Modeling the Optimal Maintenance Strategy for Bridge Elements Based on Agent Sequential Decision Making

• Published in: Applied sciences 2024-01, Vol.14 (1), p.14
• Main Authors: Xin, Gongfeng, Liang, Zhiqiang, Hu, Yerong, Long, Guanxu, Zhang, Yang, Liang, Peng
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Artificial intelligence; bridge engineering; Bridges; Decision making; Defects; dynamic programming algorithm; Genetic algorithms; Laws, regulations and rules; Maintenance costs; maintenance decision analysis; Maintenance management; Management decisions; Optimization; Planning; semi-Markov; sequential decision process
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14010014

In addressing the issues of low efficiency in bridge maintenance decision making, the inaccurate estimation of maintenance costs, and the lack of specificity in decision making regarding maintenance measures for specific defects, this study utilizes data from regular bridge inspections. It employs a two-parameter Weibull distribution to model the duration variables of the states of bridge elements, thereby enabling the prediction of the duration time of bridge elements in various states. Referring to existing bridge maintenance and repair regulations, the estimation process of maintenance costs is streamlined. Taking into account the specific types and development state of bridge defects, as well as considering the adequacy of maintenance and the restorative effects of maintenance measures, an intelligent agent sequential decision-making model for bridge maintenance decisions is established. The model utilizes dynamic programming algorithms to determine the optimal maintenance and repair measures for elements in various states. The decision results are precise, all the way down to the specific bridge elements and maintenance measures for individual defects. In using the case of the regular inspection data of 222 bridges along a highway loop, this study further validates the effectiveness of the proposed research methods. By constructing an intelligent agent sequential decision-making model for bridge element maintenance, the optimal maintenance measures for 21 bridge elements in different states are obtained, thereby significantly enhancing the efficiency of actual bridge maintenance and the practicality of decision results.