Multi-Objective Optimization of Construction Worker Unsafe Behavior Inducement Prediction Model

Xiaolong Wang, Guangtai Zhang (), Erhu Li, Yan Wang and Kai Zhang ()
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Xiaolong Wang: Xinjiang University, School of Business
Guangtai Zhang: Xinjiang University, School of Civil Engineering and Architecture
Erhu Li: Xinjiang University, School of Business
Yan Wang: Xihua University, School of Economics and Trade
Kai Zhang: Xihua University, School of Mechanical Engineering

A chapter in Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023), 2024, pp 1710-1717 from Springer

Abstract: Abstract The predictive model for the causative factors of unsafe behaviors among construction workers is optimized with a multi-objective approach, based on the analysis of 27 factors and the use of four machine learning algorithms (CART, RF, AdaBoost, and GBDT) and genetic algorithms. Through a three-dimensional analysis of importance, correlation strength, and influence, five factors, namely risk awareness, education and training, hidden danger investigation and control, supervision level, and planning and design level, were identified to have the most significant impact on unsafe behaviors. This study aims to support the high-quality development of modern construction industry by studying the causative factors of unsafe behaviors among construction workers.

Keywords: machine learning; unsafe behaviors among construction workers; multi-objective optimization model; modern construction industry; high-quality development (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-256-9_174

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