Citation

Guo B, Zhang L, Li Y. Safety Sci. 2019; 118: 389-396.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.ssci.2019.05.038

PMID

unavailable

Abstract

With the aggravation of China's aging population problem, the elevator renovation in old residential areas has developed rapidly in the field of people's production and life. However, at present, elevator installation in old residential quarters in China shows slow progress. Therefore, due to the problems of poor accuracy of traditional elevator safety evaluation methods, the disadvantages of human factors and the complicated safety management of elevators, this study proposes an elevator safety management evaluation method based on machine learning. Based on this, first of all, aiming at the contradiction that residents' willingness are difficult to be coordinated and unified, based on the theory of planned behavior and from the perspective of equity perception, social justice theory is introduced to construct and expand the TPB model and explore the influence mechanism of various potential variables. Then the precision of elevator safety evaluation method is not high and the complexity of elevator safety management. An elevator safety management evaluation method based on machine learning is proposed. Use safety checklist to collect elevator safety status data and conduct safety inspection. The importance and degree of each factor affecting elevator safety are considered by table analysis method and fuzzy mathematics theory. The machine learning principle is applied to the evaluation results, and the existing maintenance knowledge of the evaluation knowledge base is combined to provide users with comprehensive and effective Suggestions and measures for rectification. The practical application shows that this method can find out the potential vulnerabilities in the elevator system management and operation, and reduce the risks. Finally, based on the empirical results, this paper expounds the ways to improve residents' willingness to install elevators and puts forward specific and effective elevator safety measures, so as to provide decision-making reference for the smooth implementation of the installation of elevators in old residential areas in China.

Language: en

Keywords

Elevator renovation; Machine learning; Old residential quarters; Reconstruction safety; Residents' willingness; The theory of planned behavior