Ensemble Classifiers and Feature-Based Methods for Structural Damage Assessment
In this paper, a new structural damage detection framework is proposed based on vibration analysis and pattern recognition, which consists of two stages: (1) signal processing and feature extraction and (2) damage detection by combining the classification result. In the first stage, discriminative f...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/8899487 |
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| _version_ | 1849304380895920128 |
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| author | Hossein Babajanian Bisheh Gholamreza Ghodrati Amiri Ehsan Darvishan |
| author_facet | Hossein Babajanian Bisheh Gholamreza Ghodrati Amiri Ehsan Darvishan |
| author_sort | Hossein Babajanian Bisheh |
| collection | DOAJ |
| description | In this paper, a new structural damage detection framework is proposed based on vibration analysis and pattern recognition, which consists of two stages: (1) signal processing and feature extraction and (2) damage detection by combining the classification result. In the first stage, discriminative features were extracted as a set of proposed descriptors related to the statistical moment of the spectrum and spectral shape properties using five competitive time-frequency techniques including fast S-transform, synchrosqueezed wavelet transform, empirical wavelet transform, wavelet transform, and short-time Fourier transform. Then, forward feature selection was employed to remove the redundant information and select damage features from vibration signals. By applying different classifiers, the capability of the feature sets for damage identification was investigated. In the second stage, ensemble-based classifiers were used to improve the overall performance of damage detection based on individual classifiers and increase the number of detectable damages. The proposed framework was verified by a suite of numerical and full-scale studies (a bridge health monitoring benchmark problem, IASC-ASCE SHM benchmark structure, and a cable-stayed bridge in China). The results showed that the proposed framework was superior to the existing single classifier and could assess the damage with reduced false alarms. |
| format | Article |
| id | doaj-art-1935aae9cbb143dea054ab7bba09c6a9 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-1935aae9cbb143dea054ab7bba09c6a92025-08-20T03:55:45ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/88994878899487Ensemble Classifiers and Feature-Based Methods for Structural Damage AssessmentHossein Babajanian Bisheh0Gholamreza Ghodrati Amiri1Ehsan Darvishan2School of Civil Engineering, Iran University of Science and Technology, Tehran, IranNatural Disasters Prevention Research Center, School of Civil Engineering, Iran University of Science & Technology, Tehran, IranDepartment of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, IranIn this paper, a new structural damage detection framework is proposed based on vibration analysis and pattern recognition, which consists of two stages: (1) signal processing and feature extraction and (2) damage detection by combining the classification result. In the first stage, discriminative features were extracted as a set of proposed descriptors related to the statistical moment of the spectrum and spectral shape properties using five competitive time-frequency techniques including fast S-transform, synchrosqueezed wavelet transform, empirical wavelet transform, wavelet transform, and short-time Fourier transform. Then, forward feature selection was employed to remove the redundant information and select damage features from vibration signals. By applying different classifiers, the capability of the feature sets for damage identification was investigated. In the second stage, ensemble-based classifiers were used to improve the overall performance of damage detection based on individual classifiers and increase the number of detectable damages. The proposed framework was verified by a suite of numerical and full-scale studies (a bridge health monitoring benchmark problem, IASC-ASCE SHM benchmark structure, and a cable-stayed bridge in China). The results showed that the proposed framework was superior to the existing single classifier and could assess the damage with reduced false alarms.http://dx.doi.org/10.1155/2020/8899487 |
| spellingShingle | Hossein Babajanian Bisheh Gholamreza Ghodrati Amiri Ehsan Darvishan Ensemble Classifiers and Feature-Based Methods for Structural Damage Assessment Shock and Vibration |
| title | Ensemble Classifiers and Feature-Based Methods for Structural Damage Assessment |
| title_full | Ensemble Classifiers and Feature-Based Methods for Structural Damage Assessment |
| title_fullStr | Ensemble Classifiers and Feature-Based Methods for Structural Damage Assessment |
| title_full_unstemmed | Ensemble Classifiers and Feature-Based Methods for Structural Damage Assessment |
| title_short | Ensemble Classifiers and Feature-Based Methods for Structural Damage Assessment |
| title_sort | ensemble classifiers and feature based methods for structural damage assessment |
| url | http://dx.doi.org/10.1155/2020/8899487 |
| work_keys_str_mv | AT hosseinbabajanianbisheh ensembleclassifiersandfeaturebasedmethodsforstructuraldamageassessment AT gholamrezaghodratiamiri ensembleclassifiersandfeaturebasedmethodsforstructuraldamageassessment AT ehsandarvishan ensembleclassifiersandfeaturebasedmethodsforstructuraldamageassessment |