Modified Fuzzy Group Decision-Making Approach to Cost Overrun Risk Assessment of Power Plant Projects

Islam, Muhammad; Nepal, Madhav Prasad; Skitmore, Martin

doi:10.1061/(asce)co.1943-7862.0001593

Cited by 42 publications

(47 citation statements)

References 46 publications

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“…The mean score (MS) method was applied by Chan and Kumaraswamy (1996) to evaluate the relative importance of the causes of construction delays in Hong Kong. The MS method also has been used to analyse data by other researchers such as: Xu et al (2010); Islam et al (2018) and Xiang et al (2018). A similar method was employed for data collected analysis in this study.…”

Section: Methodsmentioning

confidence: 99%

Approaches to Risk Identification in Public–Private Partnership Projects: Malaysian Private Partners’ Overview

Sarvari

Valipour

Yahya

et al. 2019

Administrative Sciences

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The complexity of public–private partnership (PPP) projects ensures that risks could emerge and spread in unpredictable ways if they are not well identified and managed. The emergence of PPP projects has brought major changes in the construction industry, the most notable being in procurement methods influencing risk allocation to private parties. Thus, it is crucial to have an effective risk management for public and private partners to eliminate or minimise risks. Formulating an effective risk management system is a crucial challenge faced by both of parties in order to minimise or optimise risks. The aim of this study was to investigate the process of risk identification of private partners in Malaysian PPP projects. Data were collected throughout a 2-month period using a survey with a sample of nine Malaysian companies engaged in PPP projects, and the survey results were analysed using mean scores. The findings indicate that due to a lack of knowledge and experience of Malaysian private partners in the risk identification process, a comprehensive database for risk identification is highly necessary for the private sector. Another issue emerging from the findings is that it may be reasonable to use a combination of risk identiﬁcation tools for PPP projects with a high level of complexity. The findings of the present study can greatly assist public and private partners to select the most appropriate tools for risk identification at the early stages of PPP projects.

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Section: Methodsmentioning

confidence: 99%

Approaches to Risk Identification in Public–Private Partnership Projects: Malaysian Private Partners’ Overview

Sarvari

Valipour

Yahya

et al. 2019

Administrative Sciences

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“…Sharma and Goyal (2019) developed a fuzzy based model to estimate the risk magnitude of 55 important risk factors causing cost overrun in Indian construction projects. Models of cost overrun prediction based on the fuzzy logic were also developed by Knight and Fayek (2002), Dikmen et al (2007), Phama et al (2020), Amadi and Higham (2017), El-Maaty et al (2017), Kim et al (2018), Plebankiewicz (2018), Plebankiewicz and Wieczorek (2020), Islam et al (2019).…”

Section: Costmentioning

confidence: 99%

Modelling of Time, Cost and Risk of Construction With Using Fuzzy Logic

Plebankiewicz

Zima

Wieczorek

2021

Journal of Civil Engineering and Management

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The paper presents an overview of the literature from recent years devoted to planning the time, costs and risk of a construction investment using fuzzy logic. It also presents three own original models concerning the issue. The first model is used to build a fuzzy construction schedule taking into account fuzzy norms and the number of workers. The costing model uses fuzzy inference from CBR cases. The aim was to increase the accuracy and correctness of the cost calculation performed for the investor in the construction and investment process with a certain degree of vagueness of the available information about materials. In the last of the presented models, fuzzy sets were used to assess the effects of technological and construction (implementation) risk factors. The presented examples prove the usefulness of fuzzy logic in solving problems in construction, where we have incomplete and imprecise information.

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“…CCA process is one of the major causes of disputes in construction projects (El-adaway et al, 2018) and is a concern to construction professionals worldwide (Arcadis, 2018). The challenges associated with CCA performance include unresponsive acts by CCA staff ECAM 27,6 (Ssegawa, 2008), insufficiency and incompetency of staff (Puil and Weele, 2014;Surajbali, 2016), lack of adequate policies and procedures (Surajbali, 2016), inefficient monitoring of the contract (Surajbali, 2016), ineffective decision-making (Puil and Weele, 2014), frequent changes in design and specifications of major equipment (Islam et al, 2019), payments problems (Puil and Weele, 2014;Rendon, 2010;Ssegawa, 2008), communication issues (Barakat et al, 2018), timeline constraints (Joyce, 2014;Rendon, 2010), and corruptions (Joyce, 2014). The adverse impact of the poor CA is recognized through several terminated contracts (Yap, 2013), increased rate of claims, reduced liquidity in the markets and excessive use of variations (Okere, 2012).…”

Section: Review Of Literaturementioning

confidence: 99%

Critical assessment of construction contract administration using fuzzy structural equation modeling

Gündüz

Elsherbeny

2020

ECAM

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PurposeThis paper covers the development of a multidimensional contract administration performance model (CAPM) for construction projects. The proposed CAPM is intended to be used by the industry stakeholders to measure the construction contract administration (CCA) performance and identify the strengths and weaknesses of the CCA system for running or completed projects.Design/methodology/approachThe research design follows a sequential mixed methodology of qualitative and quantitative data collection and analysis. In the first phase, contract administration indicators were collected from relevant literature. In the second phase, an online questionnaire was prepared, and data were collected and analyzed using the crisp value of fuzzy membership function, and structural equation modeling (SEM). The fuzzy set was chosen for this study due to the presence of uncertainty and fuzziness associated with the importance of several key indicators affecting the CCA performance. Finally, SEM was used to test and analyze interrelationships among constructs of CCA performance.FindingsThe data collected from 336 construction professionals worldwide through an online survey was utilized to develop the fuzzy structural equation model. The goodness-of-fit and reliability tests validated the model. The study concluded a significant correlation between CCA performance, CCA operational indicators, and the process groups.Originality/valueThe contribution of this paper to the existing knowledge is the development of a fuzzy structural equation model that serves as a measurement tool for the contract administration performance. This is the first quantitative structural equation model to capture contract administration performance. The model consists of 93 Construction Contract Administration(CCA) performance indicators categorized into 11 project management process groups namely: project governance and start-up; team management; communication and relationship management; quality and acceptance management; performance monitoring and reporting management; document and record management; financial management; changes and control management; claims and dispute resolution management; contract risk management and contract closeout management.

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Modified Fuzzy Group Decision-Making Approach to Cost Overrun Risk Assessment of Power Plant Projects

Cited by 42 publications

References 46 publications

Approaches to Risk Identification in Public–Private Partnership Projects: Malaysian Private Partners’ Overview

Approaches to Risk Identification in Public–Private Partnership Projects: Malaysian Private Partners’ Overview

Modelling of Time, Cost and Risk of Construction With Using Fuzzy Logic

Critical assessment of construction contract administration using fuzzy structural equation modeling

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