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Predicting Change by Evaluating the Change Implementation Process in Construction Projects Using Event Tree Analysis Gholamreza Heravi, M.ASCE 1; and Mohammad Hadi Charkhakan 2
Abstract: Changes often have significant and unpredictable effects on construction projects. There are numerous kinds of changes that can occur in construction projects. This research considers changes that have negative effects on one or more project objectives or project participant’s interests. Promoting a change prediction system can increase the ability to prevent the negative effects of change. Throughout a construction project life cycle, a change may occur through two consecutive phases, including the change formation phase and change implementation phase. To predict occurrence of change in construction projects, this study focuses on the implementation phase of change occurrence. The implementation process of a change, which has negative effects, is almost often accompanied with unresolved issues, conflicts, claims, or even disputes. The purpose of this study is to develop a framework to predict the change in construction projects by evaluating the change implementation process. In this way, first, the predicted change formation scenarios are entered into the change implementation phase by change requests and/or change orders. Then, based on general possible scenarios of conflicts/nonconflicts, followed by scenarios of request/nonrequest, the change implementation phase is subdivided into change implementation paths. To evaluate the developed change implementation paths, a risk index is developed using the event tree analysis. Separating the probability and effect of different paths by utilizing a risk index provides the ability to analyze the probability of different effects of implementing change. Thereby, occurrence of change and arising conflicts due to the change implementation process may be predicted. Finally, a real construction project is considered as an example to illustrate the application of the method and demonstrate the method’s capabilities. DOI: 10.1061/(ASCE)ME.1943-5479 .0000325. © 2014 American Society of Civil Engineers. Author keywords: Change management; Change formation scenario; Change implementation process; Construction project; Change prediction; Event tree analysis; Conflicts.
Introduction Change is commonly defined as any event that results in a modification of the original scope, execution time, cost, and/or quality of work in projects (Ibbs and Allen 1995; Ibbs et al. 2007). Various aspects of the nature of change have been addressed in some previous research. In some research, such as Ibbs et al. (2001, 2007), changes with regard to the contract were studied, including changes to project scope and goals. In some other research, such as Nalewaik (2010), changes in a project and its participants were addressed. Change, from another view, can be beneficial, neutral, or disruptive according to its effects (Motawa et al. 2007). Although some projects may benefit from positive changes, most changes interrupt the flow of work. Thus, they result in cost and time overrun (Sun and Meng 2009). Changes, also, may have significant and often unpredictable effects on a project’s organization and management (Love et al. 2002). Although agreed-upon changes to the contract documents occur frequently, disputes 1 Assistant Professor, School of Civil Engineering, College of Engineering, Univ. of Tehran, 16 Azar Ave., P.O. Box 11155-4563, Tehran, Iran (corresponding author). E-mail:
[email protected] 2 Ph.D. Student, School of Civil Engineering, College of Engineering, Univ. of Tehran, 16 Azar Ave., P.O. Box 11155-4563, Tehran, Iran. E-mail:
[email protected] Note. This manuscript was submitted on June 8, 2013; approved on June 20, 2014; published online on July 16, 2014. Discussion period open until December 16, 2014; separate discussions must be submitted for individual papers. This paper is part of the Journal of Management in Engineering, © ASCE, ISSN 0742-597X/04014081(9)/$25.00.
