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A proposal for construction project risk assessment using fuzzy logic
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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Citations
Cited by
Towards a better modelling and assessment of construction risk: Insights from a literature review
Taroun, A. (2014) · International Journal of Project Management · doi:10.1016/j.ijproman.2013.03.004 · Cited by 387
Eybpoosh, M., Dikmen, I., & Talat Birgonul, M. (2011) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0000382 · Cited by 217
Metadata
- Authors
- Tah, J. H. M., & Carr, V.
- Venue
- Construction Management and Economics
- Publisher
- Informa UK Limited
- 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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- 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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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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