Abstract
This study proposes a methodology that can construct the hysteretic curves of two-side clamped steel shear walls (TCSSWs) using a machine learning technology to address the inherent deficiencies of multivariable regression analysis which has been typically used for developing the hysteretic model of a common structural element. A deep neural network (DNN) is adopted for this study and a new architecture is proposed by modifying numbers of neurons and hidden layers to well capture the cyclic response of TCSSWs. Using a training datasets DNN architectures with several combinations of input parameters and configurations relating to numbers of neurons and hidden layers had been trained. This study considers a total of 70 DNN architectures and selects the best-fitting DNN architecture which produces the minimum mean-square-error value. Estimates from the proposed DNN architecture are evaluated with a test dataset and their accuracy is acceptable. Finally, the selected DNN architecture is further verified with a validation dataset consisting of TCSSWs with different material properties of steel. The modeling parameters for the hysteretic curves of TCSSWs can be properly captured by the proposed DNN architecture.
Original language | English |
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Article number | 105875 |
Journal | Structures |
Volume | 60 |
DOIs | |
State | Published - Feb 2024 |
Keywords
- Deep neural network
- Hysteretic curves
- Machine learning
- Two-side clamped steel shear walls