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among the stakeholders of a project are almost as common (PMI 2007). This research considers changes that have negative effects on one or more project objectives or project participant’s interests. In this regard, the research concerns the conflicts and disputes which result from issued change requests and/or change orders. According to Motawa et al. (2006), proactive change management requires estimating the likelihood of occurrence of a change event, as well as estimating the degree of change impacts on project parameters. In this regard, predicting occurrence of change is a prerequisite for proactive change management, which may increase the ability to prevent the adverse effects of change. Although it is hard to predict changes in construction projects (Zhao et al. 2010), this study tries to predict such changes by developing a change prediction framework. The review of previous studies related to this research leads to the following two categories of research: 1. The effects of changes in construction projects. Semple et al. (1994) reported that one of the most common causes of claims and disputes is scope changes. Hanna et al. (1999) proposed a linear regression model that predicts the impact of change orders on labor productivity. Moselhi et al. (2005) conducted the study to investigate the impact of change orders on construction productivity and introduced a neural network model for quantifying this impact. Zaneldin (2006) presented the results of a study of construction claims in the Emirates of Dubai and Abu Dhabi in the United Arab Emirates (UAE). The results of this analysis indicated that “changes” are the most frequent causes of claims. A claim is a demand for something, usually
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as a result of an action or change order against the terms and conditions of a contract (PMI 2007). Abd El-Razek et al. (2007) synthesized causes of claims from literature. Most of their mentioned causes of claims are directly or indirectly related to changes. However, the distinction between a claim and a change is the element of disagreement between the parties. If the parties reach an agreement, then the claim disappears and becomes a change. If not, the claim may proceed to dispute (PMI 2007). Motawa et al. (2007) emphasized that change is a major cause of delay, disruption, and disputes between project parties. Sun and Meng (2009) developed two taxonomies for change causes and change effects by reviewing and synthesizing existing literature. In their study, conflicts and disputes have been considered as both change causes and change effects. Serag et al. (2010) developed a statistical model to quantify the increase of the contract price due to change orders in heavy construction projects in Florida. Al-Nuaimi et al. (2010) discussed variations in public construction projects in Oman, and reported that the most important effects of change orders on the project are schedule delays, disputes, and cost overruns. 2. Management of changes in construction projects. Ibbs et al. (2001) emphasized that developing and implementing a project change management system before starting the project is a useful and proactive step toward constructive change management. They introduced a comprehensive project change management system that was founded on five principles: (1) promote a balanced change culture, (2) recognize change, (3) evaluate change, (4) implement change, and (5) continuously improve from lessons learned. Motawa et al. (2006) introduced a fuzzy model for predicting change events based on the available information at the early stages of a project and presented the IT system architecture of the proposed model. Later, Motawa et al. (2007) developed a model to represent the key decision points required to implement changes. Their model simulated the iterative cycles of concurrent design and construction because of unanticipated changes and their subsequent impacts. Zhao et al. (2010) proposed an activity change prediction system to simulate the process and to generate detailed information about changes that may happen during the construction process. Charkhakan and Heravi (2012) presented an approach to identifying change formation scenarios based on change occurrence paths by using a dynamic programming method. Recently, Heravi and Charkhakan (2013) indicated that the occurrence of change can be predicted by an approach which focuses on details by decomposing change occurrence phases and analyzing their components’ interrelations. They developed a method to evaluate both predictability and traceability of change formation scenarios (CFSs) and the relative importance of change formation components. The purpose of this study is to develop a framework to predict changes in construction projects by evaluating the change implementation process. Thus, this study focuses on the implementation phase of change occurrence, which follows the change formation phase. To address the research objective, the predicted CFSs (Heravi and Charkhakan 2013), through change requests and/or change orders, are entered into the change implementation phase.
Research Method Throughout a construction project life cycle, a change may occur through two consecutive phases, including the change formation phase and the change implementation phase. In this research, based © ASCE
Fig. 1. The main stages of the framework development
on general possible scenarios of conflicts/nonconflicts followed by scenarios of request/nonrequest, the change implementation phase is subdivided into change implementation paths (CIPs). To evaluate the CIPs, a risk index is developed using the event tree analysis (ETA). As a result, occurrence of change and arising conflicts due to the change implementation process may be predicted. Accordingly, the method of this research comprises three main stages as follows (Fig. 1): 1. Developing the structure of the change implementation phase; 2. Analyzing the change implementation phase by developing a risk index, based on the ETA; and 3. Developing a framework to predict the change by evaluating the change implementation process. Finally, a real construction project is considered as an example to illustrate the application of this method and demonstrate the method capabilities. Event Tree Analysis The ETA is a commonly applied technique used to identify the consequences that can result from following the occurrence of a potentially hazardous event. It was first applied to risk assessments for nuclear industry, but it is now utilized by other industries. Quantification of the event tree diagram allows the frequency of each of the outcomes to be predicted (Andrews and Dunnett 2000). Event trees offer a way to describe a sequence of probabilistic events, together with their probabilities and impacts. They are perhaps the most useful methods for depicting a probabilistic sequence, because they are very intuitive and the mathematics to combine the probabilities is simple. Event trees are built out of nodes (boxes) and arcs (arrows). The tree starts from the left, with a node and arrows to the right indicating possible outcomes and their probabilities. Branching out from these boxes are arrows to the next probability event, and attached to these arrows are the conditional probabilities that the next level of the event occurs. The conditional nature of the probabilities in an event tree is extremely important to underline (Vose 2008, pp. 39–40). To address the purpose of this research, the ETA provides the ability to identify the probability and effect of different scenarios in the change implementation phase. In this regard, each decision made by participants in the procedure of change implementation is considered as a branch of the event tree.
