Conjugate Cellular Automata and Neural Network Approach: Failure Load Prediction of Masonry Panels
The intricate interplay between the microscopic constituents and their macroscopic properties for masonry structures complicates their failure analysis modelling. A composite strategy incorporating neural network (NN) and cellular automata (CA) is developed to predict the failure load for masonry pa...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/9032857 |
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| author | Iuliia Glushakova Qihan Liu Yu Zhang Guangchun Zhou |
| author_facet | Iuliia Glushakova Qihan Liu Yu Zhang Guangchun Zhou |
| author_sort | Iuliia Glushakova |
| collection | DOAJ |
| description | The intricate interplay between the microscopic constituents and their macroscopic properties for masonry structures complicates their failure analysis modelling. A composite strategy incorporating neural network (NN) and cellular automata (CA) is developed to predict the failure load for masonry panels with and without openings subjected to lateral loadings. The discretized panels are modelled by the CA methodology using nine neighbour cells, which derive their state values from geometric parameters and opening location placement for the panels. An identification coefficient dictated by these geometric parameters and experimental data is fed together as the input training data for the NN. The NN uses a backpropagation algorithm and two hidden layers with sigmoid activation functions to predict failure loads. This method achieves greater accuracy in prediction when compared with the yield line and finite elemental analysis (FEA) methods. The results attained elucidate the feasibility of the current methodology to complement conventional approaches such as FEA to provide additional insight into the failure mechanism of masonry panels under varied loading conditions. |
| format | Article |
| id | doaj-art-94dae87aff194c8a9237e82d0aa49dc3 |
| institution | OA Journals |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-94dae87aff194c8a9237e82d0aa49dc32025-08-20T02:09:48ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/90328579032857Conjugate Cellular Automata and Neural Network Approach: Failure Load Prediction of Masonry PanelsIuliia Glushakova0Qihan Liu1Yu Zhang2Guangchun Zhou3School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, ChinaDepartment of Media Technology and Art, Harbin Institute of Technology, Harbin 150090, ChinaSchool of Civil Engineering, Harbin Institute of Technology, Harbin 150090, ChinaSchool of Civil Engineering, Harbin Institute of Technology, Harbin 150090, ChinaThe intricate interplay between the microscopic constituents and their macroscopic properties for masonry structures complicates their failure analysis modelling. A composite strategy incorporating neural network (NN) and cellular automata (CA) is developed to predict the failure load for masonry panels with and without openings subjected to lateral loadings. The discretized panels are modelled by the CA methodology using nine neighbour cells, which derive their state values from geometric parameters and opening location placement for the panels. An identification coefficient dictated by these geometric parameters and experimental data is fed together as the input training data for the NN. The NN uses a backpropagation algorithm and two hidden layers with sigmoid activation functions to predict failure loads. This method achieves greater accuracy in prediction when compared with the yield line and finite elemental analysis (FEA) methods. The results attained elucidate the feasibility of the current methodology to complement conventional approaches such as FEA to provide additional insight into the failure mechanism of masonry panels under varied loading conditions.http://dx.doi.org/10.1155/2020/9032857 |
| spellingShingle | Iuliia Glushakova Qihan Liu Yu Zhang Guangchun Zhou Conjugate Cellular Automata and Neural Network Approach: Failure Load Prediction of Masonry Panels Advances in Civil Engineering |
| title | Conjugate Cellular Automata and Neural Network Approach: Failure Load Prediction of Masonry Panels |
| title_full | Conjugate Cellular Automata and Neural Network Approach: Failure Load Prediction of Masonry Panels |
| title_fullStr | Conjugate Cellular Automata and Neural Network Approach: Failure Load Prediction of Masonry Panels |
| title_full_unstemmed | Conjugate Cellular Automata and Neural Network Approach: Failure Load Prediction of Masonry Panels |
| title_short | Conjugate Cellular Automata and Neural Network Approach: Failure Load Prediction of Masonry Panels |
| title_sort | conjugate cellular automata and neural network approach failure load prediction of masonry panels |
| url | http://dx.doi.org/10.1155/2020/9032857 |
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