Harnessing Machine Learning for Classifying Economic Damage Trends in Transportation Infrastructure Projects

Junseo Bae, Sang-Guk Yum and Ji-Myong Kim
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Junseo Bae: School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK
Sang-Guk Yum: Department of Civil Engineering, Gangneung-Wonju National University, Gangneung 25457, Korea
Ji-Myong Kim: Department of Architectural Engineering, Mokpo National University, Mokpo 58554, Korea

Sustainability, 2021, vol. 13, issue 11, 1-12

Abstract: Given the highly visible nature, transportation infrastructure construction projects are often exposed to numerous unexpected events, compared to other types of construction projects. Despite the importance of predicting financial losses caused by risk, it is still difficult to determine which risk factors are generally critical and when these risks tend to occur, without benchmarkable references. Most of existing methods are prediction-focused, project type-specific, while ignoring the timing aspect of risk. This study filled these knowledge gaps by developing a neural network-driven machine-learning classification model that can categorize causes of financial losses depending on insurance claim payout proportions and risk occurrence timing, drawing on 625 transportation infrastructure construction projects including bridges, roads, and tunnels. The developed network model showed acceptable classification accuracy of 74.1%, 69.4%, and 71.8% in training, cross-validation, and test sets, respectively. This study is the first of its kind by providing benchmarkable classification references of economic damage trends in transportation infrastructure projects. The proposed holistic approach will help construction practitioners consider the uncertainty of project management and the potential impact of natural hazards proactively, with the risk occurrence timing trends. This study will also assist insurance companies with developing sustainable financial management plans for transportation infrastructure projects.

Keywords: transportation infrastructure; economic damage; financial loss; insurance claim payout; risk occurrence timing; machine learning; neural network; data classification; risk assessment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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