Design Parameter Optimization of Self-centering Pier Based on Deep Learning
Separating the pier and platform at the base of the pier and embedding the prestressed steel bars and reinforcements into the pier body facilitates the creation of a self-centering pier. The pier’s geometric and material mechanical parameters affect its seismic performance; however, the impact on cr...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Editorial Department of Journal of Sichuan University (Engineering Science Edition)
2024-11-01
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| Series: | 工程科学与技术 |
| Subjects: | |
| Online Access: | http://jsuese.scu.edu.cn/thesisDetails#10.12454/j.jsuese.202300574 |
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| Summary: | Separating the pier and platform at the base of the pier and embedding the prestressed steel bars and reinforcements into the pier body facilitates the creation of a self-centering pier. The pier’s geometric and material mechanical parameters affect its seismic performance; however, the impact on critical seismic performance indices (energy-consuming capacity, residual deformation, and load-bearing capacity) remains unclear due to the coupling of design parameters. Optimizing these parameters for the rocking self-centering pier is crucial. This study proposes a deep learning-based method for optimizing the design parameters of self-centering piers. The NSGA and MOEA/D algorithms are employed to achieve a multi-objective optimization solution. The AHP-entropy weighting method considers subjective and objective factors when determining the weight coefficients among targets. The TOPSIS method is applied to rank the optimal solutions for Pareto. A finite element analysis model is developed to optimize the critical design parameters of the self-centering pier with embedded prestressed steel bars and reinforcements, considering the three seismic performance indices (residual displacement, equivalent viscous damping coefficient, and load-bearing capacity). The results demonstrated that the method can account for the randomness in the pier’s geometric and material mechanical performance parameters, harmonize multiple design objectives, and rapidly identify design parameters with the best comprehensive performance. In addition, longer unbonded reinforcements can effectively reduce residual deformation, higher initial pretension of prestressing tendons enhances structural load-carrying capacity and mitigates residual deformation, and high-grade reinforcements are advantageous for improving load-carrying capacity and reducing residual deformation. A finite element structural agent model created through the deep learning method can incorporate random parameters related to geometric and material mechanical properties to improve the model’s robustness and the self-centering pier’s multi-objective optimization efficiency. |
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| ISSN: | 2096-3246 |