Development of project cost contingency estimation model using risk analysis and fuzzy expert system

Idrus, Arazi; Nuruddin, Muhd Fadhil; Rohman, Mohammad Arif

doi:10.1016/j.eswa.2010.07.061

Cited by 104 publications

(61 citation statements)

References 15 publications

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“…Fuzzy logic (FL) can be put to use in time-cost risk assessment, particularly when no historical data is available [13]- [14]. The method employs several heuristics, which are developed with e.g.…”

Section: Risk-based Estimation Of Manufacturing Order Costs With Artimentioning

confidence: 99%

Risk-based estimation of manufacturing order costs with artificial intelligence

Kłosowski

Gola

2016

Annals of Computer Science and Information Systems

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Abstract-The following paper discusses the development of a risk-based cost estimation model for completing non-standard manufacturing orders. The model in question is a hybrid of Monte Carlo Simulation (MCS), which constitutes the main module of the applied model. Vector of order risk probability, which is the input data for the MCS module, is highly difficult to assess and is burdened to a considerable degree with subjectivity, therefore it was resolved that it should be generated with the application of artificial intelligence. Depending on the accessibility of historical data, the model incorporates fuzzy logic or artificial neural networks methods. The presented model could provide support to managers responsible for cost estimation, and moreover, after slight modification also in setting deadlines for non-standard manufacturing orders.I.

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Section: Risk-based Estimation Of Manufacturing Order Costs With Artimentioning

confidence: 99%

Risk-based estimation of manufacturing order costs with artificial intelligence

Kłosowski

Gola

2016

Annals of Computer Science and Information Systems

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“…Fayek and Oduba [6] improved the accuracy of FIS-based CLP models for industrial pipe rigging and welding activities, by shifting the right, left, and both legs of the triangular and trapezoidal output MFs. Idrus et al [21] also followed the FIS tuning approach proposed by Fayek and Oduba [6] and showed an improvement in accuracy of predicting construction project cost contingency.…”

Section: Literature Review Of Context Adaptation Approachesmentioning

confidence: 99%

Context Adaptation of Fuzzy Inference System-Based Construction Labor Productivity Models

Tsehayae

Fayek

2018

Advances in Fuzzy Systems

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Construction labor productivity (CLP) is one of the most studied areas in the construction research field, and several contextspecific predictive models have been developed. However, CLP model development remains a challenge, as the complex impact of multiple subjective and objective influencing variables have to be examined in various project contexts while dealing with limited data availability. On the other hand, lack of a framework for adapting existing or original models from one context to other contexts limits the possibility of reusing existing models. Such challenges are addressed in this paper through the development of a context adaptation framework. The framework is used to transfer the knowledge represented in fuzzy inference (FIS) based CLP models from one context to another, by using linear and nonlinear evolutionary based transformation of the membership functions combined with sensitivity analysis of fuzzy operators and defuzzification methods. Using four context-specific CLP models developed for concreting activity under industrial, warehouse, high-rise, and institutional building project contexts, the framework was implemented, and the prediction capability of the adapted models was evaluated based on their prediction similarity with the original models. The results showed that linearly adapted CLP models for industrial and institutional contexts and nonlinearly adapted CLP models for warehouse and high-rise contexts provide a similar prediction capability with the original models. The proposed context adaptation framework and findings from this paper address the limitations in past context adaptation research by examining a practical context-sensitive application problem and further examining the role of fuzzy operators and defuzzification methods. The findings assist researchers and industry practitioners to take full advantage of existing FIS-based models in the study of new contexts, for which data availability might be limited.

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“…With exploring and discussing other decision-making techniques handling uncertainty, authors suggested fuzzy decision framework as a viable technique to model, assess, and manage global risk factors. Idrus et al (2011) developed a model to measure cost contingency of building and infrastructure projects. Model utilizes fuzzy expert system for risk assessment with the aim of accommodating subjective judgments and experiences of contractors.…”

Section: Literature Review On Previous Risk Identification and Assessmentioning

confidence: 99%

International Construction Projects

Klee

2014

International Construction Contract Law

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Development of project cost contingency estimation model using risk analysis and fuzzy expert system

Cited by 104 publications

References 15 publications

Risk-based estimation of manufacturing order costs with artificial intelligence

Risk-based estimation of manufacturing order costs with artificial intelligence

Context Adaptation of Fuzzy Inference System-Based Construction Labor Productivity Models

International Construction Projects

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