An integrated framework for life cycle costings in buildings. Mohammed Kishk and Assem Al-Hajj, The Robert Gordon University. ISBN 0-85406-968-2 ...
COBRA 1999 An integrated framework for life cycle costings in buildings Mohammed Kishk and Assem Al-Hajj, The Robert Gordon University
ISBN 0-85406-968-2
AN INTEGRATED FRAMEWORK FOR LIFE CYCLE COSTING IN BUILDINGS Mohammed Kishk and Assem Al-Hajj School of Construction, Property and Surveying, The Robert Gordon University, UK
ABSTRACT This paper summarises the difficulties in the application of life cycle costing (LCC) as a decision making tool in the construction industry on the parts of the client, the analyst and the industry practices. Life cycle costing encompasses a great deal of uncertainty, functions of which are data imperfection, randomness and ambiguity. The traditional use of the probability theory and statistics in LCC analyses could only deal with random uncertainty. This falls short from dealing with situations including incomplete information, human judgement and uncertainty. An integrated LCC framework is proposed. This framework is based on the simple idea that a complex problem may be deconstructed into simpler tasks. Then, the appropriate tools are assigned a subset of tasks that match their capabilities. The framework utilises statistics, fuzzy set theory and artificial neural networks to improve the quality of LCC as a decision making tool. Keywords:
Artificial Neural Networks, Decision Making, Fuzzy Set Theory, Life Cycle Cost(ing).
1 INTRODUCTION It has long been recognised that it is unsatisfactory to evaluate the costs of projects on the basis of their initial costs alone. Life cycle costing is an evaluation technique that takes into consideration all costs that emerge during the life cycle of a project, such as initial investment costs and subsequent maintenance and operations costs and salvage and resale value. The technique is primarily used to facilitate effective choice between project alternatives. It is best conducted during the early viability, feasibility and conceptual design where most, if not all, options are open to consideration (Griffin, 1993). Paulson (1976) stated that the ability to influence cost decreases continually as the project progresses, from 100% at project sanction to typically 20% or less by the time construction starts. Ashworth (1993) argues that the acquisition of LCC knowledge and skills through research and application is still in its infancy, with a considerable gap between theory and practice. This is still the case today.
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2 DIFFICULTIES WITH LCC Many researchers (Ashworth, 1987, 1989, 1993, 1996; Flanagan et al., 1989; Ferry and Flanagan, 1991; Bull, 1993; among others) have tried to highlight the areas causing difficulties in the application of LCC in the construction industry. These difficulties are summarised in the following subsections. 2.1 On the Part Of The Industry There are two difficulties associated with the construction industry. These are: 1. The capital cost of construction is almost always separated from the running cost. It is normal practice to accept the cheapest initial cost and then hand over the building to others to maintain. In addition, there is no clear definition of the buyer, seller, and their responsibilities towards the operating and maintenance costs (Bull, 1993). 2. Lack of motivation in cost optimisation because the design and cost estimating fees are usually a percentage of the total project cost (McGeorge, 1993). However, the expansion of new project delivery systems such as private finance initiative (PFI) and build, operate and transfer (BOT) seems to overcome these obstacles. By the end of the century, it is expected that projects commissioned under the PFI will account for approximately 13% of what would otherwise be government capital expenditure (Grubb, 1998). 2.2 On the Part Of The Client 1. There is a lack of understanding on the part of the client (Bull, 1993). This may increase the possibility of subjective decision making. 2. The presence of multiple aspects of needs desired by clients (Chinyio et al., 1998). Usually, these criteria are weighted numerically according to their importance. Then, a score is given to each alternative in respect of each criterion. There are many methods to specify weights. However, most of these methods require a high degree of judgement and/or statistical data. In addition, the best output that can be achieved from this approach is an indication of priority orders rather than a rigid decision (Shen and Spedding, 1998). 2.3 On the Part Of the Analyst 1. The need to be able to forecast, a long way ahead in time, many factors such as life cycles, future operating and maintenance costs, and discount and inflation rates (Ferry and Flanagan, 1991). The uncertainty surrounding the variables used in any LCC model should be eliminated to improve the accuracy of the estimation. 2. The need to deal with intangible (non-monetary) data because, in some cases they have a decisive role to play (Flanagan et al., 1989). 3. The difficulty of obtaining the proper level of information upon which to base LCC. This is because of the lack of appropriate, relevant and reliable historical information and data (Bull, 1993). In addition, costs of data collection are enormous (Ferry and Flanagan, 1991). Furthermore, the time needed of data collection and the analysis process may leave inadequate time for the essential dialogue with the decision-maker and the re-run of alternative options. This is one of the reasons why computerised
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models are valuable (Griffin, 1993).
