Construction schedule risk analysis – a hybrid machine learning approach
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Publication Date
2022-01-13Journal Title
Journal of Information Technology in Construction
ISSN
1400-6529
Publisher
International Council for Research and Innovation in Building and Construction
Type
Article
This Version
VoR
Metadata
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Fitzsimmons, J. P., Lu, R., Hong, Y., & Brilakis, I. (2022). Construction schedule risk analysis – a hybrid machine learning approach. Journal of Information Technology in Construction https://doi.org/10.36680/j.itcon.2022.004
Abstract
<jats:p>The UK commissions about £100 billion in infrastructure construction works every year. More than 50% of them finish later than planned, causing damage to the interests of stakeholders. The estimation of time-risk on construction projects is currently done subjectively, largely by experience despite there are many existing techniques available to analyse risk on the construction schedules. Unlike conventional methods that tend to depend on the accurate estimation of risk boundaries for each task, this research aims to proposes a hybrid method to assist planners in undertaking risk analysis using baseline schedules with improved accuracy. The proposed method is endowed with machine intelligence and is trained using a database of 293,263 tasks from a diverse sample of 302 completed infrastructure construction projects in the UK. It combines a Gaussian Mixture Modelling-based Empirical Bayesian Network and a Support Vector Machine followed by performing a Monte Carlo risk simulation. The former is used to investigate the uncertainty, correlated risk factors, and predict task duration deviations while the latter is used to return a time-risk simulated prediction. This study randomly selected 10 projects as case studies followed by comparing their results of the proposed hybrid method with Monte Carlo Simulation. Results indicated 54.4% more accurate prediction on project delays.</jats:p>
Sponsorship
Laing O'Rourke
Funder references
Innovate UK (104795)
Identifiers
External DOI: https://doi.org/10.36680/j.itcon.2022.004
This record's URL: https://www.repository.cam.ac.uk/handle/1810/331275
Rights
Creative Commons Attribution 4.0 International
Licence URL: http://www.rioxx.net/licenses/CC BY 4.0
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