Predicting the Time Impact and Identifying the Practical Responses to Claim Causes in Building Projects
Received: 3 June 2025 | Revised: 14 July 2025 and 27 July 2025 | Accepted: 1 August 2025 | Online: 15 August 2025
Corresponding author: Veerasak Likhitruangsilp
Abstract
Construction claims are inevitable and usually result in negative impacts on the project performance, and especially the project delays. This study aims to predict the time impacts and identify the practical responses to claim causes in building projects. Firstly, potential claim causes were identified through an extensive literature review and verified by a group of nine claim experts. Face-to-face interviews were conducted with these experts to establish the causal relationships among the identified claim causes. These relationships were then used to structure a Bayesian Network (BN) model for predicting the time impacts of each claim cause. The BN model comprised 13 claim causes of which only one claim cause (i.e. change orders) was independent. Subsequently, the historical data on the claim causes were collected from 112 actual building projects via a structured questionnaire survey. The data were then encoded and used as input to the model. A real case study project was used to demonstrate the predictive capability of the model. The results showed that the BN model is capable of effectively predicting the time impact levels of the claim causes. Next, twelve claim experts were invited to identify response strategies for each claim cause. It was found that the five response strategies commonly adopted for addressing claim causes are the: acceptance, avoidance, mitigation, contingency plan/reserve, and transfer. The present study also identified practical response methods for dealing with such claim causes. This study contributes a practical approach for forecasting the potential time impacts of the claim causes and formulating practical responses, thereby enhancing the claim management practices in building projects.
Keywords:
claim causes, Bayesian Network, time impacts, predict, response strategies, response methodsDownloads
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Copyright (c) 2025 Sang Quang Van, Veerasak Likhitruangsilp, Photios G. Ioannou
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