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Various research has been conducted using the event trees for different applications, among them Song et al. (2012) and Abdelgawad and Fayek (2012). Song et al. (2012) used the ETA to simulate the alternative dispute-resolution (ADR) processes and determine the probability mass function of ADR-implementation costs. These probabilities are employed to calculate the expected ADR-implementation costs and to derive the insurance premium. Abdelgawad and Fayek (2012) introduced the concept of fuzzy event tree analysis and provided a comprehensive framework based on a combination of fuzzy logic with event trees to provide a practical and thorough approach for assessing the level of criticality of risk events in the construction domain and in supporting quantitative risk analysis. Risk Index Development In this research, on the basis of qualitative risk analysis (PMI 2013), a risk index is developed by multiplying the probability and effect of a change. This index is utilized to separate the known probability and unknown effect of the change. The risk probability assessment investigates the likelihood that each specific risk will occur. The risk impact assessment investigates the potential effect on a project objective such as schedule, cost, quality, or performance, including both negative effects for threats and positive effects for opportunities (PMI 2013). Separating the probability and effect of different paths by utilizing the risk index provides the ability to analyze the probability of different effects of implementing change. Validation Validation always depends on the specific purpose of the research (Law 2007). “No single definition of the ingredients or subsets of the concept of validity exists,” according to El-Diraby and O’Connor (2004). Lucko and Rojas (2010) asserted that there are no definite criteria for how to accomplish validation. In their research, in addition to two main areas of validity, including external and internal, they referred to face validity. The face validity is a subjective judgment of nonstatistical nature that seeks the opinion of nonresearchers regarding the validity of a particular study (Leedy and Ormrod 2001). In regard to external validity, the innovative approach of this research can be used for different projects. Generally, one validation of the framework is the application in the construction project. Obviously, the results of implementing the method in any project can only be used in that project, e.g., the results of the application example in this research. Likewise, validity of the developed framework is confirmed by validity of its backbone, i.e., the ETA, and utilizing the face validity by evaluating the documents of the project and expert judgment.
