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Machine learning and genetic algorithm-based framework for the life-cycle cost-based optimal design of self-centering building structures
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AI
DOI：10.1016/j.jobe.2023.107671.png)
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Abstract
En 中文
Self-centering systems have emerged as innovative lateral force-resisting mechanisms aimed at improving the earthquake resilience of building structures. The fully self-centering behavior effectively eliminates the residual deformation, thereby improving the post-earthquake repairability of building structures. However, this behavior does intensify floor acceleration responses that contribute to nonstructural damage and lead to increased initial costs. This research aims to develop a general machine learning and genetic algorithm-based framework for optimizing the design of self-centering building structures and to strike a balance between the initial costs and seismic losses by minimizing the overall life-cycle cost. The artificial neural network (ANN) based StructureNet model was developed for accelerating the calculation of life-cycle costs associated with self-centering structures. The genetic algorithm was adopted to determine the optimal design parameters for self-centering structures. The framework was applied to 8 design cases, with the analysis centering on two factors: the maximum allowable residual inter-story drift for controlling demolition and the initial construction cost of self-centering members. The study examined their influences on the optimization design results of self-centering building structures. The findings highlight a preference for a design approach that leans toward partially self-centering behavior as opposed to fully self-centering behavior, indicating the higher life-cycle benefits of partially selfcentering behavior.
Keywords:
Self-centering
Genetic algorithm
Life-cycle cost
ANN
Optimal design
Partially self-centering behavior
Journal
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7.4
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1.6W
Citations:
6.6W