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Optimization of construction safety resource allocation based on evolutionary game and genetic algorithm.

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This study uses evolutionary game theory and genetic algorithms to optimize safety resource allocation in construction. The findings show genetic algorithms offer superior, data-driven values for enhancing project safety performance.

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Area of Science:

  • Construction Management
  • Game Theory
  • Operations Research

Background:

  • Effective safety resource allocation is crucial for construction project performance.
  • Mental health's growing importance in construction necessitates advanced analytical approaches.
  • Leader-Member Exchange Ambivalence influences safety dynamics within construction teams.

Purpose of the Study:

  • To develop an analytical model integrating evolutionary game theory and genetic algorithms.
  • To quantify factors impacting construction safety performance and identify constraints.
  • To compare the efficacy of different safety resource allocation strategies.

Main Methods:

  • Analysis of superior-subordinate relationships within construction sites.
  • Development of a game model and payoff matrix.
  • Integration of genetic algorithms with replicator dynamics for optimization.
  • Simulation of various construction scenarios by adjusting key factors.

Main Results:

  • Genetic algorithms provide more accurate safety resource allocation values than manual or preset methods.
  • The model effectively quantifies the influence of safety risk, psychological returns, and moral identity.
  • Simulation results offer precise reference values for optimizing safety performance.

Conclusions:

  • The combined evolutionary game theory and genetic algorithm model enhances safety resource allocation accuracy.
  • This approach offers a data-driven method for improving construction safety performance.
  • The model provides valuable insights for managing safety in dynamic construction environments.