Developing the Structure of Change Implementation Phase Throughout a construction project life cycle, a change may occur through two consecutive phases: 1. The change formation phase. Heravi and Charkhakan (2013) subdivided the change formation phase into change formation paths (CFPs) by decomposing it into the following three change formation components (CFCs): (1) change sources, the conditions and characteristics of a project that create a change, such as inherent characteristics of a project including location, date, and type of project; (2) change drivers, the conditions and characteristics that merely increase the probability of change occurrence, such as discouraging factors such as low payments for the design team which increase © ASCE
the probability of design errors; and (3) change causes, the conditions or events that directly lead to change, such as design errors made by the design team. In their research, they estimated the relative importance of the CFCs and the likelihood of occurrences, as well as the traceability of change formation scenarios (CFSs) through analyzing the CFPs. The CFS is defined by its source and its cause, as the first and last change components of the related CFPs (Heravi and Charkhakan 2013). As a result, predicted CFSs through change requests (or orders) are entered into the next phase of change occurrence. 2. The change implementation phase. This phase follows the change formation phase. It is decomposed into change implementation nodes and subdivided into CIPs. This study focuses on this phase of change occurrence in construction projects. Conflicts in the Change Implementation Phase The implementation process of a change that has negative effects is almost always accompanied with unresolved issues, conflicts, claims, or even disputes. In construction projects, unresolved issues or conflicts can escalate into a claim and become a fierce contractual dispute among the stakeholders (PMI 2007). Yiu et al. (2011) identified the traditional adversarial relationship between clients and contractors as a major source of claims and disputes. This paper defines the relationship between conflicts and disputes in a way that conflicts can lead to disputes, similar to Menassa and Pe˜na Mora (2010). To analyze the complexity of relationships between changes and conflicts, distinction between the following kinds of conflicts is necessary: 1. The conflicts causing changes. Conflicts in the change formation phase are classified as one of the change causes. A number of studies have dealt with this role of conflicts (Chan and Kumaraswamy 1997; Sun and Meng 2009; Charkhakan and Heravi 2012; Heravi and Charkhakan 2013). 2. The conflicts arising through change implementation. Conflicts among project parties arise mostly throughout the change implementation phase. The change implementation depends on the behavior of the project parties. Since different parties with different motives affect the paths of implementation for change, conflicts arise after the need for change is declared by one of the project parties and before the changes are applied. This research will mainly focus on such conflicts, and the complexity of relationships between conflicts and changes will be explained by decomposing and analyzing the structure of the implementation phase. 3. The conflicts arising after change occurrence. Numerous studies have emphasized that change causes conflicts (Hanna et al. 2007; Ibbs 2005; Serag et al. 2010). Conflicts among project parties, due to change occurrence, are major causes of claims and disputes in construction projects. Structure of the Change Implementation Phase The change formation phase is linked to the change implementation phase by change request and/or change order. Thus, the change implementation phase may be subdivided into CIPs by pursuing CFSs through change request and change order. Change Request As a consequence of a CFS, the change request may be issued if the project party demanding the change has no approval authority. Then, the issued change request would be considered by the party having approval authority (i.e., project owner). In this regard, different paths may be followed depending on probable arising
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conflicts and succeeding decisions. Basically, with respect to conflicts, the following two scenarios are possible: 1. Nonconflicts. A change request does not lead to conflicts because of the owner’s decision and succeeding concerns of the requesting party. 2. Conflicts. A change request leads to conflicts because of the owner’s decision and succeeding concerns of requesting party. Each of the aforementioned scenarios should be continued by the change order if they lead to approval of the change request. Change Order A change order is a contractual document that is issued to accommodate any additional work in a contract that was not included in the original contract (Anastasopoulos et al. 2010). As a consequence of a CFS, a change order may be issued directly if the project party demanding the change has approval authority (i.e., project owner). On the other hand, if a CFS made through a change request implementation node leads to approval, then the related paths should be followed by a change order. Thus, various paths may be followed on the basis of how a change order causes claims and conflicts. In this way, two general possible scenarios are considered as follows: 1. Nonrequest (or nonclaim). A change order will not cause a request or claim by interested parties (i.e., the party receiving the order). 2. Request (or claim). A change order causes a claim (or request) by interested parties. This scenario may be followed by two possible scenarios: a. Nonconflicts. A change order does not lead to conflicts because of concerns of the party that has issued the order and the concerns of other interested parties (i.e., the parties receiving the order). b. Conflicts. A change order leads to conflicts because of concerns of the party that has issued the order and the concerns of other interested parties. As a result, depending on the project characteristics (e.g., the project delivery system), different scenarios may be followed. Each of the aforementioned scenarios may lead to occurrence or nonoccurrence of a change, through change occurrence paths (COPs) or change nonoccurrence paths (CNPs), respectively.
