{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:19:40Z","timestamp":1775913580887,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,8]],"date-time":"2022-02-08T00:00:00Z","timestamp":1644278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Identifying structural damage is an essential task for ensuring the safety and functionality of civil, mechanical, and aerospace structures. In this study, the structural damage identification scheme is formulated as an optimization problem, and a new meta-heuristic optimization algorithm, called visible particle series search (VPSS), is proposed to tackle that. The proposed VPSS algorithm is inspired by the visibility graph technique, which is a technique used basically to convert a time series into a graph network. In the proposed VPSS algorithm, the population of candidate solutions is regarded as a particle series and is further mapped into a visibility graph network to obtain visible particles. The information captured from the visible particles is then utilized by the algorithm to seek the optimum solution over the search space. The general performance of the proposed VPSS algorithm is first verified on a set of mathematical benchmark functions, and, afterward, its ability to identify structural damage is assessed by conducting various numerical simulations. The results demonstrate the high accuracy, reliability, and computational efficiency of the VPSS algorithm for identifying the location and the extent of damage in structures.<\/jats:p>","DOI":"10.3390\/s22031275","type":"journal-article","created":{"date-parts":[[2022,2,8]],"date-time":"2022-02-08T23:42:20Z","timestamp":1644363740000},"page":"1275","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Visible Particle Series Search Algorithm and Its Application in Structural Damage Identification"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9110-0677","authenticated-orcid":false,"given":"Pooya","family":"Mohebian","sequence":"first","affiliation":[{"name":"Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran 196976-4499, Iran"}]},{"given":"Seyed Bahram Beheshti","family":"Aval","sequence":"additional","affiliation":[{"name":"Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran 196976-4499, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2793-5194","authenticated-orcid":false,"given":"Mohammad","family":"Noori","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93405, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3812-0385","authenticated-orcid":false,"given":"Naiwei","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Changsha University of Science and Technology, Changsha 410076, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3618-1187","authenticated-orcid":false,"given":"Wael A.","family":"Altabey","sequence":"additional","affiliation":[{"name":"International Institute for Urban Systems Engineering, Southeast University, Nanjing 210096, China"},{"name":"Department of Mechanical Engineering, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101244","DOI":"10.1016\/j.jobe.2020.101244","article-title":"An effective deep feedforward neural networks (DFNN) method for damage identification of truss structures using noisy incomplete modal data","volume":"30","author":"Truong","year":"2020","journal-title":"J. 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