Deterministic and probabilistic risk management approaches in construction projects: A systematic literature review and comparative analysis
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Authors
Khodabakhshian, A.
Puolitaival, Taija
Kestle, Linda
Puolitaival, Taija
Kestle, Linda
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Date
2023-05-11
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Type
Journal Article
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
construction industry
risk assessment
construction management (CM)
artificial intelligence (AI)
algorithms
risk assessment
construction management (CM)
artificial intelligence (AI)
algorithms
ANZSRC Field of Research Code (2020)
Citation
Khodabakhshian, A., Puolitaival, T., & Kestle, L. (2023). Deterministic and probabilistic risk management approaches in construction projects: A systematic literature review and comparative analysis Construction Projects: A Systematic Literature Review and Comparative Analysis. Buildings Journal, vol 13 (5), 1312. https://doi.org/10.3390/buildings13051312
Abstract
Risks and uncertainties are inevitable in construction projects and can drastically change the expected outcome, negatively impacting the project’s success. However, risk management (RM) is still conducted in a manual, largely ineffective, and experience-based fashion, hindering automa-tion and knowledge transfer in projects. The construction industry is benefitting from the recent Industry 4.0 revolution and the advancements in data science branches, such as artificial intelligence (AI), for the digitalization and optimization of processes. Data-driven methods, e.g., AI and machine learning algorithms, Bayesian inference, and fuzzy logic, are being widely explored as possible so-lutions to RM domain shortcomings. These methods use deterministic or probabilistic risk reason-ing approaches, the first of which proposes a fixed predicted value, and the latter embraces the notion of uncertainty, causal dependencies, and inferences between variables affecting projects’ risk in the predicted value. This research used a systematic literature review method with the objective of investigating and comparatively analyzing the main deterministic and probabilistic methods ap-plied to construction RM in respect of scope, primary applications, advantages, disadvantages, lim-itations, and proven accuracy. The findings established recommendations for optimum AI-based frameworks for different management levels—enterprise, project, and operational—for large or small data set.
[This article belongs to the Special Issue Selected Papers from the 45th Australasian Universities Building Education Association (AUBEA 2022) expanded into a journal article which is available online: Website: https://www.mdpi.com/2075-5309/13/5/1312]
Publisher
MDPI (Multidisciplinary Digital Publishing Institute)
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DOI
https://doi.org/10.3390/buildings13051312
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Attribution 4.0 International