Uncertainty Handling in Structural Damage Detection via Non-Probabilistic Meta-Models and Interval Mathematics, a Data-Analytics Approach
Recent advancements in sensor technology have resulted in the collection of massive amounts of measured data from the structures that are being monitored. However, these data include inherent measurement errors that often cause the assessment of quantitative damage to be ill-conditioned. Attempts to...
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MDPI AG
2021-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/2/770 |
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| author | Ramin Ghiasi Mohammad Noori Wael A. Altabey Ahmed Silik Tianyu Wang Zhishen Wu |
| author_facet | Ramin Ghiasi Mohammad Noori Wael A. Altabey Ahmed Silik Tianyu Wang Zhishen Wu |
| author_sort | Ramin Ghiasi |
| collection | DOAJ |
| description | Recent advancements in sensor technology have resulted in the collection of massive amounts of measured data from the structures that are being monitored. However, these data include inherent measurement errors that often cause the assessment of quantitative damage to be ill-conditioned. Attempts to incorporate a probabilistic method into a model have provided promising solutions to this problem by considering the uncertainties as random variables, mostly modeled with Gaussian probability distribution. However, the success of probabilistic methods is limited due the lack of adequate information required to obtain an unbiased probabilistic distribution of uncertainties. Moreover, the probabilistic surrogate models involve complicated and expensive computations, especially when generating output data. In this study, a non-probabilistic surrogate model based on wavelet weighted least squares support vector machine (WWLS-SVM) is proposed to address the problem of uncertainty in vibration-based damage detection. The input data for WWLS-SVM consists of selected wavelet packet decomposition (WPD) features of the structural response signals, and the output is the Young’s modulus of structural elements. This method calculates the changes in the lower and upper boundaries of Young’s modulus based on an interval analysis method. Considering the uncertainties in the input parameters, the surrogate model is used to predict this interval-bound output. The proposed approach is applied to detect simulated damage in the four-story benchmark structure of the IASC-ASCE SHM group. The results show that the performance of the proposed method is superior to that of the direct finite element model in the uncertainty-based damage detection of structures and requires less computational effort. |
| first_indexed | 2024-03-09T04:44:11Z |
| format | Article |
| id | doaj.art-a6ec997c81034a1faf1b12001e63bb88 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T04:44:11Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a6ec997c81034a1faf1b12001e63bb882023-12-03T13:17:30ZengMDPI AGApplied Sciences2076-34172021-01-0111277010.3390/app11020770Uncertainty Handling in Structural Damage Detection via Non-Probabilistic Meta-Models and Interval Mathematics, a Data-Analytics ApproachRamin Ghiasi0Mohammad Noori1Wael A. Altabey2Ahmed Silik3Tianyu Wang4Zhishen Wu5International Institute for Urban Systems Engineering (IIUSE), Southeast University, Nanjing 210096, ChinaDepartment of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93405, USAInternational Institute for Urban Systems Engineering (IIUSE), Southeast University, Nanjing 210096, ChinaInternational Institute for Urban Systems Engineering (IIUSE), Southeast University, Nanjing 210096, ChinaInternational Institute for Urban Systems Engineering (IIUSE), Southeast University, Nanjing 210096, ChinaInternational Institute for Urban Systems Engineering (IIUSE), Southeast University, Nanjing 210096, ChinaRecent advancements in sensor technology have resulted in the collection of massive amounts of measured data from the structures that are being monitored. However, these data include inherent measurement errors that often cause the assessment of quantitative damage to be ill-conditioned. Attempts to incorporate a probabilistic method into a model have provided promising solutions to this problem by considering the uncertainties as random variables, mostly modeled with Gaussian probability distribution. However, the success of probabilistic methods is limited due the lack of adequate information required to obtain an unbiased probabilistic distribution of uncertainties. Moreover, the probabilistic surrogate models involve complicated and expensive computations, especially when generating output data. In this study, a non-probabilistic surrogate model based on wavelet weighted least squares support vector machine (WWLS-SVM) is proposed to address the problem of uncertainty in vibration-based damage detection. The input data for WWLS-SVM consists of selected wavelet packet decomposition (WPD) features of the structural response signals, and the output is the Young’s modulus of structural elements. This method calculates the changes in the lower and upper boundaries of Young’s modulus based on an interval analysis method. Considering the uncertainties in the input parameters, the surrogate model is used to predict this interval-bound output. The proposed approach is applied to detect simulated damage in the four-story benchmark structure of the IASC-ASCE SHM group. The results show that the performance of the proposed method is superior to that of the direct finite element model in the uncertainty-based damage detection of structures and requires less computational effort.https://www.mdpi.com/2076-3417/11/2/770surrogate modelsuncertaintiesnon-probabilisticinterval analysiswavelet packet decomposition (WPD)data analytics |
| spellingShingle | Ramin Ghiasi Mohammad Noori Wael A. Altabey Ahmed Silik Tianyu Wang Zhishen Wu Uncertainty Handling in Structural Damage Detection via Non-Probabilistic Meta-Models and Interval Mathematics, a Data-Analytics Approach Applied Sciences surrogate models uncertainties non-probabilistic interval analysis wavelet packet decomposition (WPD) data analytics |
| title | Uncertainty Handling in Structural Damage Detection via Non-Probabilistic Meta-Models and Interval Mathematics, a Data-Analytics Approach |
| title_full | Uncertainty Handling in Structural Damage Detection via Non-Probabilistic Meta-Models and Interval Mathematics, a Data-Analytics Approach |
| title_fullStr | Uncertainty Handling in Structural Damage Detection via Non-Probabilistic Meta-Models and Interval Mathematics, a Data-Analytics Approach |
| title_full_unstemmed | Uncertainty Handling in Structural Damage Detection via Non-Probabilistic Meta-Models and Interval Mathematics, a Data-Analytics Approach |
| title_short | Uncertainty Handling in Structural Damage Detection via Non-Probabilistic Meta-Models and Interval Mathematics, a Data-Analytics Approach |
| title_sort | uncertainty handling in structural damage detection via non probabilistic meta models and interval mathematics a data analytics approach |
| topic | surrogate models uncertainties non-probabilistic interval analysis wavelet packet decomposition (WPD) data analytics |
| url | https://www.mdpi.com/2076-3417/11/2/770 |
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