Analyzing the Change Implementation Phase Using the ETA Event Tree Diagram of Change Implementation Paths The implementation paths for a change request and a change order may be developed in the form of an event tree, based on the scenarios mentioned in the previous section. One of the tools of the decision tree method which can be used in the ETA is pruning, which eliminates some branches to increase accuracy. In this study, pruning will be used to eliminate redundancy paths identified based on the developed assumptions. Thus, after making a complete tree, the branches that do not add accuracy will be eliminated. Fig. 2 illustrates a typical event tree diagram of CIPs in the change implementation phase. Risk Index of the Change Implementation Scenario Each CFS and its subsequent change request (or change order) is recognized as a change implementation scenario (CIS). In this research, the change implementation process for each CIS is evaluated by developing a risk index (RI). To develop the RI of a CIS, first, the probabilities and impacts of each path in the implementation process related to each CIS should be estimated. In this way, based on the conditional nature of the probabilities in an event tree and following the rules of conditional probability algebra (Vose 2008, p. 39), the probability of each CIP is calculated. The probability of each path is calculated by multiplying the probability of events located on the selected path. The impact of each path is the summation of the impacts of events located on the selected path. Then the risk index of each CIS is determined by the following equation: RIðkÞ ¼
i¼n X
PðiÞ × IðiÞ
ð1Þ
i¼1
where RIðkÞ= risk index of the CIS (k); PðiÞ = probability of the CIP (i) related to the CIS (k); IðiÞ = impact of the CIP (i) related to the CIS (k); n = number of CIP (i) for the CIS (k).
Fig. 2. The event tree diagram of CIPs in the change implementation phase © ASCE
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Table 1. Events and Their Probabilities in the Change Implementation Process
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Implementation branch
First level
Second level
Third level
Event
P
Event
P
Event
Change request
Nonconflicts Conflicts
(1 − A) A
— —
— —
Change ordera
Nonrequest Request
(1 − B) or (1 − B 0 ) B or B 0
— Nonconflicts Conflicts
— (1 − C) or (1 − C 0 ) C or C 0
— Agreement on nonoccurrence Agreement on occurrence Disagreement and litigation — Approval of request Rejection of request Approval of request Disagreement and litigation
a
P
b1 b2 b3 b4
— a1 a2 a3 — or b 0 1 or b 0 2 or b 0 3 or b 0 4
Probabilities of direct change orders may be different from indirect change orders (e.g., B and B 0 for direct and indirect change orders, respectively).
Developing the Framework to Predict a Change by Evaluating the Change Implementation Process
Table 2. The overall impact of each path, in a parametric format, is then calculated by summation of the impacts’ factor of events located on the selected path. The overall probability and impact of CIPs are depicted in Table 3.
Structure of the Developed Method The framework to predict the change by evaluating the change implementation process is developed in the following four steps. Step 1: Determining the CISs For each predicted CFS (Heravi and Charkhakan 2013), the factors that should be considered in its succeeding implementation process should be determined, including the change requesting party, the change ordering party, and the parties receiving the change order. Step 2: Developing the Event Tree Diagram of CIPs To determine the events of the implementation process and their probabilities depicted in Table 1, some assumptions are made as follows: 1. The change request partially approved is considered as a change request approval. 2. The owner’s change order is binding, so the only probable scenario for nonoccurrence of the owner’s order is litigation, as a result of failing to resolve the conflicts. 3. When each request in the change implementation process leads to nonconflicts, it is assumed that the request has been approved. Therefore, the nonconflicts’ CNPs should be eliminated. The event tree diagram of CIPs is developed and pruned based on the project requirements such as terms and contract provisions. Step 3: Evaluating the Probability and Impacts of CIPs for Each CIS 1. Probability. The probabilities of each event are shown in Table 1 in the form of parameters. The overall probability of each path is then calculated by multiplying the probability of events located on the selected path. 2. Impact. Various types of impacts based on possible consequences of the implementation process are described in
