Abstract
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.
Highlights
Around £100bn is spent in the UK each year on infrastructure investments (Infrastructure and Projects Authority 2016), making the delivery of infrastructure 70% of the total spending on the National Health Service
Critical Path Method (CPM) uses a series of tasks with defined dependency links to create a directed acyclic graph (DAG) network, which determines the earliest start and latest finish for each of the tasks in the network
We propose a novel hybrid data-driven method that combines the strengths of Support Vector Machine (SVM) and Monte Carlo Simulation (MCS) to train a robust predictive model to achieve this goal
Summary
Around £100bn is spent in the UK each year on infrastructure investments (Infrastructure and Projects Authority 2016), making the delivery of infrastructure 70% of the total spending on the National Health Service. From the construction of schools and hospitals to the delivery of road and rail infrastructure, research has shown that time and again projects tend to exceed their initial time estimates (Drury et al 2018). Projects are being delivered later than intended, with a regularity which suggests the problem is not being improved by the combined efforts of research to date (Salling and Leleur 2015; Vick and Brilakis 2018). The money wasted on poorly delivered infrastructure would equate to as much as £35bn per year in unrealised economic benefit (HM Treasury 2014). If this trend were extrapolated worldwide, the cost to the global economy would be $620bn each year, with around $1.1tn of potential lost (Oxford Economics 2017)
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