in assuring a stable business performance in construction organisations (Ehrlich et al., 2012). In identifying the preferred methods of FOREX risk management, international construction organisations were found to focus on minimizing their transaction exposure by (1) attempting to denominate contracts in hard currency, especially U.S. dollars; (2) implementing experimental contingencies in contracts; (3) matching roughly the same in magnitude and timing between cash inflows and outflows from operating and financing and; (4) using contractual hedges, especially forward exchange contracts (Ahn et al., 2009). It has been suggested that hedging FOREX risk can contribute to minimizing overall foreign exchange exposure (Loderer & Pichler, 2000) and increase organisation value (Stulz, 1984) in such circumstances. Ehrlich et al. (2012) in their survey to Small and Medium Entrepreneurs (SMEs) construction organisations reveal that 74% of respondents claimed that construction organisations are practicing FOREX risk management. Instead of implementing risk management on FOREX, the respondents’ hedge their foreign exchange exposure, with the result being very low, at only 36%. Similarly to Berkman et al.’s (1997) findings, 53% of large firms (market equity value greater than US$250 million) use derivatives but only 36% of small firms (market equity value less than US$50 million) take the same action. This means that, the organisations’ capabilities play a role in mitigating the impacts of FOREX

fluctuations where perhaps large scale organisations have more ability compared to the small scale organisations. Continuing the idea of managing risk of FOREX fluctuations, there are recommendations to be included into contracts: (1) clear contractual provisions for the method of payment; (2) agreed exchange rates and; (3) currency of payments. A typical payment scheme for consultants is 30% upon signing the consultancy contract, 60% upon completion of the design phase, and 10% retention sum. Normally, the consultant’s fees are paid by the time the main contract is awarded. Thus, the risk is managed by having a front-loaded payment scheme to reduce exposure (Ling & Hoang, 2010). Similarly, Wang and Tiong (2000) in their study which focused on China’s construction organisations found that part of an effective measure to be used in managing the FOREX risk is using dual-currency contracts with certain portions of the tariff to be paid in Renminbi (RMB) and other transactions denominated in a foreign currency. Construction organisations can manage FOREX risk by making payments in the same currency as their revenues (Eiteman et al., 2006; Ling & Lim, 2007). However, there are construction organisations who still not really serious about managing the impacts of FOREX fluctuations (Ehrlich, Ph, et al., 2012; Eiteman et al., 2006). In this case, the real barrier can be lack of OC, because in implementing the mitigation action to the impacts of FOREX fluctuations requires these OCs (Eiteman et al., 2004). Hence, this shows that OC play important roles in the effectiveness of mitigating the impacts of FOREX fluctuations and sustaining construction business performance. It can be seen that even though many mitigation actions are recommended, organisations which do not have relevant capabilities to the subject matter will still be affected by FOREX risk. Concluding Remarks Most of the previous research only focused on the competitive advantages and recommending the mitigation actions on the impacts of FOREX fluctuation but lack of understanding on OC’s role to manage the risk. This study is to fill the gaps by focusing on the necessary OC for international construction business to withstand the FOREX fluctuations. This will contribute to enhancing the theoretical understanding of OC; and providing a mechanism for construction organisations – enabling these organisations to recognize their OC, which are relevant to and can be implemented in order to mitigate the impacts of FOREX fluctuations in sustaining their business performances. References Ahn, Y.H., Holley, P., & Kang, J. S. (2009). Risk Management of Exchange Rates in International Construction. International Journal of Construction Education and Research, 5(1), 24–44. doi:10.1080/15578770902717550 Aje, O. I., Odusami, K. T., & Ogunsemi, D. R. (2009). The impact of contractors’ management capability on cost and time performance of construction projects in Nigeria. Journal of Financial Management of Property and Construction, 14(2), 171–187. doi:10.1108/13664380910977619 Bing, L., & Tiong, R. (1999). Risk management model for international construction joint ventures. Journal of Construction Engineering and Management, (October), 377–384. Retrieved from http://ascelibrary.org/doi/abs/10.1061/(ASCE)07339364(1999)125:5(377)

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ID163 ASSESSMENT OF BID EVALUATION STRATEGIES IN INDIAN CONSTRUCTION INDUSTRY USING ANALYTICAL HIERARCHY PROCESS (AHP) MODEL N. Agrawal 1 and S. V. Barai 2 1

Graduate Student and Conference Speaker, Department of Civil Engineering, IIT Kharagpur, India Email: [email protected]

2

Professor, Department of Civil Engineering, IIT Kharagpur, India Email: [email protected]

Bid evaluation is one of the most important aspects of any construction contract and lies at the core of the construction project. It involves overall planning, controlling, coordinating and assessing the cost needed to execute a project from inception to completion aimed at meeting a client’s requirement. Bid evaluation is thus a multivariate decision making process involving several underlying criteria, which in a multi-agent contracting environment presents a challenging search problem in terms of identifying an appropriate bidding strategy for the underlying project. In recent decades, several mathematical methods have been developed for selecting the most preferable alternatives. The present paper aims to use the MCDM approach to assess the various bid evaluation strategies in Indian construction industry through Analytical Hierarchy Process (AHP). Analytic Hierarchy Process method was developed in the beginning of 1870s as a tool in the decision-making analysis based on MCDM methodology, by Thomas Saaty. It was designed to assist the planners in resolving complex decision making problems where a large number of planners along with a number of criteria exist in a number of specific time periods and is therefore aptly suited for use in our bidding context. An initial survey is carried amongst the construction and contracting firms in India to assess the various bid evaluation strategies being used in the Indian construction sector in terms of the underlying criteria. The data so obtained is used to create a hierarchical model for selection of the most appropriate bidding strategy as represented by the Analytic Hierarchy Process (AHP), as per the tender requirements of the client. The model is then used to predict the most appropriate strategies for a set of different simulated problem statements, the results of which are validated by the firms. Keywords: Bid evaluation strategies, Analytical Hierarchy Process (AHP), Multi criteria decision making (MCDM).

Introduction The construction industry is one of the largest single industries in the world which contributes immensely to employment, gross fixed capital formation and gross domestic products (Fadamiro and Ogunsemi, 1996). While its direct contributions to development are significant, it also stimulates a sizeable amount of economic growth through backward and forward linkages (F Moavenzadeh, 1975). In industry, today most of the large scale construction projects involve outsourcing through the process of bidding and tendering. This is necessary to ensure competitive selection of the contractors, due to increasing demand for value for money in terms of time, cost and quality. According to Frame, 2002, ‘careful project selection is the first step to the success of the construction company and it should not be carried out in a careless manner’. The process of bid evaluation thus forms the core of a construction project. A bid evaluation strategy involves overall planning, controlling, coordinating and assessing the cost needed to execute a project from inception to completion aimed at meeting a client’s requirement (Hughes, 1978). The bid evaluation is thus a multivariate decision making process involving several underlying criteria, which in a multi-agent contracting environment presents a challenging search problem in terms of identifying an appropriate bid evaluation strategy for the underlying project. . The subject of the bidding strategy has interested various researchers in America and Europe since the mid-1950s when the first mathematical model was introduced by Friedman, 1956. Since then the literature has been flooded with several bidding models. However most of these models remained in academic circles and did not find their way into the practical world (McCaffer and Harris, 2001). Fayek, 1998 presented a competitive bidding strategy model for use in setting a mark-up for construction projects. Fayek et al., 1998 and Fayek et al., 1999 studied bidding practices in Australian and Canadian construction industries. Jaselsis and Talukhaba, 1998 focus on bidding considerations in developing countries, considering the inherent risks. F. Ling, 1996 examined bidding practices in Australia, Canada, Saudi Arabia, Singapore, the UK and the USA. According to Hughes (1978), the criteria used for bid evaluation should reflect the client's objectives. These are that bids are fully responsive to the contract and bidders are sufficiently well qualified to undertake the contract. A research study conducted by Merna and Smith (1990) for bid evaluation for the public sector in the UK found that clients who require a tender submission of only an initial lump sum price without qualifications would then request further information for a more detail evaluation of the three lowest bids. Ellis and Herbsman (1991) proposed a new time/cost approach to determine the winning bidder in highway construction contracts. Herbsman and Ellis (1992), proposed a multi parameter bidding system for bid evaluation. He suggested considering secondary criteria, in addition to the major three parameters of cost, time and quality. Moselhi and Martinelli (1993), in consultation with the industry experience, established the selection criteria to be considered for bid evaluation to be: bid amount; annual life cycle cost; number of years in business/bid amount; volume business/bid amount; financial credit/bid amount; previous performance; project management organization; technical expertise; time of execution; and relation with subcontractors. A great volume of literature concerning the bid evaluation subsequently emerged after the contribution of the above research studies. Most of the research done in the last 50 years was on four main subjects, namely, bidding strategy, bid mark-up

