基于二元黏菌算法的结构健康监测数据特征子集选择

IF 3 Q2 ENGINEERING, CIVIL
Ramin Ghiasi, A. Malekjafarian
{"title":"基于二元黏菌算法的结构健康监测数据特征子集选择","authors":"Ramin Ghiasi, A. Malekjafarian","doi":"10.1080/24705314.2023.2230398","DOIUrl":null,"url":null,"abstract":"ABSTRACT Feature Selection (FS) is an important step in data-driven structural health monitoring approaches. In this paper, an Advanced version of the Binary Slime Mould Algorithm (ABSMA) is introduced for feature subset selection to improve the performance of structural damage classification techniques. Two operators of mutation and crossover are embedded to the algorithm, to overcome the stagnation situation involved in the Binary Slime Mould Algorithm (BSMA). The proposed ABSMA is then embedded in a new data-driven SHM framework which consists of three main steps. In the first step, structural time domain responses are collected and pre-processed to extract the statistical features. In the second step, the order of the extracted features is reduced using an optimization algorithm to find a minimal subset of salient features by removing irrelevant, and redundant data. Finally, the optimized feature vectors are used as inputs to Neural Network (NN) based classification models. Benchmark datasets of a timber bridge model and a three-story frame structure are employed to validate the proposed algorithm. The results show that the proposed ABSMA provides a better performance and convergence rate compared to other commonly used binary optimization algorithms.","PeriodicalId":43844,"journal":{"name":"Journal of Structural Integrity and Maintenance","volume":" ","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Feature subset selection in structural health monitoring data using an advanced binary slime mould algorithm\",\"authors\":\"Ramin Ghiasi, A. Malekjafarian\",\"doi\":\"10.1080/24705314.2023.2230398\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Feature Selection (FS) is an important step in data-driven structural health monitoring approaches. In this paper, an Advanced version of the Binary Slime Mould Algorithm (ABSMA) is introduced for feature subset selection to improve the performance of structural damage classification techniques. Two operators of mutation and crossover are embedded to the algorithm, to overcome the stagnation situation involved in the Binary Slime Mould Algorithm (BSMA). The proposed ABSMA is then embedded in a new data-driven SHM framework which consists of three main steps. In the first step, structural time domain responses are collected and pre-processed to extract the statistical features. In the second step, the order of the extracted features is reduced using an optimization algorithm to find a minimal subset of salient features by removing irrelevant, and redundant data. Finally, the optimized feature vectors are used as inputs to Neural Network (NN) based classification models. Benchmark datasets of a timber bridge model and a three-story frame structure are employed to validate the proposed algorithm. The results show that the proposed ABSMA provides a better performance and convergence rate compared to other commonly used binary optimization algorithms.\",\"PeriodicalId\":43844,\"journal\":{\"name\":\"Journal of Structural Integrity and Maintenance\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2023-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Structural Integrity and Maintenance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/24705314.2023.2230398\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Structural Integrity and Maintenance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/24705314.2023.2230398","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0

摘要

本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feature subset selection in structural health monitoring data using an advanced binary slime mould algorithm
ABSTRACT Feature Selection (FS) is an important step in data-driven structural health monitoring approaches. In this paper, an Advanced version of the Binary Slime Mould Algorithm (ABSMA) is introduced for feature subset selection to improve the performance of structural damage classification techniques. Two operators of mutation and crossover are embedded to the algorithm, to overcome the stagnation situation involved in the Binary Slime Mould Algorithm (BSMA). The proposed ABSMA is then embedded in a new data-driven SHM framework which consists of three main steps. In the first step, structural time domain responses are collected and pre-processed to extract the statistical features. In the second step, the order of the extracted features is reduced using an optimization algorithm to find a minimal subset of salient features by removing irrelevant, and redundant data. Finally, the optimized feature vectors are used as inputs to Neural Network (NN) based classification models. Benchmark datasets of a timber bridge model and a three-story frame structure are employed to validate the proposed algorithm. The results show that the proposed ABSMA provides a better performance and convergence rate compared to other commonly used binary optimization algorithms.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
3.90
自引率
9.50%
发文量
24
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信