CONSTRUCTION SCHEDULE RISK ANALYSIS - A HYBRID MACHINE LEARNING APPROACH

The UK commissions about 100 pound 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.

Automated summary

The UK spends billions of pounds on infrastructure construction works annually, but more than half of them are delayed, causing stakeholders' interests to be compromised. This research introduces a hybrid method to improve the accuracy of risk analysis and prediction of project delays, by combining machine intelligence with a large database of completed infrastructure construction projects in the UK. The results show a 54.4% increase in accuracy in predicting project delays compared to traditional methods.