determination, factors that affect or characterize the bid/no bid decision, and the bid decision-making process. In recent decades, several mathematical methods have been developed for selecting the most preferable alternatives. Among them, the MCDM (Multiple Criteria Decision Making) methods have attracted much attention from academics and practitioners. The development of MCDM methods has been motivated not only by a variety of real-life problems requiring the consideration of multiple criteria, but also by practitioners’ desire to propose enhanced decision making techniques using recent advancements in mathematical optimization, scientific computing, and computer technology (Eshlaghy and Homayonfar, 2011). The impact that the MCDM paradigm makes on business, engineering, and science is being reflected in the large number of articles with MCDM-type studies and analyses which are presented at professional meetings in various disciplines (Wiecek et al., 2008). These studies have focused on wide spectrum of disciplines such as environment management, water management, business and financial management, transportation and logistics, manufacturing and assembly, energy management, social service, military service and a few others. Through this paper, we aim to use the multi criteria decision making approach to assess the various bid evaluation strategies in Indian construction industry through Analytical Hierarchy Process or the AHP. This study thus aims to assess the various bid evaluation strategies being used in the Indian construction industry in terms of the underlying factors which affect the value of a bid. For this a questionnaire is being sent to the construction and contracting firms in India. The paper then aims at using the acquired data to build a model that predicts and suggests the most appropriate bid evaluation strategy based on the client’s needs, using the analytical hierarchy model. This would be followed by validation of the results of the model by the firms. The rest of the paper is organized as follows: Section 2 provides a review of some relevant literature on bid evaluation strategies in the construction industry. Section 3 gives a brief introduction about the analytical hierarchy process or the AHP. Section 4 discusses the methodology used to carry out the research. Section 5 presents the detailed analysis and the results of the model so developed. Section 6 concludes with the discussion and the scope for future study. Analytical Hierarchy Process The Analytic Hierarchy Process method (AHP) was developed by Thomas Saaty in the beginning of 1870s and it represents a tool in the decision making analysis based on multi-criteria decision making methodology. It was designed to assist the planners in resolving complex decision making problems where a large number of planners participate, and a number of criteria exist in a number of specific time periods. The application of Analytic Hierarchy Process can be found in such diverse fields as portfolio selection model, project procurement system, project management and engineering problems (Saaty 2001). According to Partovi (1994), the AHP is a decision aiding tool for dealing with complex , unstructured and multi attribute decision. Nydick and Hill (1992) described the AHP as a methodology to rank alternative courses of action based on the decision makers judgment concerning the important of the criteria and the extent to which they are met by each alternative. Golden (1989), described AHP as analytical by using members, hierarchy by

structuring the decision problem into levels and process-oriented because its step- bystep approach. The traditional Analytical Hierarchy Process developed by Saaty consists of the following steps:1. Formulating the decision table This step involves identifying the selection attributes for the considered decision making problem and short-listing the alternatives on the basis of the identified attributes satisfying the requirements. A quantitative or qualitative value or its range may be assigned to each identified attribute as a limiting value or threshold value for its acceptance for the considered application. An alternative with each of its attribute, meeting the requirements, may be short-listed. The short-listed alternatives may then be evaluated using the proposed methodology. This can be represented by the hierarchal tree shown in Fig 1.

Fig 1: Problem Structuring 2. Deciding weights of the attributes This step involves identifying the relative importance of different attributes with respect to the objective. This requires construction of a pair wise construction matrix using a scale of relative importance. An attribute compared with it is always assigned the value 1 so the main diagonal entries of the pair wise comparison matrix are all 1.The numbers 3, 5, 7, and 9 correspond to the verbal judgments ‘moderate importance’, ‘strong importance’, ‘very strong importance’, and ‘absolute importance’ (with 2, 4, 6, and 8 for compromise between the previous values). Table 1 presents the relative importance scale used in the AHP method. • Assuming M attributes, the pair wise comparison of attribute i with attribute j yields a square matrix A M × M where rij denotes the comparative importance of attribute i with respect to attribute j. In the matrix, rij = 1 when i = j and rji = 1/rij.

AM ×M

=

 r11 r  21  r31  --  rM 1

r12 r13 -- -- r1M  r22 r23 -- -- r2 M  r31 r31 -- -- r3 M   -- -- -- -- --  -- -- -- -- --   rM 2 rM 3 -- -- rMM 

• Once the pair wise comparison matrix is formed, the relative normalized weight (wj) of each attribute is found by calculating the geometric mean of ith row and then normalizing the geometric means of rows in the comparison matrix. This can be represented as follows: GM j=

{Π

w j = GM j /

M j =1

rij }

1/ M

(1)

M

∑ GM j =1

j

(2)

The geometric mean method of AHP is used in the present work to find out the relative normalized weights of the attributes because of its simplicity and easiness to find out the maximum eigen value and to reduce the inconsistency in judgments. • The next step is to evaluate the maximum eigen value λmax. • Once we have λmax , the consistency index is calculated as: C.I . = (λmax − M ) / ( M − 1)

(3)

The smaller the value of C.I., the smaller is the deviation from consistency. • The random index (RI) is obtained for the number of attributes used in decision making. Table 2 presents the RI values for different number of attributes. Table 1: Nine point scale for AHP Scale

Ranking

Explanation

1

Equally important

3

Moderately important

5

Strictly more important

7

Very strict, proven importance

9

Extreme importance

Both the criteria or alternatives contribute to the objective equally. Based on experience and estimation, moderate preference is given to one criteria or alternative over the other. Based on experience and estimation, strict preference is given to one criteria or alternative over the other One criteria or alternative is strictly preferred over the other; its dominance has been proven in practice The evidence based on which one criteria or alternative is preferred over the other has been confirmed to the highest confidence

2;4;6;8

Mid values

•

Calculate the consistency ratio CR = CI/RI. Usually, a CR of 0.1 or less is considered as acceptable and it reflects an informed judgment that could be attributed to the knowledge of the analyst about the problem under study.

N R.I.

1 0

Table 2: Random consistency index values R.I. (Saaty, 1980) 2 3 4 5 6 7 8 9 0 0.52 0.89 1.11 1.25 1.35 1.40 1.45

10 1.49

3. Calculating Composite Performance Scores The next step is to obtain the overall or composite performance scores for the alternatives by multiplying the relative normalized weight (wj) of each attribute (obtained in Step 2) with its corresponding normalized weight value for each alternative (obtained in Step 1) and making summation over all the attributes for each alternative. Pi =

M

∑ w (m j =1

j

ij

) normal

(4)

where (mij)normal represents the normalized value of mij. Pi is the overall or composite score of the alternative Ai. The alternative with the highest value of Pi is considered as the best alternative.

Results and Discussion For the study the target population consists of the 33 construction and contracting firms in the Indian construction industry Considering the exploratory nature of the research, data collection is being made with the aim of maximizing the number of respondents in order to attain a representative sample of the contractors available in this specific part of market. The research method selected for this study comprises of both qualitative and quantitative approaches. This is manifested in the development of the questionnaire that was firstly drafted based on the recommendation of the literature, then refined and fine-tuned through interviews and/or correspondence through emails. The questionnaire was developed until it reached the current form. So in the first stage, the questionnaire was designed based on the literature review to verify the actual selection methods used by different firms in India to select the most suitable contractor for a project and to identify the different criteria actually used in evaluating contractors’ pre-qualification and bid information. In the second stage, an initial set of questions were mailed to these firms. The questions asked in these mails were whether the points covered in the questionnaire were sufficient, clear and relevant to the Indian construction industry. Based on this, some adjustments were introduced to enhance clarity and to assure consistency in pursuit of appropriate results and conclusions. In the next stage, the modified questionnaires were distributed among the firms. The data collected through these questionnaires then forms the basis for the data analysis. The data obtained from the firms is then analyzed and used to build a model to predict the most appropriate bid evaluation strategy depending on the demands/ requirements of the client. The overall framework of the bid evaluation strategies in

the Indian construction firms as obtained from the initial stage of the survey can be represented as in Fig 2. The second level attributes (underlying bid evaluation criteria) are marked in the following way in the rest of the paper:A1 – bid price, A2 – time proposed, A3 – construction experience, A4 – technical ability, A5 – management capability, A6 – past performance, A7 – financial standing, Next, the relative importance of attributes are assigned depending on the tender requirements of the client.

Bid price Time proposed Construction experience Selection of bidding strategy

Technical ability Past performance Management capability

Open competitive bidding Limited competitive bidding

Single source procurement

Two stage bidding Electronic Reverse auction

Financial standing

Fig 2: Structuring the problem of selection of bid evaluation strategy

This gives us the matrix of comparison which constitutes the problem statement of our model. A sample matrix for a problem statement is provided in Table 3.

Table 3: Comparison of second level attributes / Underlying factors (A sample matrix) A1 A2 A3 A4 A5 A6 A7

A1 1 0.14 0.14 0.2 0.17 0.14 0.14

A2 7 1 1 0.33 0.5 1 1

A3 7 1 1 0.33 0.14 1 1

A4 5 3 3 1 2 3 3

A5 6 2 7 0.5 1 2 2

A6 7 1 1 0.33 0.50 1 1

A7 7 1 1 0.33 0.50 1 1

Weights 0.4923 0.0944 0.1292 0.0414 0.0536 0.0944 0.0944

Here the entry aIJ refers to the relative importance of Ith attribute compared to the Jth attribute, as required by the tender/client. The third level attributes (alternatives) have been marked in the following way:B1 – Open competitive bidding, B2 – Limited competitive bidding, B3 – Single source procurement, B4 – Two stage bidding, B5 – Electronic reverse auction. The data so obtained is used to arrive at the matrix of relative importance of underlying factors wrt each of the bid evaluation strategies. One such simulated matrix is shown in Table 4.

