ITcon Vol. 27, pg. 70-93,

Construction schedule risk analysis – a hybrid machine learning approach

submitted:February 2021
revised:November 2021
published:January 2022
editor(s):Amor R
authors:John Patrick Fitzsimmons, Principal Planner
Planning and Project Controls, Laing O’Rourke

Ruodan Lu, Senior Member (corresponding author)
Darwin College, University of Cambridge

Ying Hong, Research Associate
Department of Engineering, University of Cambridge

Ioannis Brilakis, Laing O’Rourke Reader
Department of Engineering, University of Cambridge
summary: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.
keywords:Construction Scheduling, Machine Learning, Risk Analysis
full text: (PDF file, 1.351 MB)
citation:Fitzsimmons J P, Lu R, Hong Y, Brilakis I (2022). Construction schedule risk analysis – a hybrid machine learning approach, ITcon Vol. 27, pg. 70-93,