Step 4: Determining the Risk Indices of CISs with Respect to the Parametric Impacts According to Eq. (1) and the overall probability and parametric impact of the CIPs (Table 3), the risk index of each CIS is calculated with respect to the parametric impacts by the following equation: RIðkÞ ¼ P1 ðkÞ × I 1 ðkÞ þ P2 ðkÞ × I 2 ðkÞ þ P3 ðkÞ × I 3 ðkÞ þ P4 ðkÞ × I 4 ðkÞ
ð2Þ
where RIðkÞ = risk index of the CIS (k) calculated by Eq. (1); I 1 , I 2 , I 3 , and I 4 = overall parametric impacts of the CIPs related to the CIS (k) (Table 2); P1 ðkÞ, P2 ðkÞ, P3 ðkÞ, and P4 ðkÞ = overall probabilities of the CIPs related to the CIS (k) as coefficients of I 1 , I 2 , I 3 , and I 4 , respectively. Results of Application of the Developed Framework The application of the developed framework on a construction project leads to the following results: 1. The occurrence of change related to the CIS may be predicted based on the evaluated P1 , as a coefficient of I 1 ; larger values of P1 indicate higher probability of change occurrence. 2. The occurrence of change due to secondary requests related to the CIS may be predicted based on the evaluated P2 , as a coefficient of I 2 ; larger values of P2 indicate higher probability of secondary change occurrence. Secondary change occurrence is defined as the approved changes that have been requested by the party receiving the change order. 3. The arising conflicts caused by the CIS may be predicted based on the evaluated P3 , as a coefficient of I 3 ; larger values
Table 2. Impact Types Based on Possible Consequences of the Change Implementation Process Impact identifier I1 I2 I3 I4 a
Type of impact
Description
Change occurrence impacts Secondary change request impacts Conflict impactsa Litigation impactsa
The impacts related to change occurrence, as follows: direct effects, indirect effects, ripple effects, etc. The impacts related to occurrence of changes that have been requested by the party receiving the change orders The impacts merely related to arising conflicts due to the change implementation process The impacts merely related to arising litigation due to the change implementation process
These impacts are considered independent of engaged parties and the implementation branch.
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Table 3. Overall Probability and Impact of the CIPs (COPs and CNPs)
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Identifier
Change request scenarios Nonconflicts (1 − A)
COP COP COP COP CNP
(1) (2) (3) (4) (1)
COP COP COP COP CNP
(5) Conflicts (A) (6) (7) (8) (2)
CNP (3) CNP (4)
Agreement on occurrence (a2)
Agreement on nonoccurrence (a1) Disagreement and litigation (a3)
Change order scenarios
—
of P3 indicate higher probability of arising conflicts caused by the CIS throughout the change implementation process. 4. The arising litigation caused by the CIS may be predicted based on the evaluated P4 , as a coefficient of I 4 ; larger values of P4 indicate higher probability of arising litigation caused by the CIS throughout the change implementation process.
Application In this section, a real construction project is considered to illustrate the application of the method and demonstrate the method’s capabilities. Project Description The project is a subway station construction project which is located in the center of Tehran, the capital of Iran (Heravi and Charkhakan 2013). This project encountered various changes during the construction phase; namely, the duration and contract value greatly increased. The project delivery system is the construction management, and the project duration is 42 months. In this example, input data have been provided by a construction contractor task group including (1) the project manager, a civil engineer with 16 years of experience; (2) the technical manager, an architectural engineer with 13 years of experience; (3) the project control manager, a civil engineer with 10 years of experience; and (4) the contract manager, a civil engineer with 10 years of experience. Heravi and Charkhakan (2013) identified the most traceable CFSs in