Table 4: Matrix of relative importance of attributes wrt the bid evaluation strategies

B1 B2 B3 B4 B5

A1 .25 .15 .10 .05 .10

A2 .15 .10 .10 .15 .20

A3 .20 .20 .20 .20 .10

A4 .15 .15 .20 .20 .20

A5 .10 .15 .15 .05 .10

A6 .05 .15 .20 .15 .15

A7 .10 .10 .05 .10 .15

The entry bIJ in this table refers to the relative importance of the Jth attribute in the Ith bid evaluation strategy as concluded from the survey. From this the third level alternative comparison matrices are deduced as follows -

For alternative comparison matrix corresponding to AJ : The entry cIK of the required matrix signifies the relative importance of the Jth attribute in the Ith bid evaluation strategy in comparison to that in the Kth evaluation strategy. Therefore, cIK =

bIJ bKJ

(5)

This forms the base for arriving at our model. The third level alternative comparison matrices for each attribute, as deduced from Table 4 using the above methodology, are presented in Appendix (Tables 6-11). At the end of the procedure, a total problem analysis of the selection of bid evaluation criteria is done, so that weights of all the alternative strategies are multiplied by the weight of the individual decision criteria, and the results obtained are summarized. The alternative with the highest value is, in fact, the most acceptable or optimal alternative. The final matrix so obtained, using the AHP method application for the sample problem corresponding to Table 3, is presented in Table 5. Hence for the tender requirements corresponding to Table 3, we obtain the highest value for the alternate- Open Competitive Bidding (B1). Therefore, we deduce that the most appropriate bidding strategy for the tender requirements corresponding to Table 3 is Open Competitive Bidding, as determined using the Analytical Hierarchy Process for the survey data. For the purpose of validation of our model, we plan to float a set of 10 different problem statements corresponding to 10 different tender requirements and ask for their opinion on the choice of most appropriate bid evaluation strategy in each case. We then determine (for the same 10 problem statements), the most appropriate bidding strategy using our model and the results so obtained were compared.

Table 5: Synthesized table on the optimal alternative selection (for sample matrix in Table 3) Criterion A1 A2 A3 A4 A5 A6 A7

Criteria Weight 0.4923 0.0944 0.1292 0.0414 0.0536 0.0944 0.0944

B1 0.3806 0.214 0.248 0.0685 0.1726 0.071 0.1999

Weight ×B1 0.1873 0.0202 0.0320 0.0028 0.0925 0.0067 0.0188 0.3603

B2 0.2282 0.1425 0.248 0.0685 0.2602 0.2144 0.2175

Weight ×B2 0.1123 0.0134 0.0320 0.0028 0.0139 0.0202 0.0205 0.2151

B3 0.1519 0.1425 0.248 0.2876 0.2602 0.2854 0.0999

Weight ×B3 0.0747 0.0134 0.0320 0.0119 0.0139 0.0269 0.0094 0.1822

B4 0.0872 0.2140 0.248 0.2876 0.0863 0.2144 0.1999

Weight ×B4 0.0429 0.0202 0.032 0.0119 0.0046 0.0202 0.0188 0.1506

B5 0.1519 0.2857 0.0077 0.2876 0.1726 0.2144 0.3003

One of the prime steps in the application of this method is the determination of the third level decision attributes (underlying factors) and the evaluation of their relative

Weight ×B5 0.0597 0.0269 0.0009 0.0119 0.0092 0.0202 0.0283 0.1571

weight. In this paper, the underlying factors in the Indian construction industry have been determined through consultation with the construction and contracting firms. The study in this paper is targeting a sample of 33 such firms. A larger sample population would definitely lead to a more accurate and better estimate of the most appropriate bid evaluation strategy in the Indian context. References Ellis, R.D., Herbsman, Z.J. (1991). Cost-time bidding concept: an innovative approach, Transportation Research Record 1282, Washington D.C., 89-94. Eshlaghy, Abbas Toloie, Homayonfar, Mahdi (2011). MCDM Methodologies and Applications: A literature Review from 1999 to 2009, Research Journal of International Studies (21), 86-137. Fadamiro, J.A. and Ogunsemi, D.R. (1996). Fundamentals of building design, construction and materials. Ile-Ife: Fancy Production Ltd. Fayek, A. (1998). Competitive bidding strategy model and software system for bid preparation. Journal of Construction Engineering and Management, 124(1): 1–10. Fayek, A., Young, D., Duffield, C. ( 1998). A survey of tendering practices in the Australian construction industry. Engineering Management Journal, 10(4): 29–34. Fayek, A., Ghoshal, I., Abourizk,(1999). S. A survey of the bidding practices of Canadian civil engineering construction contractors. Canadian Journal of Civil Engineering, 26: 13–25. Frame, J. (2002). The new project management, 2ed. San Francisco, Jossey-Bass. Friedman, L. (1956) A competitive bidding strategy. Operational Research, 4, 104– 12. Golden B, Wasil E, Harker P. (1989). The analytic hierarchy process: applications and studies. Berlin: Springer. Herbsman, Z., Ellis, R. (1992). Multi parameter bidding system-innovation in contract administration, J of Const Engrg and Mangt, 118(1), 142-50. Hughes, G. A. (1978). The anatomy of quantity surveying (2nd ed.). London: The construction press. Jaselsis, E., Talukhaba. A. (1998). A bidding consideration in developing countries. Journal of Construction Engineering and Management, 124(3): 185–93. Ling, F. (1996) Decision Making with Dependence and Feedback: The Analytica Network Process, RWS Publications, Pittsburgh, P.A. McCaffer, R., Harris, F. (2001). Modern construction management, 5th edn. Blackwell Science Ltd.

Merna, A., Smith, N.J. (1990). Bid evaluation for uk public sector construction contracts, Proc Inst Civ Engrs, Pt 1, Feb, 91-105. Moavenzadeh, Fred (1975). The construction industry in developing countries. Technology Adaptation Program,, Massachusetts Institute of Technology. Moselhi, O., Martinelli, A. (1993) Analysis of bids using multi attribute utility theory. Nydick, Robert L., Hill, Ronald Paul (1992). Using the Analytic Hierarchy Process to Structure the Supplier Selection Procedure, International Journal of Purchasing and Materials Management; Spring 1992; 28 (2), 31-36. Partovi, Fariborz Y. (1994). Determining what to benchmark: An analytical hierarchy process approach. International Journal of Operations and Production Management, Vol. 14( 6), pp.25 – 39. Saaty, T.L. (2001). Decision making in complex environments: the analytic network process for decision making with dependence and feedback. RWS Publications, USA. Wiecek, M, M., Ehrgott, M., Fadel, G., Figueira, J, R. (2008). Multiple criteria decision making for engineering. Omega 36, 337-339.

Appendix

Table 6: Matrix of alternative relative importance corresponding to A1 (bid price) B1 B2 B3 B4 B5

B1 1 0.6 0.4 0.2 0.4

B2 1.67 1 0.66 0.33 0.66

B3 2.5 1.5 1 1 1

B4 5 3 2 1 2

B5 2.5 1.5 1 0.5 1

Weights 0.3806 0.2282 0.1519 0.0872 0.1519

Table 7: Matrix of relative importance corresponding to A2 (time proposed) B1 B2 B3 B4 B5

B1 1 0.66 0.66 1 1.33

B2 1.5 1 1 1.5 2

B3 1.5 1 1 1.5 2

B4 1 0.66 0.66 1 1.33

B5 0.75 0.5 0.5 0.75 1

Weights 0.214 0.1425 0.1425 0.2140 0.2857

Table 8: Matrix of alternative relative importance compared to A3 (construction experience) B1 B2 B3 B4 B5

B1 1 1 1 1 0.5

B2 1 1 1 1 0.5

B3 1 1 1 1 0.5

B4 1 1 1 1 0.5

B5 2 2 2 2 1

Weights 0.248 0.248 0.248 0.248 0.0077

Table 9: Matrix of alternative relative importance compared to A4 (technical ability) B1 B2 B3 B4 B5

B1 1 1 1.33 1.33 1.33

B2 1 1 1.33 1.33 1.33

B3 0.75 0.75 1 1 1

B4 0.75 0.75 1 1 1

B5 0.75 0.75 1 1 1

Weights 0.0685 0.0685 0.2876 0.2876 0.2876

Table 10: Matrix of alternative relative importance compared to A5 attribute (management capability) B1 B2 B3 B4 B5

