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

In: Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023)

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xiaolong Wang & Guangtai Zhang & Erhu Li & Yan Wang & Kai Zhang, 2024. "Multi-Objective Optimization of Construction Worker Unsafe Behavior Inducement Prediction Model," Advances in Economics, Business and Management Research, in: Suhaiza Hanim Binti Dato Mohamad Zailani & Kosga Yagapparaj & Norhayati Zakuan (ed.), Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023), pages 1710-1717, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-256-9_174
    DOI: 10.2991/978-94-6463-256-9_174
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