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CONSTRUCTION SCHEDULE RISK ANALYSIS - A HYBRID MACHINE LEARNING APPROACH

Published version
Peer-reviewed

Type

Article

Change log

Authors

Fitzsimmons, John Patrick 
Lu, Ruodan 
Hong, Ying 

Abstract

jats:pThe 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>

Description

Keywords

Construction Scheduling, Machine Learning, Risk Analysis

Journal Title

JOURNAL OF INFORMATION TECHNOLOGY IN CONSTRUCTION

Conference Name

Journal ISSN

1874-4753
1874-4753

Volume Title

Publisher

International Council for Research and Innovation in Building and Construction
Sponsorship
Innovate UK (104795)
EPSRC (EP/V056441/1)
Laing O'Rourke