B1 1 1.5 1.5 0.5 1

B2 0.66 1 1 0.33 0.66

B3 0.66 1 1 0.33 0.66

B4 2 3 3 1 2

B5 1 1.5 1.5 0.5 1

Weights 0.1726 0.2602 0.2602 0.0863 0.1726

Table 11: Matrix of alternative relative importance compared to A6 attribute (past performance) B1 B2 B3 B4 B5

B1 1 3 4 3 3

B2 0.33 1 1.33 1 1

B3 0.25 0.75 1 0.75 0.75

B4 0.33 1 1.33 1 1

B5 0.33 1 1.33 1 1

Weights 0.071 0.2144 0.2854 0.2144 0.2144

Table 12: Matrix of alternative relative importance compared to A7 attribute (financial standing) B1 B2 B3 B4 B5

B1 1 1 0.5 1 1.5

B2 1 1 0.5 1 1.5

B3 2 2 1 2 3

B4 1 1 0.5 1 1.5

B5 0.66 0.66 0.33 0.66 1

Weights 0.1999 0.1999 0.0999 0.1999 0.3003

ID186 IMPROVING PRODUCTIVITY OF CONSTRUCTION OPERATION USING SIMULATION MODEL

S. M. Hossain1, T. Ahmed2 and M. A. Hossain3

1

Lecturer, Department of Civil Engineering, Presidency University, Bangladesh

E-mail : [email protected] 2

Lecturer, Department of Civil & Environmental Engineering, Islamic University of Technology, Bangladesh E-mail : [email protected]

3

Assistant Professer, Department of Civil & Environmental Engineering, Islamic University of Technology, Bangladesh E-mail : [email protected]

Different activities involved in a construction operation are interdependent and some activities require common resources which make it difficult to schedule the construction operation effectively. Eventually it leads to lengthen project duration and make inefficient use of resources. Therefore improving construction productivity to reduce duration and cost is very important for a project. Total Ineffective Time of a construction project can be minimized through proper planning & scheduling prior to construction. Manual calculation and planning is complicated and time consuming considering the interaction of different work activities. This situation has motivated the study to figure out the usefulness of simulation models for minimizing project duration. Simulation models can be an efficient tool to generate effective plans and schedules as they consider complex interactions among various units on the jobsite to evaluate the performance of the construction operation. A simulation model has been developed using STROBOSCOPE simulation language in this study considering different combinations of resources and interaction among different activities of a real life construction operation. The results show that the simulation model is very effective to select the optimum combination of resources that produces minimum project duration. The simulation model is also effective to find out the amount of concurrent execution of activities to further shorten the project duration. Keywords: construction operation, construction productivity, planning & scheduling, project duration & cost, total ineffective time, simulation model, combinations of resources, interaction among activities.

Introduction Productivity is generally a measure of the efficiency of production, a ratio of input to output. For a construction operation inputs may be different human and non-human resources such as labor, materials, equipment, tools, capital, designs etc. (Wang et al.,

1999). These resources should be utilized in a proper way to ensure an optimum productivity. Time, cost and quality are the three factors that determine the success or failure of a construction project (J. Hoffman et al., 2007). So to minimize the delays and cost overruns, in other words to improve construction productivity project team should maintain scheduling (Osman et al., 2011). Simulation models can be an efficient tool to generate these types of plans and schedules. Simulation models can analyze different combinations of resources and generate the best combination with minimum cost and idle time associated with each resource. Manual calculation of productivity has certain difficulties – it is both time consuming and complicated, there lies possibility of making mistakes. On the other hand, simulation software takes less time; calculation is easier and more accurate. Repeated delays and cost overruns are the characteristics of the construction industry (Kaming et al., 1997), where in most construction operations, the contractor and subcontractors are obliged to finish the project by a certain date specified in the contract. So the use of computer simulation technique is highly recommendable in construction industries because of the complex interactions among various units on jobsites. But the construction industries are not used to with these different tools of simulation, for two basic reasons (Hassan et al., 2006) – (1) lack of credibility of this technique among the contractors, (2) to achieve the level of expertise required from the user is difficult. However, computer based simulation is one of the most useful techniques which can be used to evaluate performance of construction operations. Other techniques like real system experimentation or application of mathematical models including queuing theory, Line of Balance (LOB), are either too expensive, time consuming or contain many assumptions that limit their use in the construction site. Construction simulation is usually favored with the availability of modern computers that may simulate the operations realistically. It is also inexpensive, flexible, and requires less computational time (Hassan et al., 2006).

Model Development Construction of a 25 storied commercial building with 3 basements named “Bangladesh Shipping Corporation Building” has been selected as case project for this study. The construction site was located at Doinik Bangla More, Motijheel, Dhaka. The owner of the building is Bangladesh Shipping Corporation. The architectural and the structural designs were prepared by Department of Architecture and Public Works Department respectively. The estimated duration of the project is 36 months. The construction operation that has been selected for the model development is mainly substructure work and it consists of three different phases such as piling work, earth excavation, bracing work. For piling operation they used a boring rig and a concreting funnel. Piling includes placement of casing, concreting and boring – 5 to 6 hours are required for the entire process. There were total 93 shore piles and 4 bracing piles. Placement of bracing includes four phases – placement of angles, placement of I-

sections, placement of main bars and welding. The entire operation requires about 4 weeks. Excavation was conducted in two phases. The volume of excavation of each phase was 95,625 ft3 (85ft×75ft×15ft). Truck and excavator were used for the operation. Cost of local labor was 250 taka/day. Daily work shift was of 7 hours. The operation has been modeled by STROBOSCOPE and visualized by Microsoft Visio 2007. The model has been developed using different modeling elements such as NORMAL, COMBI, LINK, QUEUE etc. Resources have been represented by QUEUE. Each activity of the operation has been represented either by COMBI or by NORMAL in the simulation model. When an action is followed by a QUEUE then only COMBI is used, otherwise NORMAL is used. LINK has been used to connect network nodes. It indicates the direction in which a type of resource flows. The symbols of different modeling elements used in this study for the model development are shown in Table 1 and the detail of the model is shown in Figure 1

Table 1: Modeling Elements Element Name Symbol QUEUE COMBI NORMAL LINK

The construction operation can be divided into three phases. These are: 1. Pile construction, 2. Earth excavation, 3. Placement of bracing Again, pile construction involves the following sub-activities: Boring, Placement of casing, Concreting. And the bracing work involves the following sub-activities: Angle placing, I-section placing, Bar placing, Welding. Two types of resources have been used for pilling. They are: a. Hydraulic rig (required for boring), b. Crane (used for casing and concreting) The excavation cycle involves the following activities: a. Load, b. Haul, c. Dump, d. Return Two types of resources have been used for excavation. They are: a. Excavator (for the purpose of excavation and filling), b. Truck (used for hauling and dumping)

Total

PC1

PT1

Bore

Boring

B to

PL1

C

PL2

PR1

PC2 Crane

PL3

PR2

Rig

Casing

wait C to

Wait

PB1

PL4

Co

PB2

Concreting Loader

EL1

Wait

PE1

P to E

EL2 ES2 EH2

Load ES1

Soil to

E to B

Angle

Dump EH4

EB1

Move

Total

ES3 EH3

Haul

BT1

Hauler

EH1

BL1

Return

Wait

EB2

Angle Placing

EH5

I-section Placing

A to I

BL3 I to B

Welding

BL6

B to

BL5

W

Bar Placing

BL4

Figure 1: Model of the construction operation.

Durations for each activity of the sub-structure work are shown in Table 2.

Table 2: Different activities and their duration Activities

Duration (Minutes)

Boring Casing Concreting Load Haul Dump Return Angle placing I-section placing Bar placing Welding

210 30 60 20 30 5 20 15 180 180 60

The project starts with pilling and the first activity of pilling is boring so without starting boring following sub-activities cannot start. Hydraulic rig is required only for boring. On the other hand crane is required for both casing and concreting. Therefore, if a single crane is used for the whole project the casing and concreting cannot be started simultaneously. Now the time required for boring is less comparing total time required for casing and concreting. So when one crane is used hydraulic rig may sit idle. On the other hand if two cranes are used, it will speed up the construction operation as two piles can be completed concurrently. Eventually idle time for rig will reduce. If more cranes are used, then some crane may sit idle as excavation may not be completed by using a single hydraulic rig. In that case, more hydraulic rigs may be required in order to cope with the number of cranes. So an optimal combination of hydraulic rig and crane is to be determined in order to minimize the idle time of the resources. Similar scenario can be seen in case of the resources (truck and excavator) of the excavation phase. Optimal combination of resources should be selected based on duration of the operation. A detail analysis for different combination of resources is necessary in order to select an optimal combination of resources that will result minimum duration for the project.