3 DIFFICULTIES WITH CURRENT ANALYSIS TOOLS 3.1 Cost Estimating Methods Estimating models used in the industry can be broadly classified as detailed models, parametric models and analogous models (Asiedu and Gu, 1998). Dysert (1997) suggested that any particular estimate may involve any combination of estimating techniques or methods. Detailed models are the most time consuming and costly approach and require a very detailed knowledge of the product and processes. In the parametric method to cost estimating, the cost drivers are related to cost by cost estimating relationships (CERs). Examples in the framework of the construction industry include the simple models developed by Al-Hajj (1991) and Al-Hajj and Horner (1998) to predict the running costs in buildings. These models are based on the finding that for defined building categories identical cost-significant items can be derived using a statistical approach. However, estimating the parameters may be difficult at early stages of the design due to lack of data or insight (Mason and Kahn, 1997). Where no similar project history, judgement often will be relied upon to formulate the project estimate (Dysert, 1997). A lot of companies in the UK only use judgement in their predictions as they found statistical methods to be far more cumbersome, expensive, and no more accurate (Sparks and McHugh, 1984). According to Ashowrth (1996), historic data will never provide precise solutions and high quality judgement will always be required. However, the problem of personal bias arises because people are involved in applying judgement. A person’s assigned role or position seems to influence his or her forecasts (Fabrycky and Blanchard, 1991). Therefore, a method for modelling human judgement should be built in any proposed model. 3.2 Uncertainty and Risk Analysis The decision-making process in the construction industry often involves uncertainty (Flanagan and Norman, 1993). Two approaches to risk analysis can be identified: (1) probability-based approach; and (2) traditional sensitivity analysis approach. Probability-based procedures are more powerful than traditional sensitivity analysis procedures. The Monte Carlo simulation (Flanagan et al., 1987) interprets cost data indirectly to generate a probability distribution for total costs from the descriptions of elemental cost distributions. This provides the decision maker with a more wider view in the final choice between alternatives. However, this will not remove the need for the decision-maker to apply judgement. There will be, inevitably, a degree of subjectivity in this judgement (Flanagan et al., 1989). The main assumption in these methods is that all uncertainty follows the characteristics of random uncertainty. A random process is one in where the outcomes of any particular realisation of the process are strictly a matter of chance. However, the 94
overwhelming amount of uncertainty is non-random in nature (Ross, 1995). Some of the limitations of the probability theory can be removed by considering subjective probability (Flanagan and Norman, 1993). However, there are two major criticisms to the subjective approach: (1) a probability which is subjectively measured today could well be different tomorrow; and (2) the additional costs in time and expertise necessary to extract the knowledge (Byrne, 1997). This raises a question over the adequacy of the probability theory in modelling uncertainty.
4 THE WAY AHEAD To tackle some of the difficulties and shortcomings presented above, an integrated framework is proposed. First, the computing technologies of fuzzy logic and neural networks are briefly introduced. Then, the proposed framework is outlined. 4.1 Fuzzy Set Theory The concept of fuzzy set theory (FST) was introduced by Zadeh (1965). Fuzzy sets are the basis of fuzzy logic. A fuzzy set F is defined as a set of ordered pairs (x, µ (x)) by a membership function f, which establishes the following relationship: f: x → µ (x) where x is the value of an element in the domain of function f; µ (x) is the value of f at x; and µ (x) has values in the interval [0, 1]. For a given value x, µ (x) = 0 means that x has null membership in F, and µ (x) = 1 means that x has a full membership. The fuzzy set introduces vagueness by eliminating the sharp boundary dividing members of the set from non-members. This is because the transition from member to non-member is gradual (Fig. 1). This differs from the conventional crisp set theory where objects are either in or out of the set (Kruse et al., 1994).
0.0
cont mem inuous bers hip
µ (x)
sharp edged membership
1.0
(x)
1.0
0.0 (a)
x
(b)
x
Fig. (1): Membership functions for (a) a crisp set and (b) a fuzzy set.
There has been quite a debate over the years regarding fuzziness and probability. However, some researchers claim that probability may be viewed as a subset of the fuzzy set theory (Zadeh, 1995). Fuzzy set theory is an efficient tool for modelling the uncertainty associated with vagueness, imprecision, and/or with a lack of information
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regarding some elements of a given problem. The fuzzy logic seems to be the most appropriate in two kinds of situations: (1) very complex models where understanding is limited or judgmental, and (2) processes where human reasoning, human perception, or human decision making are inextricably involved (Ross, 1995; Kosko, 1997). The fuzzy approach has three more advantages namely: (1) the simplicity of the mathematical concepts of FST; (2) its ability to match any set of input-output data; and (3) it can be blended with traditional techniques. The use of fuzzy logic has recently yielded impressive success in many engineering and science domains. Examples of recent studies in the framework of Civil Engineering and Construction Industry include Mason and Kahn (1997), Chao and Skibniewski (1998), Fayek (1998), Holt (1998), among others. 4.2 Artificial Neural Networks Artificial neural networks (ANNs) mimic, in a very simplified way, the human brain structure and functions. Neural networks are composed of interconnected elements called neurons or nodes. The neurons are organised in the form of layers. In the simplest architecture of an ANN, there is an input layer, one or more hidden layers, and an output layer (Fig. 2).
