Optimal placement method of multi-objective and multi-type sensors for courtyard-style timber historical buildings based on Meta-genetic algorithm

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Chengwen Zhang, Qing Chun, J. Leng, Yijie Lin, Yuchong Qian, Guang-qiang Cao, Qingchong Dong
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引用次数: 1

Abstract

Optimal sensor placement for timber architecture heritage poses a significant challenge due to the unique structural types and complex monitoring purposes. In this study, a three-stage method is proposed, taking a courtyard-style heritage, built 133 years ago, as an example. First, a finite element model that accounted for the parameter randomness and initial damage was constructed using a genetic algorithm (GA) and experimental results. Second, a new weighted fitness function of logarithmic type was developed for multi-type sensors and multi-objective monitoring. Third, a novel genetic algorithm, Meta-GA, was proposed, introducing competition group mechanisms and gene libraries to improve optimal capability while maintaining computational efficiency. The Meta-GA is then compared to the other two optimization modes using seven indexes. Finally, damage detection capability was tested for the proposed three schemes at noise levels of 0%, 5%, and 10%. The results reveal that the proposed three-stage method with Meta-GA can provide the best solution.
基于元遗传算法的院落木结构历史建筑多目标多类型传感器优化布置方法
由于独特的结构类型和复杂的监测目的,木结构遗产的最佳传感器放置提出了重大挑战。本研究以133年前的合院式遗产为例,提出了三阶段法。首先,利用遗传算法和实验结果建立了考虑参数随机性和初始损伤的有限元模型;其次,针对多类型传感器和多目标监测,提出了一种新的对数型加权适应度函数;第三,提出了一种新的遗传算法Meta-GA,在保持计算效率的同时,引入竞争群体机制和基因库来提高最优能力。然后使用7个指标将Meta-GA与其他两种优化模式进行比较。最后,在0%、5%和10%的噪声水平下测试了所提出的三种方案的损伤检测能力。结果表明,本文提出的三阶段元遗传算法能够提供最佳解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
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