Data Analysis and Results The simulation model is incorporated with three major sub-structure activities viz. placement of shore piles & bracing piles, excavation and placement of bracing. These activities are linked to one another. Again different kinds of resources are required to carry out these activities. For pilling operation cranes and rigs are required. For this study, it has been assumed that the maximum number of available rigs or cranes for the piling work is 4. Again, for excavation haulers and loaders are required, and for simulation purpose the maximum number of available loaders has been assumed to be 3. Without finishing piling other activities cannot start. So for starting excavation piling is needed to be finished. Now during excavation soil will be removed so the piles will be uncovered. Therefore the concrete of the piles should attain sufficient strength before that. It has been considered that within 8 days most of the piles will attain sufficient strength and excavation can be started. It will be better to start excavation from the place where initial piles were constructed. Now as without pilling no other activities can be started so in this study it has been considered that the pilling will be carried out on a go. For that reason simulation has been run for pilling cycle separately. In the Table 3 the duration of piling is shown for different combinations of resources.

Table 3: Duration of piling operation for different combination of resources Cranes Rigs Duration (Days) 1 1 1 2 2 3

1 2 3 3 4 4

48.714 24.714 21.286 16.714 12.714 12.714

Table 3 shows that the combination of 2 cranes and 4 rigs provides the least duration (12.714 days). To simplify the calculation the number of cranes and rigs has been kept constant to 2 and 4 respectively. In case of excavation loader and hauler are required. It has been assumed that maximum no of loader is 3. The simulation has been run for excavation cycle using different combinations of loaders and haulers. In the Table 4 the duration of excavation is shown for different combinations of resources.

Table 4: Duration of excavation for different combination of resources Loaders Haulers Duration (Days) 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3

1 2 3 4 5 5 6 7 8 9 9 10 11 12 13

120.216 60.197 40.178 32.046 32.046 24.150 20.160 17.319 16.112 16.112 13.495 12.169 11.078 10.785 10.785

Resource is limited for any construction project. For different combinations of resources and different activity interactions have also influence on the project duration. Sometimes the project duration is less when the activities are carried out sequentially. Sometimes it may be less if two or more activities or sub-activities are carried out

concurrently rather than sequentially. It depends on the type of the activities. In this study four different cases have been considered for simulation. In case 1, both activities and sub-activities have been considered to be finished in sequential manner. No two activities will be started concurrently. The project will be started with pilling. Excavation will be started after waiting for 8 days to allow curing of piles. Then bracing will be completed sequentially. In case 2, firstly full piling will be completed. After that excavation will start. When half of the excavation is finished placement of bracing will be started with angle placing. When 47 angles (half of the total angles) are placed, the model will check whether rest of the excavation is finished or not. If finished, then the bracing will continue, if not it will wait until the excavation is finished. In case 3, full piling and full excavation have been considered to be carried out sequentially. After that the bracing will be started. When angle placing will be completed, the I-section placing will be started. When 6 I-sections is placed, bar placing will start. When 6 bars will be placed, the model will check whether the Isection placing has been completed or not. If completed, the bar placing will continue, if not it will wait until the I-section placing is completed. After finishing bar placing all the joints will be welded. In case 4, firstly full piling will be carried out. After that excavation will start. When half of the excavation is finished, bracing will be started with angle placing. When 47 angles are placed, the model will check whether rest of the excavation is finished or not. If finished, then the angle placing will continue, if not it will wait until the excavation is finished. Then the I-section placing will be started. When 6 I-sections will be placed, bar placing will start. When 6 bars will be placed again the model will check whether the I-section placing has been completed or not. If completed, the bracing activity will continue, if not it will wait until the I-section placing is completed. After bar placing is finished all the joints will be welded. Case 1: Full piling → Full excavation → Bracing 1 Case 2: Full piling → Half excavation → 47 angles placing1 → Second half of excavation → Rest of the bracing1 Case 3: Full piling → Full excavation → Bracing2 Case 4: Full piling → Half excavation → 47 angles placing 2 → Second half of excavation → Rest of the bracing2 1 When bracing will start it will continue through angle placing then I-section placing and then bar placing. Total number of I-sections are 12 and bars are also 12, in this case 12 I-section will be placed and then the bar placing will be started. 2 When 6 I-sections are placed, bar placing will be started. When 6 bars will be placed, the model will check whether placement of all the 12 I-sections has been completed or not. If completed, the placement of the rest 6 bars will be started, if not it will wait until the I-section placing is completed.

Durations obtained for different cases using different combination of resources have been shown in Tables 5, 6, 7, 8.

Table 5: Duration for Case 1 (Full piling → Full excavation → Bracing1) using different combination of resources Loaders Haulers Cranes Rigs Duration (Days) 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3

1 2 3 4 5 5 6 7 8 9 9 10 11 12 13

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

4 4 4 4 4 4 4 4 4 4 4 4 4 4 4

161.407 101.388 81.369 73.237 73.237 65.341 61.351 58.509 57.303 57.303 54.686 53.360 52.269 51.976 51.976

Table 6: Duration for Case 2 (Full piling → Half excavation → 47 angles placing1 → Second half of excavation → Rest of the bracing1) using different combination of resources Loaders Haulers Cranes Rigs Duration (Days) 1 1 1 1 1 2 2 2 2 2 3 3

1 2 3 4 5 5 6 7 8 9 9 10

2 2 2 2 2 2 2 2 2 2 2 2

4 4 4 4 4 4 4 4 4 4 4 4

159.693 99.673 79.655 71.523 71.523 63.626 59.636 56.795 55.588 55.588 52.972 51.646

3 3 3

11 12 13

2 2 2

4 4 4

50.555 50.261 50.261

Table 7: Duration for Case 3 (Full piling → Full excavation → Bracing2) using different combination of resources Loaders Haulers Cranes Rigs Duration (Days) 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3

1 2 3 4 5 5 6 7 8 9 9 10 11 12 13

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

4 4 4 4 4 4 4 4 4 4 4 4 4 4 4

157.192 97.173 77.155 69.023 69.023 61.126 57.136 54.295 53.088 53.088 50.472 49.146 48.055 47.761 47.761

Table 8: Duration for Case 4 (Full piling → Half excavation → 47 angles placing2 → Second half of excavation → Rest of the bracing2) using different combination of resources Loaders Haulers Cranes Rigs Duration (Days) 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3

1 2 3 4 5 5 6 7 8 9 9 10 11 12 13

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

4 4 4 4 4 4 4 4 4 4 4 4 4 4 4

154.549 94.530 74.512 66.380 66.380 58.483 54.494 51.653 50.446 50.446 47.829 46.503 45.412 45.118 45.118

From the tables 5, 6, 7 and 8 it can be seen that, case 4 generates the least duration (45.118 days). So the best alternative according to minimum duration will be case 4 with a resource combination of 3 loaders, 12 haulers, 2 cranes and 4 rigs. Another important thing revealed from these tables is that overlapping of activities and sub-activities reduces duration. Among the four cases maximum overlapping between activities and sub-activities has been done in case 4 which provided the least project duration. Such overlapping in activities can be done in several ways, this study showed only a few. Now the selection of an alternative may vary from project to project. Some projects consider that the duration should be least; some projects are flexible, that they consider delay for one or two days is not a problem if number of resources can be reduced to some extent. For case 4 if 10 haulers are used instead of 12 haulers the project will be delayed only by 1 day. But it can reduce the number of hauler by 2, which is significant for the projects that consider delay for some days is not a problem. In today’s competitive market, people are trying to complete their project with minimum duration and cost. Taking this fact into consideration, the study has focused on how construction productivity can be improved. The main purpose of this study was to develop a simulation model for producing a proper schedule that will minimize the duration of a particular construction operation. The model has considered different combination of resources and complex interaction between different activities of the operation. From the analysis best alternative that results minimum duration with minimum resources has been sorted out. However, such simulation models can provide a proper planning prior to the construction project begins. So conflict due to resource sharing among different activities can be reduced, smooth working condition can be ensured. In general construction productivity can be improved effectively.

References Hassan, M. (2006). Use of Real Life Construction Projects as an Effective Tool for Teaching Construction Simulation. ASC Proceedings of the 42nd Annual Conference, Colorado State University Fort Collins, Colorado J. Hoffman, G., Alfred E., T., Timothy S., W., and Jeffery, D. (2007). Estimating Performance Time for Construction Projects, Journal of Management in Engineering, ASCE, 23 (4): 193–199 Kaming, P., Olomolaiye, P., Holt, G., and Harris, F. (1997). Factors influencing construction time and cost overruns on high-rise projects in Indonesia, Construction Management and Economics, 15 (1), 83-94 Osman, A. S. 2011. Productivity of Concreting Work in Jabatan Kerja Raya Building Projects, M.Sc. thesis. Universiti Teknologi Malaysia, Malaysia Wang, S. Q. (1999). Improving Construction Productivity by Management. 2nd

International Conference on Construction Industry Development and 1st Conference of CIB TG29 on Construction in Developing Countries: Construction Industry Development in the New Millennium, School of Building and Real Estate, National University of Singapore, Singapore, 419-429

ID190 CONCEPTUAL DESIGN OF INTELLIGENT HEARING PROTECTION DEVICE (IHPD) TO ENHANCE WORKER'S SELF-EFFICACY AGAINST HAZARDOUS NOISE EXPOSURE IN WORKPLACE L. M. Han1, T. W. Kang2, Z. Haron3, 1

