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

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.

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

“…In Occupational accident field, 16 out of 61 papers dealt with data on accidents and injuries at work from 2014 to 2023. In the class "occupational accidents," the 89 out of 202 submissions covered the following topics: reporting of accidents [3,26,27,29,32,35,43,45,49,[59][60][61][62]75,76] and days away from work. On this topic, Yelda et al analysed textual narratives to predict injury outcome and days off work in a mining operation.…”

“…The model was applied to an urban metro project built in Wuhan, China, and was used to provide guidance on effective accident prevention strategies. Mostofi et al [45], explored the predictive ability of a multilayer GCN algorithm that learns the connection between construction accidents and project types, believing that richer information from existing safety and construction accident datasets by project type would provide better learning for the predictive model adopted. In addition, it would have supplied more information to predict the severity of accident consequences.…”

“…Construction safety standards and accident reduction are achieved through information and worker training, aiming to enhance the level of risks perception associated with the production process. However, the impact of traditional accident prevention strategies has been limited due to their reactive and regulatory nature [45,77]. A relevant aspect is the increased risks associated with the organisation and production goals of construction companies.…”

A Bibliometric Review on Safety Risk Assessment of Construction Based on CiteSpace Software and WoS Database

“…Deep neural networks (DNNs) were also used for construction cost estimation and were found to perform slightly better than other machine learning tools [133]. In addition, scholars also combined graph convolutional networks (GCNs) to account for information on the dependencies between construction accidents [134]. Kim et al (2022) developed a deep learning model for predicting sustainable construction safety accidents [135].…”

Determining the Short-term Susceptibility of Construction Workers to Occupational Accidents Using Stochastic Gradient Boosting

“…This makes the construction projects extremely dangerous by their nature [3]. As a result, a significant number of occupational accidents occur at construction sites [4][5][6]. Due to challenging environmental and dynamic conditions on construction sites and pressures on construction workers to achieve time, cost and quality objectives, occupational accidents are inevitable in the industry [7].…”