Damage Classification Approach for Concrete Structure Using Support Vector Machine Learning of Decomposed Electromechanical Admittance Signature via Discrete Wavelet Transform
The identification of structural damage types remains a key challenge in electromechanical impedance/admittance (EMI/EMA)-based structural health monitoring realm. This paper proposed a damage classification approach for concrete structures by using integrating discrete wavelet transform (DWT) decom...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2075-5309/15/15/2616 |
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| author | Jingwen Yang Demi Ai Duluan Zhang |
| author_facet | Jingwen Yang Demi Ai Duluan Zhang |
| author_sort | Jingwen Yang |
| collection | DOAJ |
| description | The identification of structural damage types remains a key challenge in electromechanical impedance/admittance (EMI/EMA)-based structural health monitoring realm. This paper proposed a damage classification approach for concrete structures by using integrating discrete wavelet transform (DWT) decomposition of EMA signatures with supervised machine learning. In this approach, the EMA signals of arranged piezoelectric ceramic (PZT) patches were successively measured at initial undamaged and post-damaged states, and the signals were decomposed and processed using the DWT technique to derive indicators including the wavelet energy, the variance, the mean, and the entropy. Then these indicators, incorporated with traditional ones including root mean square deviation (RMSD), baseline-changeable RMSD named RMSDk, correlation coefficient (CC), and mean absolute percentage deviation (MAPD), were processed by a support vector machine (SVM) model, and finally damage type could be automatically classified and identified. To validate the approach, experiments on a full-scale reinforced concrete (RC) slab and application to a practical tunnel segment RC slab structure instrumented with multiple PZT patches were conducted to classify severe transverse cracking and minor crack/impact damages. Experimental and application results cogently demonstrated that the proposed DWT-based approach can precisely classify different types of damage on concrete structures with higher accuracy than traditional ones, highlighting the potential of the DWT-decomposed EMA signatures for damage characterization in concrete infrastructure. |
| format | Article |
| id | doaj-art-8f606a8797d6445aaf433130c314b831 |
| institution | DOAJ |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Buildings |
| spelling | doaj-art-8f606a8797d6445aaf433130c314b8312025-08-20T03:02:55ZengMDPI AGBuildings2075-53092025-07-011515261610.3390/buildings15152616Damage Classification Approach for Concrete Structure Using Support Vector Machine Learning of Decomposed Electromechanical Admittance Signature via Discrete Wavelet TransformJingwen Yang0Demi Ai1Duluan Zhang2School of Civil Engineering, Qilu Institute of Technology, Jinan 250103, ChinaSchool of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaThe identification of structural damage types remains a key challenge in electromechanical impedance/admittance (EMI/EMA)-based structural health monitoring realm. This paper proposed a damage classification approach for concrete structures by using integrating discrete wavelet transform (DWT) decomposition of EMA signatures with supervised machine learning. In this approach, the EMA signals of arranged piezoelectric ceramic (PZT) patches were successively measured at initial undamaged and post-damaged states, and the signals were decomposed and processed using the DWT technique to derive indicators including the wavelet energy, the variance, the mean, and the entropy. Then these indicators, incorporated with traditional ones including root mean square deviation (RMSD), baseline-changeable RMSD named RMSDk, correlation coefficient (CC), and mean absolute percentage deviation (MAPD), were processed by a support vector machine (SVM) model, and finally damage type could be automatically classified and identified. To validate the approach, experiments on a full-scale reinforced concrete (RC) slab and application to a practical tunnel segment RC slab structure instrumented with multiple PZT patches were conducted to classify severe transverse cracking and minor crack/impact damages. Experimental and application results cogently demonstrated that the proposed DWT-based approach can precisely classify different types of damage on concrete structures with higher accuracy than traditional ones, highlighting the potential of the DWT-decomposed EMA signatures for damage characterization in concrete infrastructure.https://www.mdpi.com/2075-5309/15/15/2616concrete structureelectro-mechanical admittance (EMA)support vector machine (SVM)piezoelectric ceramic (PZT) sensordamage classificationdiscrete wavelet transform (DWT) |
| spellingShingle | Jingwen Yang Demi Ai Duluan Zhang Damage Classification Approach for Concrete Structure Using Support Vector Machine Learning of Decomposed Electromechanical Admittance Signature via Discrete Wavelet Transform Buildings concrete structure electro-mechanical admittance (EMA) support vector machine (SVM) piezoelectric ceramic (PZT) sensor damage classification discrete wavelet transform (DWT) |
| title | Damage Classification Approach for Concrete Structure Using Support Vector Machine Learning of Decomposed Electromechanical Admittance Signature via Discrete Wavelet Transform |
| title_full | Damage Classification Approach for Concrete Structure Using Support Vector Machine Learning of Decomposed Electromechanical Admittance Signature via Discrete Wavelet Transform |
| title_fullStr | Damage Classification Approach for Concrete Structure Using Support Vector Machine Learning of Decomposed Electromechanical Admittance Signature via Discrete Wavelet Transform |
| title_full_unstemmed | Damage Classification Approach for Concrete Structure Using Support Vector Machine Learning of Decomposed Electromechanical Admittance Signature via Discrete Wavelet Transform |
| title_short | Damage Classification Approach for Concrete Structure Using Support Vector Machine Learning of Decomposed Electromechanical Admittance Signature via Discrete Wavelet Transform |
| title_sort | damage classification approach for concrete structure using support vector machine learning of decomposed electromechanical admittance signature via discrete wavelet transform |
| topic | concrete structure electro-mechanical admittance (EMA) support vector machine (SVM) piezoelectric ceramic (PZT) sensor damage classification discrete wavelet transform (DWT) |
| url | https://www.mdpi.com/2075-5309/15/15/2616 |
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