Postgraduate, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Malaysia E-mail: [email protected] 2 Postgraduate, Faculty of Electrical Engineering, UniversitiTeknologi Malaysia, Malaysia 3 Senior lecturer, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Malaysia The adoption of hearing protection device (HPD) in noisy area is significantly protecting workers' hearing system, but most of the workers areirregularly use HPD due to bad attitude and low motivation among workers. This study proposes method to enhance the self-efficacy of workers in use of hearing protector against the hazardous noise exposure problems in workplace.The method uses intelligent hearing protection devices and dosiwatch connected wirelessly to centralized system (Integrated Noise Exposure Software (INES)) for the collection of instantaneous noise data including noise exposure level for 8 hours, Lpeak and the noise exposure level and self assessment report. IHPD installed with microphone, warning lights if noise level exceed the permissible limit, and touch sensor while dosiwatch displays instantaneous noise levels, and vibrates when workers are exposed topermissible noise exposure limit. As worker can feel and observe these characteristic, IHPD motivate the workers to believe the impact of noise towards their health and consequently enhance workers self-efficacy in using of HPD. Management officer could use this method as monitoring tool for their workers with touch sensor that enable to locate the workers with irregular use of HPD. Key words: Hearing protection device, occupational noise exposure, self-efficacy, dosiwatch;

Introduction The adoption of hearing protection device (HPD) in noisy area is significantly protecting workers' hearing system. Regularly use of HPD can prevent the worker suffers from noise-induced hearing loss (NIHL)and has better hearing compared with the worker periodically removes HPD during working day (Hong, 2005).It could be categorised into four basic types, such as earplugs, semi-inserts, earmuffs and helmets. The design of HPD mustcomply with general requirements that it should have sufficient noise attenuation, comfortable for workers wear and absence of negative effects on human skin (Gerges and Casali, 2007).In the regulatory development of Hearing Conservation Amendment (OSHA 1983), at the part of 29 CFR 1910.95, it has stated that (1) Employers shall make hearing protectors available to all employees exposed to an 8-hour time-weighted average of 85 decibels or greater at no cost to the employees. Hearing protectors shall be replaced as necessary.(2)Employers shall ensure that hearing protectors are worn.(3) Employees shall be given the opportunity to select their hearing protectors from a variety of suitable hearing protectors provided by the employer. (4) The employer shall provide training in the use and care of all hearing protectors provided to employees.(5) The employer shall ensure proper initial fitting and supervise the correct use of all hearing protectors.For hearing protection emerging trends, OSHA recommended to utilize Individual Fit Testing to predict how well he or she is wearing the protection device and the sufficiency of protection to be utilized, its method includes subjective real-ear

attenuation at threshold (REAT) measurements, objectives field microphone-in-realear (F-MIRE) measures and loudness balance technologies (NHCA OSHA, 2008). Self-reportis a useful method to assess the adoption of HPD in workplace and can also analyse behaviour of worker. Erroneously, self-reported data was not as same as the observed workers' behaviour and sometimes it was overestimated, especially the usage data from younger workers are unreliable due to they are lacking of experience in HPD use. Eventually, improper wearing HPD might lead to reduce the efficiency of noise attenuation, so workers are not protected if they did not wear HPD continuously (Arezes and Miguel, 2012). Noise exposure variability is a factor influencing the accuracy of self-report hearing protection use. As Griffin (2009) had found that the report of hearing protection use in steady noise environment is more reliable than the variable noise environment. Attitude and belief of workers are the reasons caused workers ignore the function of HPD. Enforcement of training and education should be provided to enhance selfefficacy of workers towards changing their attitude and believe the negative impact towards their health as prolonged exposing to noise (Arezes and Miguel, 2002;Arezes and Miguel, 2005; Arezes and Miguel, 2008).However, training is only important to gain worker's knowledge regarding to the noise exposure risk and the benefit of regular HPD use, but not necessarily increase the utilization rate of HPD(Arezes and Miguel, 2006;Williams et al., 2007). Promotion of HPD use should be focused on workers' self-efficacy of HPD use, where the workers with high self-efficacy are more willing to wear HPD regularly and capable to perceive the benefit and importance of HPD to protect them in workplace, even though HPD will make them feeling uncomfortable and difficult to communicate with others (Arezes and Miguel, 2006).Concomitantly, co-workers and management group are playing an important role by supporting and given the feeling of empowerment to individual with positive attitude in adopting the preventive action(Williams et al., 2004). The development of caring attitude should be encouraged by the management group. Positive knowledge and attitude of co-workers would give the support and caring to other workers by ensuring they are working in a safe environment (Burt et al., 2008). This study proposes method to enhance the self-efficacy of workers in use of hearing protector against the hazardous noise exposure problems in workplace. The method describe the conceptual design of intelligent hearing protection devices (IHPD), dosi-watch and the centralized system (Integrated Noise Exposure Software (INES)) for the collection of instantaneous noise data including noise exposure level for 8 hours, Lpeak and the noise exposure level and self assessment report. Conceptual Design of IHPD IHPD is accompanied with dosiwatch and integrated noise exposure software (INES). IHPD considers several factors including the factors of risk perception, selfawareness and group supports in changing the worker's attitude and belief that influence the self-efficacy of HPD use, as described in the previous literature. The design of INES include the concepts of generating self-report of HPD use and noise exposure report, thus managing officer can refer the reports to provide noise abatement strategies and their commitment to minimize the noise problems in workplace. In addition, IHPD has combination of dosimeter's function to increase its function and performance, where it can be used as personal noise measuring device.

Workers

Awareness Leader commitment Noise abatement strategy

Risk perception Self-awareness Group supports

Attitude & Belief

Managing Officer

Self-report HPD use Noise exposure report

Self-efficacy

HPD use

Figure 1: Factors Using in the Conceptual Design of Intelligent Hearing Protection Device Basically, IHPD is the new installations of microphone, warning lights and touch sensor device withthe ordinary HPD. The measuring device inIHPD is complied with the IEC 61672-1:2002and IEC 61252 standards. A customized data acquisition device (DAQ) is used to control the behaviors of IHPD and wireless connection system allows noise data transmission between IHPD, dosiwatch and INES. As referring to Factories and Machinery (Noise Exposure) Regulations 1989, it has stated that the quiet means 'absence of exposure to sound levels exceeding 80 dBA'. Thus, the warning lights on IHPD will turn into red lights when the noise level exceeds 80 dBA and green lights when the noise level below 80 dBA. The warning light on IHPD is used to aware the nearby workers and officers that it indicates the circumstances of noise level in particular working area. Touch sensor device detects whether the workers are regularly adopting IHPD in workplace and signal will be sent wirelessly to officer's computer as they taken-off the IHPD. Figure 1a and Figure 1b illustrated the design of new invention of IHPD.

Figure 1a: Side View of Intelligent Hearing Protection Device

Figure 1b: Front View of Intelligent Hearing Protection Device The new invention of dosiwatch consists of display screen, warning lights, vibratory device, buttons and others. Similarly, the warning lights in dosiwatch have the same function as mentioned earlier in IHPD and it is used to aware the worker regarding to their current noise exposing situation.By the same token, the display screen in dosiwatch allows worker knows more about noise exposure circumstances in workplace. It will display the current time, current noise level, equivalent continuous noise level, time-weighted average noise level for 8 hours, dose percentage, peak level, and minimum noise level. The vibratory device reacts immediately when exposing to high noise area. It will be slight vibrating for every period of time, so it will not induce the workers feel uncomfortable and can effectively remind worker about high noise level in particular area. Figure 2 shows the drawing of new design of dosiwatch.

Figure 2: Dosiwatch The integrated noise exposure software (INES) is programmed by using MATLAB Graphical User Interface (GUI), where it is a powerful programming tool and is useful in calculating complex data.In INES, it is receiving the noise data wirelessly and hearing protector wearing status from IHPD. The software assists managing officers in noise calculations, such as equivalent continuous noise level, time-weighted average noise level for 8 hours, dose, peak level, minimum noise level, impulse noise at 140dB, noise level exceeded for 10% of the time, as well as noise level exceeded for 90% of the time.It will send data to the dosiwatch when the noise calculation is done. The formulations for the calculation of noise data are conformed with the OSHA Occupational Noise Exposure (1998) and BS EN ISO 9612:2009. Besides, as the recommendation inOSHA, the method of noise reduction rating (NRR) for A-weighting sound pressure level will be used in this software to estimate the effectiveness of noise-reducing level when the IHPD is worn.Graphs, histograms, and tables will be shown in this software as the data analysis and results for the measurement. For the sake of improving functionalities in personal noise exposure measurement, the INES provides the selection of measurement strategies as recommended in BS EN ISO 9612:2009, for instance task-based measurement, jobbased measurement and full-day measurement. Lastly, INES has database for the data storage of company's profile, worker's profile, worker noise exposure data, results, as well as reports and so forth. Figure 3 shows the flow chart of overall process in IHPD. IHPD Measure noise

Send signal to devices if exceeded limit.

Data collection Via wireless

Alarm lights turn on.