Input layer of source nodes
Layer of hidden neurons
Layer of output neurons
Fig. (2): A three-layer neural network. In an Ann, each neuron receives information from other neurons, processes it through an activation function, and produces output to other neurons. The output of a neuron k, yk, is given by n y k = ϕ ∑ w kj x j + b k j =1 where, ϕ is the activation function, xj are the inputs to the neuron, wkj are the weights, and bk is an externally applied bias. Before an ANN can be used to perform its task, it should be trained to do so. This training or learning process is simply to determine the weights and biases using an appropriate learning algorithm. A neural network derives its computing power through its massive parallel distributed 96
structure and its ability to learn and therefore generalise. Generalisation refers to the ability of ANNs to produce reasonable outputs for inputs not encountered during training (Haykin, 1999). In addition, the use of ANN computing technology offers three more useful capabilities: 1. Non-linearity which is an important property. 2. Universality that makes it possible to share theories and learning algorithms with other science domains. 3. It eliminates the need for finding a good relationship that mathematically describes the output as a function of the input data. This is an advantage over classical statistical techniques where it is typically assumed that the form of the correct model is known and the objective is to estimate the model parameters. Many researchers have highlighted the potential application of ANNs in construction (e.g. Moselhi et al., 1991; Boussabaine, 1996; Boussabaine and Duff, 1996; Adeli and Wu, 1998; to mention a few). However, neural networks cannot provide the solution by working in isolation. Rather, they need to be integrated into a consistent system approach (Haykin, 1999). In addition, there is no unique architecture or training algorithm suitable for all problems. Recently, some researchers have developed models utilising the desirable properties of both fuzzy logic and neural networks (e.g. Boussabaine and Elhag, 1997). These models are known in the literature as neurofuzzy systems. There are many ways fuzzy logic can be integrated with neural networks. Two main categories can be identified (Rao and Rao, 1993; Bossley et al., 1995; Kosko, 1997): 1. An ANN can be used to extract the required rules and membership functions for a fuzzy system. This is useful when it is difficult to deduce these rules and functions with a given set of complex data. 2. Fuzzy logic can be used in the pre-processing and/or post-processing of data for an ANN, especially when dealing with less tangible, judgmental data. 4.4 An Integrated Framework Based on the above arguments, an integrated framework for life cycle costing may be proposed. Figure (3) shows a schematic representation outlining the issues around which the proposed framework is structured. The framework utilises the inherent capabilities of FST, ANNs, probability and statistics to overcome the difficulties associated of various tasks of LCC. The data are evaluated in terms of availability, tangibility and certainty. The levels of these measures increase from left to right; and consequently the appropriate tools varied gradually from modelfree methods to closed form solutions.
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Data availability ?
Not available
Available
Intangible
Tangible
Uncertain
Fuzzy set theory
Certain
Nonrandom
Random
Neural networks
Probability & statistics
Closed form solution
Fig. (3): Schematic representation of the proposed framework.
The benefits of the proposed framework may be summarised as: 1. It will identify aspects of information and data that are really needed and can be used. This will reduce the cost undertaking the LCC analysis. 2. It will enable decision-makers to get estimates and decision-making guidelines in a systematic and fast manner. 3. It will reduce the subjective elements that are predominant in life cycle costing. It is anticipated that by making the analysis process more objective, straightforward, and less expensive, the LCC technique can receive extensive practical application in the industry.
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5 CONCLUSION The present state of the art of LCC as a decision making/support tool in buildings may be regarded as less than satisfactory. There are many difficulties in the implementation of LCC. Methods designed to tackle some of these difficulties exist but are, in general, disjointed. A need remains for models that are designed to suit the actual practices and requirements of the construction industry. Life cycle costing models will be more readily accepted if they can ensure the speed and ease of use. These models should also provide estimates from different levels of data and information availability. Life cycle costing does not fit completely into the framework of probability and statistics theories. An obvious direction for improving the quality of LCC as a decisionsupporting tool is to incorporate, where appropriate, the techniques of artificial neural networks and fuzzy set theory. These techniques can model non-linear characteristics, generalise and deal with situations including incomplete information, human judgement and uncertainty. It is anticipated that the proposed framework will stimulate more research in the area of life cycle costing. Based on this framework, an integrated LCC model is being developed in the School of Construction, Property and Surveying, The Robert Gordon University.
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