A Neural-Wavelet Technique for Damage Identification in the ASCE Benchmark Structure Using Phase II Experimental Data
Damage pattern recognition research represents one of the most challenging tasks in structural health monitoring (SHM). The vagueness in defining damage and the significant overlap between damage states contribute to the challenges associated with proper damage classification. Uncertainties in the d...
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| Format: | Article |
| Language: | English |
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Wiley
2010-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2010/675927 |
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| _version_ | 1832552329515106304 |
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| author | Mahmoud M. Reda Taha |
| author_facet | Mahmoud M. Reda Taha |
| author_sort | Mahmoud M. Reda Taha |
| collection | DOAJ |
| description | Damage pattern recognition research represents one of the most challenging tasks in structural health monitoring (SHM). The vagueness in defining damage and the significant overlap between damage states contribute to the challenges associated with proper damage classification. Uncertainties in the damage features and how they propagate during the damage detection process also contribute to uncertainties in SHM. This paper introduces an integrated method for damage feature extraction and damage recognition. We describe a robust damage detection method that is based on using artificial neural network (ANN) to compute the wavelet energy of acceleration signals acquired from the structure. We suggest using the wavelet energy as a damage feature to classify damage states in structures. A case study is presented that shows the ability of the proposed method to detect and pattern damage using the American Society of Civil Engineers (ASCEs) benchmark structure. It is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy to varying levels of damage. |
| format | Article |
| id | doaj-art-ddc5bb4194ae4dbb9cbbb7dc8621a2c4 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2010-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-ddc5bb4194ae4dbb9cbbb7dc8621a2c42025-02-03T05:59:05ZengWileyAdvances in Civil Engineering1687-80861687-80942010-01-01201010.1155/2010/675927675927A Neural-Wavelet Technique for Damage Identification in the ASCE Benchmark Structure Using Phase II Experimental DataMahmoud M. Reda Taha0Department of Civil Engineering, The University of New Mexico, Albuquerque, NM 87131, USADamage pattern recognition research represents one of the most challenging tasks in structural health monitoring (SHM). The vagueness in defining damage and the significant overlap between damage states contribute to the challenges associated with proper damage classification. Uncertainties in the damage features and how they propagate during the damage detection process also contribute to uncertainties in SHM. This paper introduces an integrated method for damage feature extraction and damage recognition. We describe a robust damage detection method that is based on using artificial neural network (ANN) to compute the wavelet energy of acceleration signals acquired from the structure. We suggest using the wavelet energy as a damage feature to classify damage states in structures. A case study is presented that shows the ability of the proposed method to detect and pattern damage using the American Society of Civil Engineers (ASCEs) benchmark structure. It is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy to varying levels of damage.http://dx.doi.org/10.1155/2010/675927 |
| spellingShingle | Mahmoud M. Reda Taha A Neural-Wavelet Technique for Damage Identification in the ASCE Benchmark Structure Using Phase II Experimental Data Advances in Civil Engineering |
| title | A Neural-Wavelet Technique for Damage Identification in the ASCE Benchmark Structure Using Phase II Experimental Data |
| title_full | A Neural-Wavelet Technique for Damage Identification in the ASCE Benchmark Structure Using Phase II Experimental Data |
| title_fullStr | A Neural-Wavelet Technique for Damage Identification in the ASCE Benchmark Structure Using Phase II Experimental Data |
| title_full_unstemmed | A Neural-Wavelet Technique for Damage Identification in the ASCE Benchmark Structure Using Phase II Experimental Data |
| title_short | A Neural-Wavelet Technique for Damage Identification in the ASCE Benchmark Structure Using Phase II Experimental Data |
| title_sort | neural wavelet technique for damage identification in the asce benchmark structure using phase ii experimental data |
| url | http://dx.doi.org/10.1155/2010/675927 |
| work_keys_str_mv | AT mahmoudmredataha aneuralwavelettechniquefordamageidentificationintheascebenchmarkstructureusingphaseiiexperimentaldata AT mahmoudmredataha neuralwavelettechniquefordamageidentificationintheascebenchmarkstructureusingphaseiiexperimentaldata |