the project by utilizing the following identified CFCs: (1) six change sources including inherent characteristics of the project, characteristics of the project parties, characteristics of the project environment, factors related to the project delivery system, characteristics of the external stakeholders, and internal characteristics of the contractor’s company; (2) eight change causes including design errors, misunderstanding the owner’s need, the owner’s lack of commitment, construction phase errors, the owner’s need variation, site condition variation, unavailability of project resources, and disputes; and (3) five significant evaluation indices of change drivers including the amount of the public fund proportion in project finance, the project intensity (the ratio of the contract value to the contract duration), the number of simultaneous activities in the project schedule, the amount of project third-party © ASCE
Probability
Impact
Nonrequest (1 − B) ð1 − AÞ × ð1 − BÞ I1 Request (B) Nonconflicts (1 − C) Approval of request (b1) ð1 − AÞ × B × ð1 − CÞ × b1 I1 þ I2 Conflicts (C) Rejecting request (b2) ð1 − AÞ × B × C × b2 I1 þ I3 Approval of request (b3) ð1 − AÞ × B × C × b3 I1 þ I2 þ I3 Disagreement and ð1 − AÞ × B × C × b4 I3 þ I4 litigation (b4) Nonrequest (1 − B 0 ) A × a2 × ð1 − B 0 Þ I1 þ I3 Request (B 0 ) Nonconflicts (1 − C 0 ) Approval of request (b 0 1) A × a2 × B 0 × ð1 − C 0 Þ × b 0 1 I 1 þ I 2 þ I 3 Conflicts (C 0 ) Rejecting request (b 0 2) A × a2 × B 0 × C 0 × b 0 2 I 1 þ 2I 3 Approval of request (b 0 3) A × a2 × B 0 × C 0 × b 0 3 I 1 þ I 2 þ 2I 3 A × a2 × B 0 × C 0 × b 0 4 Disagreement and 2I 3 þ I 4 litigation (b 0 4) — A × a1 I3 A × a3
I3 þ I4
dependencies, and the rate of communication with the urban officials in the pre-construction phase. As step 1, based on the five most important identified CFSs, with respect to the project parties’ roles and responsibilities, five important CISs are determined as follows: 1. CIS (1). Changes that are formed due to the contractor’s errors resulting from contractor shortcomings. The contractor considers itself as the source of such errors. The owner gives a rework order to the contractor. 2. CIS (2). Changes that are formed due to the contractor’s errors resulting from owner and/or designer shortcomings. The contractor considers other parties (owner and/or designer) as the source of such errors. The owner gives a rework order to the contractor. 3. CIS (3). Changes that are formed due to not fulfilling the owner’s commitments. The contractor considers itself as the source of such shortcomings. The contractor requests time extension or additional payment. 4. CIS (4). Changes that are formed due to design errors resulting from the project delivery system. The contractor considers the project delivery system as the source of such errors. The contractor requests the design correction and time extension or additional payment.
Fig. 3. The event tree diagram of CIPs in the change implementation phase for the CIS (2) in the application example
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Fig. 4. The event tree diagram of CIPs in the change implementation phase for the CIS (5) in the application example
Table 4. Probability of the CIPs of the CIS (2) in the Application Example Type of path
Parametric value
Numerical value
Amount
COP
(1 − B) B × ð1 − CÞ × b1 B × C × b2 B × C × b3 B × C × b4
(1 − 0.812) 0.812 × ð1 − 0.43Þ × 1 0.812 × 0.43 × 0.69 0.812 × 0.43 × 0.248 0.812 × 0.43 × 0.0612
0.188 0.463 0.241 0.087 0.022
CNP
5. CIS (5). Changes that are formed due to design errors resulting from owner and/or designer shortcomings. The contractor considers the owner and/or designer as the source of such errors. The contractor requests the design correction and time extension or additional payment. As steps 2 and 3, to determine the events and paths of the change implementation process as well as the probability of each event, pictorial questionnaires are used to reach the task group’s expert consensus. As a result, the event trees of the change implementation nodes for each CIS are developed. Also, the probabilities of CIPs for each CIS are obtained based on the developed event trees and equations depicted in Table 3. For instance, the event tree diagram and the probabilities of CIPs for CIS (2) and CIS (5) are depicted in Figs. 3 and 4 and Tables 4 and 5, respectively.