USB device

Noise calculation by INES

Check the permissible exposure limit from FMA and OSHA

Display the current and TWA noise exposure. Dosiwatch If exceeded limit, alarm light turns on and vibrate. Noise action plan

Data storage: Worker details and daily exposure

Figure 3: Flow Chart of IHPD Application of IHPD Figure 3 shows the application of IHPD in efficient and effective way. A representative among the workers will be chosen in particular area to wear IHPD with measuring device. The representative should be the person who exposed critically to noise sources and noise data will be sent to INES for further calculations. After calculation of the noise data, the results will be sent back to the representative and adjacent workers, who are wearing IHPD without measuring device, and it indicates

current noise circumstances in that particular area. Worker A, B and C are sharing the same noise levels with the representative and safety officer is monitoring noise level at workplace. In the consideration of safety purpose, even though the adjacent workers are sharing the same noise level with the representative, they should know the critical noise level in their working area and so preliminary prevention could be taken. The intention of this application method is to minimize the cost of IHPD and enhance the method of noise monitoring in workplace. Also, the promotion of caring attitude (Burt et al.,2008) could be found in this method, co-workers are inter-reliant to each other and definitely they might remind others to use HPD regularly in workplace. Moreover, INES produces the personal noise exposure report, where the result is represented critical noise exposure levels of these homogenous workers. The employer can assess noise exposure level and identify the impact of noise source affects workers via this report.

Figure 1: The Application of IHPD The preliminary design of IHPD aims to achieve the objective to enhance the selfefficacy of workers in use of hearing protector. In prototyping process, the design of model identifies the basic requirements of personal noise exposure device. Likewise, the graphical user interfaces of INES have been drawn out to meet the design requirements. After the development of initial prototyping model, the structure of data collection method has been designed to justify the preliminary design of invention meets the objective. Several case studies will be conducted on different trades of workers in factory, laboratory and construction site. The way of collecting data is utilized quantitative and qualitative research methodologies. The quantitative data is collected from the distribution of questionnaire to the workers after they used IHPD in case study. By the way, qualitative data is collected from the interview with workers and managing officers to obtain feedbacks and recommendation of device. Besides, the result of dosimeter will be used to compare with INES for data validation. Discussion on the result of IHPD is important for further improvement and

modification in order to enhance the prototyping model. Figure 4 shows the flow chart of research methodology.

IHPD

Prototype Model:

INES Programming:

  

 

Design of IHPD Design of Dosiwatch Design of USB

 Data analysis Data validation Discussions Recommendations

MATLAB GUI Noise Exposure Calculation (BS EN ISO 9612:2009) Permissible Exposure

Case Study:

  

Survey on different trades of workers in factory, laboratory and construction. Quantitative research methodology: Questionnaires Qualitative research methodology: Interview

Figure 4: Flow Chart of Methodology Discussions New invention of IHPD has greater functionality compared with conventional HPD, where it can measure the noise data and produce instantaneous noise exposure calculations and charts in INES. Noise data transmission with wireless system has improved current noise measuring technology. It is faster in calculating personal noise exposure levels and preventive action could be taken immediately by managing officers.On the part of installation, the design of the devices can be simply attached on the normal HPD,so no modification of HPD is needed, and can be easily detachedthe devices if found HPD is damaged. As discussed earlier on the issue of unreliable accuracy of self-report data in HPD use(Arezes and Miguel, 2012), the function of touch sensor device in IHPD is vital to check the consistency of workers wear hearing protector during working period. It will report to the officers if detect any worker does not wearing IHPD, hence it helps managing officers to analyze the behavior of workers and also they can decide the suitable action to be taken based on the result of analysis. Besides,awareness of workers will be increasing when they notice their colleagues' warning lights are turning into red, therefore they realize that how the noise exposure circumstances in their workplace. Undoubtedly, self-efficacy of worker in IHPD use will be increased and change their attitude to be more willing to wear hearing protector consistently. In the design of dosiwatch, it is comfortable to wear and would not disturb worker's working performance. A display screen is showing the current noise level, it allows worker to understand how the noise level varies in workplace. They can also report to officer when they found the noise source which is generating high noise level and assist officer to control noise problems. The built-in vibrator reminds worker that they are exposed to noisy area, thus they are more aware and more frequent to wear IHPD as their risk perception increased. Risk perception is

significant to change workers' belief and attitude, as their perceived level of risk is high, it will greatly influence the utilization rate of HPD (Arezes et al., 2006). Even though warning lights from dosiwatch will enhance worker's self-efficacy in hearing protector use.Still, the warning lights from colleagues' IHPD also play an important role to give the feeling of empowerment to the worker with positive attitude.Hence, they will adopt the preventive action and also give advice to other worker who is still reluctant to wear hearing protector regularly(Williams et al., 2004; Burt et al.,2008). Generally, the aims of the integrated noise exposure software are simple, easy to use and user friendly. The most significant features in this software are receiving noise data wirelessly from IHPD, directly calculating instantaneous noise exposure levels, as well as updating the charts once receiving any new data. Therefore, managing officers can be easily observed the variation of noise level in workplace. It assists officer in managing the workers' noise exposure, minimizing noise problem and maintaining a sustainable workplace. Interestingly, INES enables the officers to know the worker has not worn hearing protector regularly during working period. The software is also programmed to allow computer speak-out to remind officer. Indeed, the variation of noise environments is the factor influencing the accuracy of reporting data of HPD use (Griffin et al., 2009).Nevertheless, INES could analyze the usage of hearing protector and the results are more reliable compared with the common method of self-report HPD use, even it has been using in different environments. The application of INES is flexible for managing officers either it can be used as noise measurement in a group of workers or personal exposure measurement device. It supports the measurement strategies for complex work tasks, number of workers in particular job and full-day measurement as discussed inBS EN ISO 9612:2009. Although the standard provided lowest cost-consuming and uncertainty values to be adopted in complex and time-consuming process, but the industrial environments and procedure improvement for the strategies should be concerned (Arezes et al., 2012). Therefore, itis programmed into simpler way to understand the application and allows managing officer to decide suitable strategy to be applied in personal noise exposure measurement. As a final point, the INES provides database for personal noise exposure data storage and credible information can be used as documentation for noise monitoring report and noise impact assessment. It can also self-generate a printable report with the Microsoft Words format file. So, the officers can easily print, edit or save it as documents for management use. Conclusion In summary, the functions of current HPD should be improved to solve the current occupational noise problem. Managing officers and workers should concern and pay more attention on occupational noise problem in their workplace. The new invention of IHPD is vital to be widely utilized in workplace since it has better functions to assist managing officers to monitor the occupational noise exposure problems. Workers are more willing to wear IHPD because they believe the occupational noise damage their hearing system in every moment they expose to high noise area. References Ali. S. A. (2011). Industrial Noise Levels and Annoyance in Egypt. Applied Acoustics, 72, 221–225. Andrew. P. K., Stacey. A. A. (2007). Occupationally-Acquired Noise-Induced Hearing Loss a Senseless Workplace Hazard. International Journal of Occupational Medicine and Environmental Health, 20(2), 127 – 136.

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ID191 OCCUPATIONAL NOISE EXPOSURE AMONG ROADCONSTRUCTION WORKERS K. M. Said1, Z. Haron2,A. Saim A3,M. Z. Abidin4, K. Yahya5, S. Balubaid6 1,4

MasterStudent, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Malaysia E-mail: [email protected] 2,3,5 Senior lecturer, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Malaysia E-mail: [email protected] 6 Phd Student, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Malaysia E-mail: [email protected] Construction activities generate high noise level that may put the worker’s at risk of hearing loss. This study aims to evaluate noise exposure and prevalence of symptom of hearing loss among typical road construction workers.Personal noise dosimeter was used to obtain the noise exposure profile of heavy equipment operators. The results show 39% of heavy equipment operators exposed to noise levels above 85 decibel(dBA) with and 7% of operators exposed above the permissible exposure limit which is 90 dBA.The percentage of hearing protection used is only 3.5%. From all heavy equipment operators, 25% of operators suffer tinnitus, 5% of them recorded poorhearing ability which may associate with presbycusis and 25% of them show symptom to develop hearing loss. These exposed workers have to go for an audiometric testing program annually in order to identify deterioration in their hearing ability as early as possible. Keywords: noise exposure, tinnitus,road construction, construction worker, hearing loss symptom

