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A proposal for construction project risk assessment using fuzzy logic

Tah, J. H. M., Carr, V.·2000·Construction Management and Economics

Abstract

The construction industry is plagued by risk and often has suffered poor performance as a result. There are a number of risk management techniques available to help alleviate this, but usually these are based on operational research techniques developed in the 1960s, and for the most part have failed to meet the needs of project managers. In this paper, a hierarchical risk breakdown structure representation is used to develop a formal model for qualitative risk assessment. A common language for describing risks is presented which includes terms for quantifying likelihoods and impacts so as to achieve consistent quantification. The relationships between risk factors, risks and their consequences are represented on cause and effect diagrams. These diagrams and the concepts of fuzzy association and fuzzy composition are applied to identify relationships between risk sources and the consequences for project performance measures. A methodology for evaluating the risk exposure, considering the consequences in terms of time, cost, quality, and safety performance measures of a project based on fuzzy estimates of the risk components is presented.

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Metadata

Authors
Tah, J. H. M., & Carr, V.
Venue
Construction Management and Economics
Published
2000-06
Volume
18
Issue
4
Pages
491-500
ISSN
  • 0144-6193 (ISSN-L)
  • 1466-433X
OpenAlex
W2155467150
Cited by
374
References
0
FWCI
3.51
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A proposal for construction project risk assessment using fuzzy logic. https://app.cochat.ai/papers/10.1080/01446190050024905
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Tah, J. H. M., & Carr, V. (2000). A proposal for construction project risk assessment using fuzzy logic. Construction Management and Economics. https://doi.org/10.1080/01446190050024905

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.