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• W2861606319 abstract "Inspired by sensor coverage density and matching & preserving strategy, this paper proposes an Improved Artificial Bee Colony (IABC) algorithm which is designed to optimize bridge sensor placement. We use dynamic random coverage coding method to initialize colony to ensure the diversity and effectiveness. In addition, we randomly select the factors with lower trust value to search and evolve after food source being matched in order that the relatively high trust point factor is retained in the exploitation of food sources, which reduces the blindness of searching and improves the efficiency of convergence and the accuracy of the algorithm. According to the analysis of the modal data of the Ha-Qi long span railway bridge, the results show that IABC algorithm has faster convergence rate and better global search ability when solving the optimal placement problem of bridge sensor. The final analysis results also indicate that the IABC's solution accuracy is 76.45% higher than that of the ABC algorithm, and the solution stability is improved by 86.23%. The final sensor placement mostly covers the sensitive monitoring points of the bridge structure and, in this way, the IABC algorithm is suitable for solving the optimal placement problem of large bridge and other structures." @default.
• W2861606319 created "2018-07-19" @default.
• W2861606319 creator A5002854837 @default.
• W2861606319 creator A5063770598 @default.
• W2861606319 date "2018-07-11" @default.
• W2861606319 modified "2023-09-26" @default.
• W2861606319 title "Improved ABC Algorithm Optimizing the Bridge Sensor Placement" @default.
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• W2861606319 doi "https://doi.org/10.3390/s18072240" @default.
• W2861606319 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6068669" @default.
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• W2861606319 hasPublicationYear "2018" @default.
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