Predicting Change by Evaluating the Change Implementation Process in Construction Projects Using Event Tree Analysis

Heravi, Gholamreza; Charkhakan, Mohammad Hadi

doi:10.1061/(asce)me.1943-5479.0000325

Cited by 21 publications

(13 citation statements)

References 26 publications

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“…They proposed decision-making trial and evaluation laboratory to analyze change formation paths and estimate the relative importance of change formation components, the probability of occurrence and the traceability of the change formation scenarios (Heravi & Charkhakan, 2014). Later, the authors employed the event tree analysis to evaluate change implementation phase and predict the likelihood of occurrence of change and arising conflicts and litigation due to a change order (Heravi & Charkhakan, 2015). Based on empirical formulation, W. Ibbs and Chen (2015) proposed a multivariate change-predictive tool.…”

Section: Systems and Models For Proactive Change Managementmentioning

confidence: 99%

“…Motawa et al, 2006). Estimation of the likelihood of occurrence of a change event and the degree of change impacts on project parameters are prerequisites for proactive change management (Heravi & Charkhakan, 2015;Ibrahim A. Motawa et al, 2006). Therefore, this study intends to develop a framework to assess change probability and impact, which finally help to assess the risk of change.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid framework to assess the risk of change in construction projects using fuzzy fault tree and fuzzy event tree analysis

Abad

Naeni

2020

International Journal of Construction Management

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Change is known as a common risk in construction projects that have adverse impact on project performance. Thus, proactive change management in construction project plays a significant role in project successful completion by investigating potential changes and their probable consequences on project objectives. The focus of this study is to develop a hybrid framework to proactively manage change by evaluating change formation and implementation phases. In the proposed framework, first, the fuzzy fault tree is used to identify the root causes of change and estimate the change occurrence probability. Then, the fuzzy event tree analysis is employed to foresee the possible scenarios that may arise from a change and assess the probability of occurrence and also the degree of change impact on project objectives. Considering the probability and the degree of impacts, a risk index is employed to describe risk priority level. The proposed framework enables the project team to recognize and monitor change causes and also identify change effects.Eventually, it informs the project team to identify effective risk response strategies and deal with disruptive effects of change.

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Section: Systems and Models For Proactive Change Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hybrid framework to assess the risk of change in construction projects using fuzzy fault tree and fuzzy event tree analysis

Abad

Naeni

2020

International Journal of Construction Management

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“…Zhao et al [32] represented the information flow between change factors and changes using an activity-based dependency structure matrix and introduced Monte Carlo simulations to analyze the change probabilities of activities. Heravi and Charkhakan [33] subdivided the change implementation phase into change implementation paths and developed a risk index using event tree analysis. However, correlations between the tasks and dynamics of change risk were neglected in these studies.…”

Section: Literature Reviewmentioning

confidence: 99%

Risk Propagation Model and Simulation of Schedule Change in Construction Projects: A Complex Network Approach

et al. 2020

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Construction schedules play an important role in construction project management. However, during construction activities, risks may arise due to unexpected schedule changes, resulting in the ineffective delivery of projects. This study aims to reveal the law of schedule change risk propagation and to analyze the effects on the risk propagation through numerical simulations. First, construction projects are represented by activity-on-node (AON) networks. A model of risk propagation is then built based on a susceptible-infected (SI) model considering the effects of the nodal characteristics on the propagation process. Next, the model is tested on a real-world project to examine cascading failures with varying parameters. The experimental results demonstrate that the model is effective in identifying the activities most capable of affecting a project schedule and evaluating the impact of schedule change risk propagation. This study will provide a basis for enhancing the robustness of AON networks and controlling the propagation of schedule change risks.

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“…Change prediction is one of the change management responses [2,21,[29][30][31][32]. Isaac and Navon [33] proposed a change control tool to identify the implications of a change as soon as it is suggested.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid MCDM Model with Grey Numbers for the Construction Delay Change Response Problem

et al. 2019

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Stakeholders carry out construction projects under fast-changing conditions. The conditions can undermine the concept of a stable and prosperous construction plan without an appropriate permit and an active and targeted plan for environmental management. Therefore, the decision maker often faces many challenges of Multi-Criteria Decision-Making (MCDM) when it comes to solving the construction management proper response selection problem for planning delay changes when sustainable environment requirements are essential. Any addition, reduction, or modification of the original project plan is a change to the project and impacts the environment. Change occurrence is a probable issue while projects are implemented. One of the most complex tasks for the project manager is to work correctly and to find the most suitable decisions for the not precisely predetermined future expectations of a change. Therefore, the relevant criteria of values must reflect the uncertain properties of the problem model. Similar problems require fuzzy or grey MCDM methods. The paper introduces a new MCDM approach, which combines four different MCDM methods with grey numbers: the SWARA, TOPSIS-GM, Additive Ratio ASsessment with Grey Numbers (ARAS-G) techniques and Geometric Mean to cover uncertainty and improve the problem-solving model. An analysis of a case study has examined and highlighted four possible alternatives described by eight performance criteria (cost, duration, and some linguistic criteria). Stakeholders determined the best alternative, calculated the efficiency of choice, and practically implemented the best option.

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Predicting Change by Evaluating the Change Implementation Process in Construction Projects Using Event Tree Analysis

Cited by 21 publications

References 26 publications

A hybrid framework to assess the risk of change in construction projects using fuzzy fault tree and fuzzy event tree analysis

A hybrid framework to assess the risk of change in construction projects using fuzzy fault tree and fuzzy event tree analysis

Risk Propagation Model and Simulation of Schedule Change in Construction Projects: A Complex Network Approach

A New Hybrid MCDM Model with Grey Numbers for the Construction Delay Change Response Problem

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