Construction Safety Risk Model with Construction Accident Network: A Graph Convolutional Network Approach

Fatemeh Mostofi (), Vedat Toğan, Yunus Emre Ayözen and Onur Behzat Tokdemir
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Fatemeh Mostofi: Civil Engineering Department, Karadeniz Technical University, 61080 Trabzon, Türkiye
Vedat Toğan: Civil Engineering Department, Karadeniz Technical University, 61080 Trabzon, Türkiye
Yunus Emre Ayözen: Strategy Development Department, Ministry of Transport and Infrastructure, 06338 Ankara, Türkiye
Onur Behzat Tokdemir: Civil Engineering Department, Istanbul Technical University, 34469 Istanbul, Türkiye

Sustainability, 2022, vol. 14, issue 23, 1-18

Abstract: Construction risk assessment (RA) based on expert knowledge and experience incorporates uncertainties that reduce its accuracy and effectiveness in implementing countermeasures. To support the construction of RA procedures and enhance associated decision-making processes, machine learning (ML) approaches have recently been investigated in the literature. Most ML approaches have difficulty processing dependency information from real-life construction datasets. This study developed a novel RA model that incorporates a graph convolutional network (GCN) to account for dependency information between construction accidents. For this purpose, the construction accident dataset was restructured into an accident network, wherein the accidents were connected based on the shared project type. The GCN decodes the construction accident network information to predict each construction activity’s severity outcome, resulting in a prediction accuracy of 94%. Compared with the benchmark feedforward network (FFN) model, the GCN demonstrated a higher prediction accuracy and better generalization ability. The developed GCN severity predictor allows construction professionals to identify high-risk construction accident scenarios while considering dependency based on the shared project type. Ultimately, understanding the relational information between construction accidents increases the representativeness of RA severity predictors, enriches ML models’ comprehension, and results in a more reliable safety model for construction professionals.

Keywords: construction industry; site accident; construction safety management; graph convolutional network (GCN); risk assessment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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