Proceedings of the 2nd International Conference on Engineering Management and Information Science, EMIS 2023, February 24-26, 2023, Chengdu, China

Research Article

Optimization of construction safety inspection path from the perspective of risk

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  • @INPROCEEDINGS{10.4108/eai.24-2-2023.2330649,
        author={Naiwen  Li and Qibin  Yuan and Jian  Dai},
        title={Optimization of construction safety inspection path from the perspective of risk},
        proceedings={Proceedings of the 2nd International Conference on Engineering Management and Information Science, EMIS 2023, February 24-26, 2023, Chengdu, China},
        publisher={EAI},
        proceedings_a={EMIS},
        year={2023},
        month={6},
        keywords={safety patrol; risk probability value; path optimization; rp-aco algorithm; risk perspective},
        doi={10.4108/eai.24-2-2023.2330649}
    }
    
  • Naiwen Li
    Qibin Yuan
    Jian Dai
    Year: 2023
    Optimization of construction safety inspection path from the perspective of risk
    EMIS
    EAI
    DOI: 10.4108/eai.24-2-2023.2330649
Naiwen Li1,*, Qibin Yuan1, Jian Dai1
  • 1: Liaoning Technical University
*Contact email: 2013227081@qq.com

Abstract

Safety inspection work often determines the probability of accidents in construction, but also affects the production cost of work, to improve the quality and efficiency of construction safety inspection work, it is very important to propose a patrol inspection strategy. By comprehensively considering risk factors, a safety inspection path is formed. The objective function is constructed the RP-ACO (Risk Probability-ACO) algorithm is proposed, and based on the original ACO algorithm, the segmentation function is introduced to adjust the pheromone strength by changing the state transition rule Simulation results show that when only the risk probability value is considered, the average objective function of the RP-ACO algorithm is 20.0573, and the average number of convergences is 110.6 times, which has great advantages over the 20.0639 and 232.2 In the oliver30 test case, the minimum value of the RP-ACO algorithm is 423.9117, and the average value is 425.8249, which has obvious advantages over the original ACO algorithm, It can be seen that with the increase of emission reduction measures and the increase of spacing, the superiority of the RP-ACO algorithm is more than that of the original ACO algorithm, which has obvious advantages over the original ACO of 425.8201 and 429.0233.