Optimal Design of Earthquake-Resistant Buildings Based on Neural Network Inversion
An effective seismic design entails many issues related to the capacity-based assessment of the non-linear structural response under strong earthquakes. While very powerful structural calculation programs are available to assist the designer in the code-based seismic analysis, an optimal choice of t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/10/4654 |
| _version_ | 1827692144671850496 |
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| author | Carlo Calledda Augusto Montisci Maria Cristina Porcu |
| author_facet | Carlo Calledda Augusto Montisci Maria Cristina Porcu |
| author_sort | Carlo Calledda |
| collection | DOAJ |
| description | An effective seismic design entails many issues related to the capacity-based assessment of the non-linear structural response under strong earthquakes. While very powerful structural calculation programs are available to assist the designer in the code-based seismic analysis, an optimal choice of the design parameters leading to the best performance at the lowest cost is not always assured. The present paper proposes a procedure to cost-effectively design earthquake-resistant buildings, which is based on the inversion of an artificial neural network and on an optimization algorithm for the minimum total cost under building code constraints. An exemplificative application of the method to a reinforced-concrete multi-story building, with seismic demands corresponding to a medium-seismicity Italian zone, is shown. Three design-governing parameters are assumed to build the input matrix, while eight capacity-design target requirements are assigned for the output dataset. A non-linear three-dimensional concentrated plasticity model of the structure is implemented, and time-history dynamic analyses are carried out with spectrum-consistent ground motions. The results show the promising ability of the proposed approach for the optimal design of earthquake-resistant structures. |
| first_indexed | 2024-03-10T11:16:19Z |
| format | Article |
| id | doaj.art-92774bf9469a4fc2864cff159e520724 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T11:16:19Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-92774bf9469a4fc2864cff159e5207242023-11-21T20:26:14ZengMDPI AGApplied Sciences2076-34172021-05-011110465410.3390/app11104654Optimal Design of Earthquake-Resistant Buildings Based on Neural Network InversionCarlo Calledda0Augusto Montisci1Maria Cristina Porcu2Department of Civil-Environmental Engineering and Architecture, University of Cagliari, 09123 Cagliari, ItalyDepartment of Electric and Electronic Engineering, University of Cagliari, 09123 Cagliari, ItalyDepartment of Civil-Environmental Engineering and Architecture, University of Cagliari, 09123 Cagliari, ItalyAn effective seismic design entails many issues related to the capacity-based assessment of the non-linear structural response under strong earthquakes. While very powerful structural calculation programs are available to assist the designer in the code-based seismic analysis, an optimal choice of the design parameters leading to the best performance at the lowest cost is not always assured. The present paper proposes a procedure to cost-effectively design earthquake-resistant buildings, which is based on the inversion of an artificial neural network and on an optimization algorithm for the minimum total cost under building code constraints. An exemplificative application of the method to a reinforced-concrete multi-story building, with seismic demands corresponding to a medium-seismicity Italian zone, is shown. Three design-governing parameters are assumed to build the input matrix, while eight capacity-design target requirements are assigned for the output dataset. A non-linear three-dimensional concentrated plasticity model of the structure is implemented, and time-history dynamic analyses are carried out with spectrum-consistent ground motions. The results show the promising ability of the proposed approach for the optimal design of earthquake-resistant structures.https://www.mdpi.com/2076-3417/11/10/4654optimal structural designearthquake-resistant buildingsinverse artificial neural networknon-linear dynamic analysis |
| spellingShingle | Carlo Calledda Augusto Montisci Maria Cristina Porcu Optimal Design of Earthquake-Resistant Buildings Based on Neural Network Inversion Applied Sciences optimal structural design earthquake-resistant buildings inverse artificial neural network non-linear dynamic analysis |
| title | Optimal Design of Earthquake-Resistant Buildings Based on Neural Network Inversion |
| title_full | Optimal Design of Earthquake-Resistant Buildings Based on Neural Network Inversion |
| title_fullStr | Optimal Design of Earthquake-Resistant Buildings Based on Neural Network Inversion |
| title_full_unstemmed | Optimal Design of Earthquake-Resistant Buildings Based on Neural Network Inversion |
| title_short | Optimal Design of Earthquake-Resistant Buildings Based on Neural Network Inversion |
| title_sort | optimal design of earthquake resistant buildings based on neural network inversion |
| topic | optimal structural design earthquake-resistant buildings inverse artificial neural network non-linear dynamic analysis |
| url | https://www.mdpi.com/2076-3417/11/10/4654 |
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