An Agglomerative Clustering Combined with an Unsupervised Feature Selection Approach for Structural Health Monitoring
Structural health monitoring (SHM) is critical for ensuring the safety and longevity of structures, yet existing methodologies often face challenges such as high data dimensionality, lack of interpretability, and reliance on extensive label datasets. Current research in SHM has primarily focused on...
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MDPI AG
2025-01-01
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| author | Tales Boratto Heder Soares Bernardino Alex Borges Vieira Tiago Silveira Gontijo Matteo Bodini Dmitriy A. Martyushev Camila Martins Saporetti Alexandre Cury Flávio Barbosa Leonardo Goliatt |
| author_facet | Tales Boratto Heder Soares Bernardino Alex Borges Vieira Tiago Silveira Gontijo Matteo Bodini Dmitriy A. Martyushev Camila Martins Saporetti Alexandre Cury Flávio Barbosa Leonardo Goliatt |
| author_sort | Tales Boratto |
| collection | DOAJ |
| description | Structural health monitoring (SHM) is critical for ensuring the safety and longevity of structures, yet existing methodologies often face challenges such as high data dimensionality, lack of interpretability, and reliance on extensive label datasets. Current research in SHM has primarily focused on supervised approaches, which require significant manual effort for data labeling and are less adaptable to new environments. Additionally, the large volume of data generated from dynamic structural monitoring campaigns often includes irrelevant or redundant features, further complicating the analysis and reducing computational efficiency. This study addresses these issues by introducing an unsupervised learning approach for SHM, employing an agglomerative clustering model alongside an unsupervised feature selection technique utilizing box-plot statistics. The proposed method is assessed through raw acceleration signals obtained from four dynamic structural monitoring campaigns, including 44 features with temporal, statistical, and spectral information. In addition, these features are also evaluated in terms of their relevance, and the most important ones are selected for a new execution of the computational procedure. The proposed feature selection not only reduces data dimensionality but also enhances model interpretability, improving the clustering performance in terms of homogeneity, completeness, V-measure, and adjusted Rand score. The results obtained for the four analyzed cases provide clear insights into the patterns of behavior and structural anomalies. |
| format | Article |
| id | doaj-art-515786655eef4b00a733021ef673d794 |
| institution | DOAJ |
| issn | 2412-3811 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj-art-515786655eef4b00a733021ef673d7942025-08-20T03:12:15ZengMDPI AGInfrastructures2412-38112025-01-011023210.3390/infrastructures10020032An Agglomerative Clustering Combined with an Unsupervised Feature Selection Approach for Structural Health MonitoringTales Boratto0Heder Soares Bernardino1Alex Borges Vieira2Tiago Silveira Gontijo3Matteo Bodini4Dmitriy A. Martyushev5Camila Martins Saporetti6Alexandre Cury7Flávio Barbosa8Leonardo Goliatt9Graduate Program in Computational Modeling, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Minas Gerais, BrazilDepartment of Computer Science, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Minas Gerais, BrazilDepartment of Computer Science, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Minas Gerais, BrazilCampus Centro Oeste, Federal University of São João del-Rei, Divinópolis 355901-296, Minas Gerais, BrazilDipartimento di Economia, Management e Metodi Quantitativi, Università degli Studi di Milano, Via Conservatorio 7, 20122 Milano, ItalyDepartment of Oil and Gas Technologies, Perm National Research Polytechnic University, 614990 Perm, RussiaDepartment of Computational Modeling, Polytechnic Institute, Rio de Janeiro State University, Nova Friburgo 22000-900, Rio de Janeiro, BrazilDepartment of Computational and Applied Mechanics, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Minas Gerais, BrazilDepartment of Computational and Applied Mechanics, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Minas Gerais, BrazilDepartment of Computational and Applied Mechanics, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Minas Gerais, BrazilStructural health monitoring (SHM) is critical for ensuring the safety and longevity of structures, yet existing methodologies often face challenges such as high data dimensionality, lack of interpretability, and reliance on extensive label datasets. Current research in SHM has primarily focused on supervised approaches, which require significant manual effort for data labeling and are less adaptable to new environments. Additionally, the large volume of data generated from dynamic structural monitoring campaigns often includes irrelevant or redundant features, further complicating the analysis and reducing computational efficiency. This study addresses these issues by introducing an unsupervised learning approach for SHM, employing an agglomerative clustering model alongside an unsupervised feature selection technique utilizing box-plot statistics. The proposed method is assessed through raw acceleration signals obtained from four dynamic structural monitoring campaigns, including 44 features with temporal, statistical, and spectral information. In addition, these features are also evaluated in terms of their relevance, and the most important ones are selected for a new execution of the computational procedure. The proposed feature selection not only reduces data dimensionality but also enhances model interpretability, improving the clustering performance in terms of homogeneity, completeness, V-measure, and adjusted Rand score. The results obtained for the four analyzed cases provide clear insights into the patterns of behavior and structural anomalies.https://www.mdpi.com/2412-3811/10/2/32unsupervised learninganomaly detectionstructural health monitoringvibrationfeature selection |
| spellingShingle | Tales Boratto Heder Soares Bernardino Alex Borges Vieira Tiago Silveira Gontijo Matteo Bodini Dmitriy A. Martyushev Camila Martins Saporetti Alexandre Cury Flávio Barbosa Leonardo Goliatt An Agglomerative Clustering Combined with an Unsupervised Feature Selection Approach for Structural Health Monitoring Infrastructures unsupervised learning anomaly detection structural health monitoring vibration feature selection |
| title | An Agglomerative Clustering Combined with an Unsupervised Feature Selection Approach for Structural Health Monitoring |
| title_full | An Agglomerative Clustering Combined with an Unsupervised Feature Selection Approach for Structural Health Monitoring |
| title_fullStr | An Agglomerative Clustering Combined with an Unsupervised Feature Selection Approach for Structural Health Monitoring |
| title_full_unstemmed | An Agglomerative Clustering Combined with an Unsupervised Feature Selection Approach for Structural Health Monitoring |
| title_short | An Agglomerative Clustering Combined with an Unsupervised Feature Selection Approach for Structural Health Monitoring |
| title_sort | agglomerative clustering combined with an unsupervised feature selection approach for structural health monitoring |
| topic | unsupervised learning anomaly detection structural health monitoring vibration feature selection |
| url | https://www.mdpi.com/2412-3811/10/2/32 |
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