Prediction of Punching Capacity of Slab-Column Connections without Transverse Reinforcement Based on a Backpropagation Neural Network
Punching shear failure of slab-column connections can cause the progressive collapse of a structure. In this study, a punching test database is first established. Then, based on the Levenberg–Marquardt (LM) algorithm and using the nonlinear function of the backpropagation neural network (BPNN), a pr...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/7904685 |
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| author | Jie Bu FanZhen Zhang Meng Zhu Zhiyang He Qigao Hu |
| author_facet | Jie Bu FanZhen Zhang Meng Zhu Zhiyang He Qigao Hu |
| author_sort | Jie Bu |
| collection | DOAJ |
| description | Punching shear failure of slab-column connections can cause the progressive collapse of a structure. In this study, a punching test database is first established. Then, based on the Levenberg–Marquardt (LM) algorithm and using the nonlinear function of the backpropagation neural network (BPNN), a prediction model of the punching capacity of slab-column connections without transverse reinforcement is established. Finally, the proposed model is compared with the formulas of the Chinese, American, and European standards using several methods. The statistical eigenvalue method shows that the BPNN model has the highest accuracy and the lowest dispersion. The defect point counting method shows that the BPNN model had the fewest total number of defects and was the safest and most economical. The influencing factor analysis suggests that factors in the BPNN model had the most reasonable influence on the punching bearing capacity of slab-column connections. Finally, the model is verified using a case study and the Matlab program. The results show that the average error of the formulas in the Chinese, American, and European standards are 21.08%, 30.21%, and 11.47%, respectively, higher than that of the BPNN model. |
| format | Article |
| id | doaj-art-7d578064b7b14c4ea2fc27e306de0618 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-7d578064b7b14c4ea2fc27e306de06182025-02-03T00:59:47ZengWileyAdvances in Civil Engineering1687-80861687-80942019-01-01201910.1155/2019/79046857904685Prediction of Punching Capacity of Slab-Column Connections without Transverse Reinforcement Based on a Backpropagation Neural NetworkJie Bu0FanZhen Zhang1Meng Zhu2Zhiyang He3Qigao Hu4College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410072, ChinaCollege of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410072, ChinaCollege of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410072, ChinaCollege of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410072, ChinaCollege of Military Education and Training, National University of Defense Technology, Changsha 410072, ChinaPunching shear failure of slab-column connections can cause the progressive collapse of a structure. In this study, a punching test database is first established. Then, based on the Levenberg–Marquardt (LM) algorithm and using the nonlinear function of the backpropagation neural network (BPNN), a prediction model of the punching capacity of slab-column connections without transverse reinforcement is established. Finally, the proposed model is compared with the formulas of the Chinese, American, and European standards using several methods. The statistical eigenvalue method shows that the BPNN model has the highest accuracy and the lowest dispersion. The defect point counting method shows that the BPNN model had the fewest total number of defects and was the safest and most economical. The influencing factor analysis suggests that factors in the BPNN model had the most reasonable influence on the punching bearing capacity of slab-column connections. Finally, the model is verified using a case study and the Matlab program. The results show that the average error of the formulas in the Chinese, American, and European standards are 21.08%, 30.21%, and 11.47%, respectively, higher than that of the BPNN model.http://dx.doi.org/10.1155/2019/7904685 |
| spellingShingle | Jie Bu FanZhen Zhang Meng Zhu Zhiyang He Qigao Hu Prediction of Punching Capacity of Slab-Column Connections without Transverse Reinforcement Based on a Backpropagation Neural Network Advances in Civil Engineering |
| title | Prediction of Punching Capacity of Slab-Column Connections without Transverse Reinforcement Based on a Backpropagation Neural Network |
| title_full | Prediction of Punching Capacity of Slab-Column Connections without Transverse Reinforcement Based on a Backpropagation Neural Network |
| title_fullStr | Prediction of Punching Capacity of Slab-Column Connections without Transverse Reinforcement Based on a Backpropagation Neural Network |
| title_full_unstemmed | Prediction of Punching Capacity of Slab-Column Connections without Transverse Reinforcement Based on a Backpropagation Neural Network |
| title_short | Prediction of Punching Capacity of Slab-Column Connections without Transverse Reinforcement Based on a Backpropagation Neural Network |
| title_sort | prediction of punching capacity of slab column connections without transverse reinforcement based on a backpropagation neural network |
| url | http://dx.doi.org/10.1155/2019/7904685 |
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