A novel method for detecting structural damage based on data-driven and similarity-based techniques under environmental and operational changes
The applications of time series modeling and statistical similarity methods to structural health monitoring (SHM) provide promising and capable approaches to structural damage detection. The main aim of this article is to propose an efficient univariate similarity method named as Kullback similarity...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
K. N. Toosi University of Technology
2021-12-01
|
| Series: | Numerical Methods in Civil Engineering |
| Subjects: | |
| Online Access: | https://nmce.kntu.ac.ir/article_160570_56ea245d3872615d577835089e4c9c2c.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846114762861051904 |
|---|---|
| author | Masoud Haghani Chegeni Mohammad Kazem Sharbatdar Reza Mahjoub Mehdi Raftari |
| author_facet | Masoud Haghani Chegeni Mohammad Kazem Sharbatdar Reza Mahjoub Mehdi Raftari |
| author_sort | Masoud Haghani Chegeni |
| collection | DOAJ |
| description | The applications of time series modeling and statistical similarity methods to structural health monitoring (SHM) provide promising and capable approaches to structural damage detection. The main aim of this article is to propose an efficient univariate similarity method named as Kullback similarity (KS) for identifying the location of damage and estimating the level of damage severity. An improved feature extraction technique based on autoregressive (AR) model is presented to extract independent residuals of the AR model as damage-sensitive features. This technique emphasizes to choose a sufficient order such that the model residuals be independent. The proposed univariate similarity approach is a new application of the well-known KS method that attempts to measure a difference between two randomly distributed variables. The major contribution of the proposed KS method is that it only requires one measurement of undamaged and damaged conditions to compute the similarity between them. For the process of damage localization, the sensor location associated with the largest KS quantity is identified as the damaged area. In the damage level estimation, it is necessary to compare at least two different damaged conditions and find the maximum KS value in these conditions as the highest level of damage severity. The performance and capability of the improved and proposed methods is successfully verified by an experimental laboratory frame belonging to the Los Alamos National Laboratory. Results show that the methods are powerful and reliable tools for identifying the location of damage and estimating the level of damage severity. |
| format | Article |
| id | doaj-art-cbb127894acf428e8e2f771f5858b6be |
| institution | Kabale University |
| issn | 2345-4296 2783-3941 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | K. N. Toosi University of Technology |
| record_format | Article |
| series | Numerical Methods in Civil Engineering |
| spelling | doaj-art-cbb127894acf428e8e2f771f5858b6be2024-12-20T08:31:40ZengK. N. Toosi University of TechnologyNumerical Methods in Civil Engineering2345-42962783-39412021-12-0164162810.52547/nmce.6.4.16160570A novel method for detecting structural damage based on data-driven and similarity-based techniques under environmental and operational changesMasoud Haghani Chegeni0Mohammad Kazem Sharbatdar1Reza Mahjoub2Mehdi Raftari3Ph.D. Candidate, Department of Civil Engineering, Khorramabad Branch, Islamic Azad University, Khorramabad, IranProfessor, Faculty of Civil Engineering, Semnan University, Semnan, IranAssistant Professor, Department of Civil Engineering, Khorramabad Branch, Islamic Azad University, Khorramabad, IranAssistant Professor, Department of Civil Engineering, Khorramabad Branch, Islamic Azad University, Khorramabad, IranThe applications of time series modeling and statistical similarity methods to structural health monitoring (SHM) provide promising and capable approaches to structural damage detection. The main aim of this article is to propose an efficient univariate similarity method named as Kullback similarity (KS) for identifying the location of damage and estimating the level of damage severity. An improved feature extraction technique based on autoregressive (AR) model is presented to extract independent residuals of the AR model as damage-sensitive features. This technique emphasizes to choose a sufficient order such that the model residuals be independent. The proposed univariate similarity approach is a new application of the well-known KS method that attempts to measure a difference between two randomly distributed variables. The major contribution of the proposed KS method is that it only requires one measurement of undamaged and damaged conditions to compute the similarity between them. For the process of damage localization, the sensor location associated with the largest KS quantity is identified as the damaged area. In the damage level estimation, it is necessary to compare at least two different damaged conditions and find the maximum KS value in these conditions as the highest level of damage severity. The performance and capability of the improved and proposed methods is successfully verified by an experimental laboratory frame belonging to the Los Alamos National Laboratory. Results show that the methods are powerful and reliable tools for identifying the location of damage and estimating the level of damage severity.https://nmce.kntu.ac.ir/article_160570_56ea245d3872615d577835089e4c9c2c.pdfstructural damage detectiondamage localizationdamage level estimationautoregressive modelindependent residualskullback similarity |
| spellingShingle | Masoud Haghani Chegeni Mohammad Kazem Sharbatdar Reza Mahjoub Mehdi Raftari A novel method for detecting structural damage based on data-driven and similarity-based techniques under environmental and operational changes Numerical Methods in Civil Engineering structural damage detection damage localization damage level estimation autoregressive model independent residuals kullback similarity |
| title | A novel method for detecting structural damage based on data-driven and similarity-based techniques under environmental and operational changes |
| title_full | A novel method for detecting structural damage based on data-driven and similarity-based techniques under environmental and operational changes |
| title_fullStr | A novel method for detecting structural damage based on data-driven and similarity-based techniques under environmental and operational changes |
| title_full_unstemmed | A novel method for detecting structural damage based on data-driven and similarity-based techniques under environmental and operational changes |
| title_short | A novel method for detecting structural damage based on data-driven and similarity-based techniques under environmental and operational changes |
| title_sort | novel method for detecting structural damage based on data driven and similarity based techniques under environmental and operational changes |
| topic | structural damage detection damage localization damage level estimation autoregressive model independent residuals kullback similarity |
| url | https://nmce.kntu.ac.ir/article_160570_56ea245d3872615d577835089e4c9c2c.pdf |
| work_keys_str_mv | AT masoudhaghanichegeni anovelmethodfordetectingstructuraldamagebasedondatadrivenandsimilaritybasedtechniquesunderenvironmentalandoperationalchanges AT mohammadkazemsharbatdar anovelmethodfordetectingstructuraldamagebasedondatadrivenandsimilaritybasedtechniquesunderenvironmentalandoperationalchanges AT rezamahjoub anovelmethodfordetectingstructuraldamagebasedondatadrivenandsimilaritybasedtechniquesunderenvironmentalandoperationalchanges AT mehdiraftari anovelmethodfordetectingstructuraldamagebasedondatadrivenandsimilaritybasedtechniquesunderenvironmentalandoperationalchanges AT masoudhaghanichegeni novelmethodfordetectingstructuraldamagebasedondatadrivenandsimilaritybasedtechniquesunderenvironmentalandoperationalchanges AT mohammadkazemsharbatdar novelmethodfordetectingstructuraldamagebasedondatadrivenandsimilaritybasedtechniquesunderenvironmentalandoperationalchanges AT rezamahjoub novelmethodfordetectingstructuraldamagebasedondatadrivenandsimilaritybasedtechniquesunderenvironmentalandoperationalchanges AT mehdiraftari novelmethodfordetectingstructuraldamagebasedondatadrivenandsimilaritybasedtechniquesunderenvironmentalandoperationalchanges |