Predicting the Likelihood of Cost Overruns in Tunnel Projects in Australia
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Keywords
Australian tunnel projects, Cost overruns, Probability Distribution Function (PDF), Cumulative Distribution Function (CDF), ANOVA, Descriptive statistics
Abstract
Cost overruns remain a persistent challenge in tunnel construction projects, yet their predictive assessment, particularly within the Australian context, has received limited scholarly attention. The purpose of this study is to develop a probabilistic, distribution-based framework for understanding and predicting the likelihood and magnitude of cost overruns in Australian tunnel projects, while explicitly examining the influence of project contract size on cost overrun risk. Using historical data from 27 completed tunnel projects, the study applies descriptive and inferential statistical techniques, goodness-of-fit testing, and probability modelling to characterise cost overrun behaviour. The analysis addresses two key questions: (1) What are the statistical properties and best-fit probability distribution of cost overruns? (2) How does contract size influence the likelihood and magnitude of overruns? The results indicate an average cost overrun of 46.60%, accompanied by pronounced variability and positive skewness (2.50). The Dagum distribution provides the best fit for the overall dataset, while the Generalised Extreme Value (GEV) and Gamma distributions best represent projects valued below and above AUD 1 billion, respectively. Although ANOVA results reveal no statistically significant difference in mean cost overruns between small and large projects (p-value = 0.89), smaller projects exhibit substantially greater variability, reflecting distinct underlying risk structures. Probability analysis further demonstrates a 77.34% likelihood of exceeding a 5% cost overrun threshold. By shifting the focus from mean-based assessment to distribution-driven risk modelling, this study contributes a quantitative, tunnel-specific approach for predicting cost overruns. The findings support more informed contingency allocation, probabilistic cost estimation, and risk-aware decision-making for infrastructure stakeholders involved in tunnel project planning and delivery in Australia.
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