Analysis and Results As step 4, the coefficients of impact factors of risk indices of CISs are calculated by the Eqs. (1) and (2) (Table 6). Thus, the following results may be concluded: 1. The highest probabilities of change and secondary change occurrences are related to the scenario of the owner’s rework order caused by the contractor’s errors due to characteristics of the owner and/or designer [CIS (2)], according to P1 and P2 values. 2. According to P1 values, the lowest probability of change occurrence is related to the scenario of the contractor’s change request caused by the owner’s lack of commitments due to characteristics of the contractor [CIS (3)], e.g., late payments by the owner due to the contractor’s mismanagement. The change implementation process may result in nonoccurrence of this change with a probability of 23%. 3. According to the P3 and P4 values, the highest probabilities of arising conflicts and litigation are related to the scenario of the contractor’s change request caused by design errors due to characteristics of the owner and/or designer [CIS (5)], e.g., design errors due to an inadequate feasibility study by an inexperienced owner and/or designer. The P4 value for this scenario is more than twice as great as other scenarios. 4. The worst scenario may be the contractor’s change request caused by design errors due to characteristics of the owner
Table 5. Probability of the CIPs of the CIS (5) in the Application Example Type of path COP
CNP
© ASCE
Parametric value
Numerical value
Amount
ð1 − AÞ × ð1 − BÞ ð1 − AÞ × B × ð1 − CÞ × b1 ð1 − AÞ × B × C × b2 ð1 − AÞ × B × C × b3 A × a2 × ð1 − B 0 Þ A × a2 × B 0 × ð1 − C 0 Þ × b 0 1 A × a2 × B 0 × C 0 × b 0 2 A × a2 × B 0 × C 0 × b 0 3 ð1 − AÞ × B × C × b4 A × a1 A × a2 × B 0 × C 0 × b 0 4 A × a3
ð1 − 0.43Þ × ð1 − 0.79Þ ð1 − 0.43Þ × 0.79 × ð1 − 0.50Þ × 1 ð1 − 0.43Þ × 0.79 × 0.50 × 0.80 ð1 − 0.43Þ × 0.79 × 0.50 × 0.138 0.43 × 0.64 × ð1 − 0.77Þ 0.43 × 0.64 × 0.77 × ð1 − 0.18Þ × 1 0.43 × 0.64 × 0.77 × 0.18 × 0.748 0.43 × 0.64 × 0.77 × 0.18 × 0.18 0.57 × 0.79 × 0.50 × 0.064 0.43 × 0.22 0.43 × 0.64 × 0.77 × 0.18 × 0.072 0.43 × 0.14
0.120 0.225 0.180 0.031 0.063 0.173 0.029 0.007 0.014 0.095 0.003 0.060
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Table 6. Coefficients of Impact Factors of the Risk Indices of the CISs in the Application Example CIS identifier
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CIS CIS CIS CIS CIS
(1) (2) (3) (4) (5)
P1
P2
P3
P4
0.97 0.98 0.77 0.92 0.83
0.63 0.70 — 0.59 0.61
0.54 0.35 0.39 0.39 0.69
0.03 0.02 0.04 0.04 0.08
and/or designer [CIS (5)], resulting in a high probability of arising conflicts and litigation. Consequently, the project design and characteristics of the designer (e.g., designer experience) are the most significant factors that may cause conflicts and litigation. The aforementioned results are confirmed by the contractor’s task group and the project documents. In general, to evaluate the change implementation phase, the developed framework should be implemented by a team consisting of all project parties at the initiating phase and throughout the project life cycle. The key to successful application of the framework is assembling a team that is capable of working together effectively. Moreover, having experts who are familiar with different aspects of the project throughout its life cycle is essential for effective application of the framework.
Conclusions This paper presented a framework to predict the change in construction projects by evaluating the change implementation process using the event tree analysis. This study focused on the implementation phase of change occurrence which follows the change formation phase. This research considered changes that had negative effects on one or more project objectives or project participant’s interests, resulting in change requests and/or change orders. In this research, based on general possible scenarios of conflicts/ nonconflicts, followed by scenarios of request/nonrequest, the change implementation phase was subdivided into change implementation paths. Thereby, the change implementation process was evaluated by the developed risk index using the event tree analysis. According to the method developed in this study, the occurrence of change and arising conflicts due to the change implementation process were predicted. Finally, a subway station construction project was considered as an example to illustrate the application of the method and demonstrate the method’s capabilities. In addition to demonstrating the method’s capabilities, the application example provided better means to follow the research’s line of reasoning. The results of this study may be utilized to predict the impacts of change orders on project objectives. In this way, the complexity of change should be dealt with by separating its impacts on various project objectives.
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