Intelligent generation and optimization method for the retrofit design of RC frame structures using buckling-restrained braces

As buildings and structures age, the challenges of reinforcement and retrofitting become more significant, especially as their service life extends and the demand for seismic fortification increases. Integrating buckling-restrained braces (BRBs) is an effective retrofit technique; however, this approach requires multiple iterations of layout adjustments and mechanical performance analysis, which are highly dependent on engineers’ design expertise, resulting in low efficiency. To address this, the study proposes a two-stage intelligent retrofit design method that integrates generative Artificial intelligence (AI) techniques with optimization algorithms for reinforced concrete (RC) frame structures using BRBs: (1) a diffusion model-based potential BRB layout generation stage, and (2) an online learning algorithm-based design optimization stage. In Stage 1, a diffusion model was employed to analyze architectural characteristics, identify potential BRB locations, narrow the feasible solution space for the optimization process, and ensure that the design meets empirical constraints. In Stage 2, an optimization algorithm, integrated with mechanical performance evaluation, was employed to determine the optimal locations and sizes of BRBs. Case studies revealed that these two methods enhanced efficiency by approximately 50 times compared to the direct design by engineers while maintaining design rationality and safety. Overall, these results demonstrate the feasibility and generalizability of the method in practical engineering applications, offering a reference for the intelligent design of more complex structural retrofits in the future.

Recommended Citation: Tan, Z., Qin, S.Z., Hu, K.G., Liao, W.J., Gao, Y., Lu, X.Z., 2024. Intelligent generation and optimization method for the retrofit design of RC frame structures using buckling-restrained braces. Earthquake Engineering & Structural Dynamics. https://doi.org/10.1002/eqe.4268
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