1.0

Introduction

The construction sector is one of the industry sectors that generate noise and put workers at over-exposed risk. Most of these workers work from various different tasks in construction sector including road construction. In the early 1980s, almost 421 000 construction workers exposed to daily noise levels above 85 dBA (Whitaker et al., 2004) and increase to 500 000 construction workers in 1999 (Lusk et al., 1999). In 2002, exposed workers range from about half a million to 750,000 (Suter, 2002) since workers TWA always exceeded 85 dBA in most trades (Nietzal and Seixas., 2005) yet less than 1% of noise inspections were carried out in the construction sector from the average of 22,700 construction inspections in 1994. (Hattis., 1998) Various heavy-equipment used in construction (Gannoruwa and Ruwanpura., 2007) range from 80 to 120 dBA (Spencer and Kolvochik., 2007; Haron et al, 2012). Noise generates by these equipments, put the operators to be at risks of overexposure to high noise level (Suter., 2002; Hong., 2005; Blute et al., 1999; Fernandez et al., 2008; Spencer and Kolvachik., 2007) that come from their own task as well as from others (Lusk et al., 1999). An association between noise exposure and hearing loss has been recognized (Davies et al., 2005; Shaikh., 1999). Hearing loss has no cure but somehow it is preventable (Lin et al., 2007; Kurmis and Apps., 2007).Construction workers recorded prevalence of hearing loss (Legris and Poulin., 1998) where during 1992 to 1998, construction industry recorded one of the highest hearing loss claims due to exposure to hazardous noise level from all degrees and severity (Suter, 2002; Hattis., 1998). Noise-induce hearing loss (NIHL) and tinnitus usually occurs due to continuous exposure to noise (Yoshioko et al., 2010; Seixas et al., 2005; Davies et al., 2005; Natchigall et al., 2003; Fernandez et al., 2008) over 80 dBA and strongly associated (Palmer et al., 2002., Hamoda et al., 2008). Most construction workers lose their hearing ability after years working in the sector (Hong, 2005). The symptom of hearing loss

include the occurrence of tinnitus and hard in understand speech. Noise-induced tininitus occurs on the early stage, in which workers usually have ringing in the ears (Hamoda, 2008) and prolong exposure to excessive noise may result in permanent hearing loss (Mueller et al., 2008). Tinnitus may be temporary or permanent depend on duration of exposure (Vermeer and Passchier., 2000) and will increase with age (Palmer et al., 2002). Age is the common causes of hearing loss among older worker that is presbycusis or loss of hearing sensitivity due to advanced age (Ciobra et al., 2011). While hardly understand speech (Vermeer and Passchier., 2000) for workers related with loss of hearing ability in the middle of the frequency range of human voices (Kurmis and Apps., 2007). Beside hearing loss, noise exposure is also associated with other health effects such as an increase in diastolic blood pressure (van-Djik, 1990; Davies et al., 2005) and cardiovascular disease risk (Whitaker et al., 2004; van-Kempen et al., 2002; Gopinath et al., 2011). This study intends to evaluate the daily 8-hours noise exposure levels of heavy equipment operators (TWA) of road construction workers, prevalence of exceeding the permissible limits and the prevalence of sysptom hearing loss among the operators.

2.0 2.1

Methodology Evaluation of worker’s noise exposure

Noise exposure level of typical heavy equipment operators used in road construction were measured using Personal noise dosimeter The Edge Quest Technologies model eg4 satisfied the requirements of ANSI S1.25-1991(R1997) – Specification for personal noise dosimeters and IEC 1252-1993 – Electroacoustic, . Noise dosimeter was clipped onto worker’s shoulder or at any position close to the ear that receive much noise. The duration of measurement usually conducted for 8-hours in order to get the full shift exposure or 8hour time weighted average (TWA). Data recorded noise dosimeter were generated to be analysed using Quest suite Professional software. Before and after measurements were conducted, noise dosimeter was calibrated at 114 dB in order to control measurement errors and uncertainties to acceptable levels. The measurement followed guidelines from ISO 9612, Acoustics- Determination of occupational noise exposure- Engineering Method.

2.2

Compliance with the regulation

Noise exposure level of heavy equipment operators was compared with existing regulation on occupational noise. Malaysia implemented Factory and Machinery Regulation (FMR)(1989) under Occupational Safety and Health (OSHA) that recommends these standards to protect workers from hearing losses resulting from occupational noise exposure.According to OSHA, worker TWAs should fall below the recommended exposure limit of 85 dBA to classify as a safe working environment. The worker also should not expose to maximum permissible exposure limit which is above 90 dB without wearing hearing protection devices. The number of workers fall under these categories were then used to predict risk of developing material hearing impairment suggested by NIOSH 1997. Each worker is assumed working 5 day every week over 40 years working lifetime and the risk of hearing loss experienced by workers can be determined as shown in Table 1. Table 1: NIOSH 1997 excess risk of developing material hearing impairment Exposure Level (8-hour time-weighted average) Excess Risk 75 dBA 80 dB A 1% 85 dB A 8% 90 dB A 25%

2.3

Determination prevalence symptoms of hearing loss

During noiseexposure measurement, prevalence of tinnitus among operators and the hearing ability were observed.Worker’s information on age and working experience in the construction industry were recorded.Observation of worker’s hearing ability was recorded using rating as shown in Table 2.Previous research by Ahmed et al. (2004) also used questions in order to obtained hearing impairment among workers for assessing hearing loss on worker.Since the workers exposed to hazardous noise level, hearing ability of these workers was observed.

Hearing ability Poor Moderate Good

3.0 3.1

Table 2: Indicator of worker’s hearing ability Observation Most common causes of hearing loss in adults that associate with noise exposure and presbycusis (Ciobra et al., 2011) Workers that hardly understand the questions and discriminate speech(Vermeer and Passchier., 2000; Kurmis and Apps., 2007) Workers that answer the interviewer's question smoothly

Results and Discussion Worker’s noise exposure

Six construction sites around Johor Baharu area that performed road works and pavement activities were selected. 57 operators of typical heavy equipment used in road construction undergone for noise measurement. 42 of them work in road work stage and another 15 operator work in pavement stage.Figure 1 shows the percentage of heavy equipment operators that exceed OSHA action level and permissible exposure limit. Only 3.5% of operators wore hearing protectionand 19.3% of heavy equipments have closed air cabin. Group of roller-compacter operator’s daily noise exposure exceeded 85 dBAwith all operators’ daily noise exposure on pavement stage was beyond 80 dBA.

Figure 1: Percentage of operators used HPD, worked in closed cab and exposed above the levels in FMR, 1989. Table 3 shows the comparison of daily noise exposure for different heavy equipment operators that works in road work and pavement stage.Dump truck operators recorded the lowest mean of daily noise exposure. This situation was related to a situation where dump

truck operators mostly sat in a closed cab with air condition which prevents them from outer noise while premix roller-compacter’s operator recorded the highest mean TWA of 89.98 dBA. In this research, the quantity of backhoe operator that undergoes noise measurement was high compare to other typical heavy equipment operators. This situation suggests backhoe is widely used in road construction due to its ability to perform a variety of tasks such as carrying light construction tools within the construction area and digging. Table 3: Comparison of operator’s daily noise exposure between groups of heavy equipment

Operators

Excavator Backhoe Roller-comp Backpusher Motor grader Dump truck Paver Premix-RC Tyre roller

3.2

MeanTWA (dBA) 79.56 82.99 86.41 86.55 82.63

Standard Deviation (dBA) 5.18 2.39 2.32 1.72 2.17

78.47 86.24 89.98 84.03

3.94 2.51 4.06 1.26

Minimum Maximum No of (dBA) (dBA) operators 66.3 78.5 83.4 84.7 80.3

83.9 85.7 90.1 88.5 84.6

10 11 7 4 3

72.7 84.5 85.4 81.4

84. 90.6 95.7 87.9

7 5 6 4

Compliance with regulation

OSHA requires the workers to be protected when their daily noise exposure exceeded 85 dBA. The maximumallowed duration of exposure at and above 90 dBA is 8 hours. Yet, workers still expose beyond this limit or than 8 hours.Table 4 shows the percentage of heavy equipment operators that exposed below and above the action level and permissible exposure limit according to the FMR(1989). According to NIOSH 1997, 38.6% operators were predicted to develop 8% of material hearing impairment and 7% operators predicted to develop 25% of material hearing impairment after 40 years in the construction sector. Table 4: Percentage of exposed workers according to FMR (1989)

Stage Unit Weight, kg/m3 (lb/ft3)

Absorption

3.2 - 3.6 1600 - 1920 (100 - 120) up to 3%

Properties of steel slag Physical and chemical properties Steel slag aggregates are consists extremely angular in shape and have rough surface texture. They considered high bulk specific gravity and reasonable water absorption (less than 3 percent). The chemical composition of slag is frequently expressed in terms of simple oxides calculated from elemental analysis determined by x-ray fluorescence. One more importance is the mineralogical form of the slag, which is highly dependent on the rate of slag cooling in the steel-making process. Table 1 shows the physical and chemical properties of steel slag. Mechanical Properties Processed steel slag has favorable mechanical properties for aggregate use, including good abrasion resistance, good soundness characteristics, and high bearing strength. Table 2 shows the mechanical properties of steel slag.

Table 2: Typical mechanical properties of steel slag. (According to US. Department of Transportation, Federal Highway Administration, Holliday et al., 1997) Property Los Angeles Abrasion (ASTM C131), % Sodium Sulfate Soundness Loss (ASTM C88), % Angle of Internal Friction Hardness (measured by Moh's scale of mineral hardness)* California Bearing Ratio (CBR), % top size 19 mm (3/4 inch)** * Hardness of dolomite measured on same scale is 3 to 4. ** Typical CBR value for crushed limestone is 100%.

Value 20 - 25