- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | ZHANG Chengwen, CHUN Qing, HUA Quanjun, LIN Yijie, CUI Zhekui. Optimal Sensor Placement and Evaluation Method of Stone Arch Bridge Based on Meta-Genetic Algorithm[J].Journal of Southwest Jiaotong University.doi:10.3969/j.issn.0258-2724.20240012
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To develop an optimal sensor placement method for ancient stone arch bridges, by taking the Beijing Lugou Bridge, a national key cultural relics protection unit, as an example, a sensor optimization model considering initial damage and random material parameters was established. A fitness function design and solution method considering complex monitoring targets was proposed, along with a meta-genetic algorithm based on the concept of meta-learning for solving the sensor placement optimization problem. The proposed method was compared with two optimization methods based on conventional genetic algorithms, achieving optimal sensor placement for ancient stone arch bridges. The results show that the proposed method offers better parameter identification capability, damage sensitivity, and information redundancy level. When the noise level is within 5%, the sensor placement scheme given by the meta-genetic algorithm can successfully detect the damage, while the other two methods achieve only a 60.0% success rate. When the noise level reaches 10%, the meta-genetic algorithm can detect 60.0% of the damage, while the other two methods fail to detect damage effectively.
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