Artificial-Neural-Network-Based Surrogate Models for Structural Health Monitoring of Civil Structures: A Literature Review
It is often computationally expensive to monitor structural health using computer models. This time-consuming process can be relieved using surrogate models, which provide cheap-to-evaluate metamodels to replace the original expensive models. Because of their high accuracy, simplicity, and efficienc...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/12/12/2067 |
| _version_ | 1797461146106593280 |
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| author | Armin Dadras Eslamlou Shiping Huang |
| author_facet | Armin Dadras Eslamlou Shiping Huang |
| author_sort | Armin Dadras Eslamlou |
| collection | DOAJ |
| description | It is often computationally expensive to monitor structural health using computer models. This time-consuming process can be relieved using surrogate models, which provide cheap-to-evaluate metamodels to replace the original expensive models. Because of their high accuracy, simplicity, and efficiency, Artificial Neural Networks (ANNs) have gained considerable attention in this area. This paper reviews the application of ANNs as surrogates for structural health monitoring in the literature. Moreover, the review contains fundamental information, detailed discussions, wide comparisons, and suggestions for future research. Surrogates in this literature review are divided into parametric and nonparametric models. In the past, nonparametric models dominated this field, but parametric models have gained popularity in the recent decade. A parametric surrogate is commonly supplied with metaheuristic algorithms, and can provide high levels of identification. Recurrent networks, instead of traditional ANNs, have also become increasingly popular for nonparametric surrogates. |
| first_indexed | 2024-03-09T17:15:16Z |
| format | Article |
| id | doaj.art-6b18b2f68f6140998b69c1d2ffa7e649 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-09T17:15:16Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-6b18b2f68f6140998b69c1d2ffa7e6492023-11-24T13:41:15ZengMDPI AGBuildings2075-53092022-11-011212206710.3390/buildings12122067Artificial-Neural-Network-Based Surrogate Models for Structural Health Monitoring of Civil Structures: A Literature ReviewArmin Dadras Eslamlou0Shiping Huang1School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, ChinaSchool of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, ChinaIt is often computationally expensive to monitor structural health using computer models. This time-consuming process can be relieved using surrogate models, which provide cheap-to-evaluate metamodels to replace the original expensive models. Because of their high accuracy, simplicity, and efficiency, Artificial Neural Networks (ANNs) have gained considerable attention in this area. This paper reviews the application of ANNs as surrogates for structural health monitoring in the literature. Moreover, the review contains fundamental information, detailed discussions, wide comparisons, and suggestions for future research. Surrogates in this literature review are divided into parametric and nonparametric models. In the past, nonparametric models dominated this field, but parametric models have gained popularity in the recent decade. A parametric surrogate is commonly supplied with metaheuristic algorithms, and can provide high levels of identification. Recurrent networks, instead of traditional ANNs, have also become increasingly popular for nonparametric surrogates.https://www.mdpi.com/2075-5309/12/12/2067structural health monitoringdamage identificationsurrogate modelemulatormetamodelartificial neural network |
| spellingShingle | Armin Dadras Eslamlou Shiping Huang Artificial-Neural-Network-Based Surrogate Models for Structural Health Monitoring of Civil Structures: A Literature Review Buildings structural health monitoring damage identification surrogate model emulator metamodel artificial neural network |
| title | Artificial-Neural-Network-Based Surrogate Models for Structural Health Monitoring of Civil Structures: A Literature Review |
| title_full | Artificial-Neural-Network-Based Surrogate Models for Structural Health Monitoring of Civil Structures: A Literature Review |
| title_fullStr | Artificial-Neural-Network-Based Surrogate Models for Structural Health Monitoring of Civil Structures: A Literature Review |
| title_full_unstemmed | Artificial-Neural-Network-Based Surrogate Models for Structural Health Monitoring of Civil Structures: A Literature Review |
| title_short | Artificial-Neural-Network-Based Surrogate Models for Structural Health Monitoring of Civil Structures: A Literature Review |
| title_sort | artificial neural network based surrogate models for structural health monitoring of civil structures a literature review |
| topic | structural health monitoring damage identification surrogate model emulator metamodel artificial neural network |
| url | https://www.mdpi.com/2075-5309/12/12/2067 |
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