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Quantifying and Prioritising Construction Delay Risks in Australia Using the Fuzzy Best–Worst Method and a Probability–Impact Matrix
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DOI：10.3390/buildings16163209.png)
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Abstract
En 中文
Construction delays remain a persistent challenge in Australian construction projects, contributing to cost escalation, disrupted work sequences, contractual claims, and reduced confidence in project delivery. Although delay causes have been widely investigated, existing studies often provide broad factor lists and prioritise risks using single-dimension or inconsistent scoring approaches. This limits guidance on which delay risks should receive priority attention when project teams face constrained time, cost, and management resources. This study addresses this limitation by quantifying and prioritising 22 validated delay risk factors in Australian construction projects. Probability of occurrence and schedule impact were evaluated as separate judgement dimensions before being integrated into an overall measure of risk criticality. Data were collected from 48 experienced Australian construction professionals. A dual-dimension Fuzzy Best–Worst Method was applied to derive separate ratio-scale weights for probability of occurrence and schedule impact, with dimension-specific consistency screening used to improve judgement reliability. The resulting weights were integrated using a probability–impact formulation and mapped onto a 5 × 5 Probability–Impact Matrix through quantile-based discretisation. A 10,000-iteration Monte Carlo robustness analysis was subsequently conducted to assess the stability of the resulting rankings under alternative expert-selection and weighting scenarios. The results indicate that the delay risks perceived by the participating professionals as having the highest combined probability and schedule impact are predominantly governance-, approval-, and coordination-related, particularly owner late decisions, change-approval delays, owner requirement changes, cost-estimation deficiencies, design-approval delays, and inadequate planning. The Monte Carlo analysis further indicated that the principal risk rankings remained relatively stable under variations in expert aggregation. Overall, the integrated FBWM–PIM framework provides a structured and practically interpretable approach for eliciting and prioritising expert perceptions of construction delay risk and translating them into an actionable classification tool for allocating limited risk management resources.
Keywords:
construction delay risk
Fuzzy Best–Worst Method
probability–impact matrix
risk prioritisation
Monte Carlo robustness analysis
Australian construction industry
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