Aseismic Optimization of Mega-sub Controlled Structures Based on Gaussian Process Surrogate Model

Due to the complex seismic characteristics of mega-sub controlled structures (MSCS), it is difficult to give full play to their advantages in earthquake resistance by traditional design methods. Meta-heuristic optimization algorithms can be used to improve the seismic performance, but the structural response needs to be calculated repeatedly, which results in high computation cost. To overcome these challenges, an efficient aseismic optimization design procedure for engineering application is developed. In this procedure, the model of optimization problem is established based on time history analysis (THA). Gaussian process regression (GPR) surrogate models are employed to predict the values of the objective and constraint functions. The expected improvement (EI) and constrained expected improvement (CEI) criteria are adopted to update the training sample set and obtain the optimal solution. Then, two examples are presented to validate the effectiveness and efficiency of this method in optimization problems of structures under earthquake loads. Finally, it is applied to optimizations of a MSCS and a mega frame structure (MFS), respectively. The response and cost of the optimized structures are reduced, and the MSCS shows better earthquake resistant capacities.

Automated summary

This paper proposes an efficient optimization design procedure for seismic resistance in the complex seismic characteristics of mega-sub controlled structures (MSCS). By establishing an optimization problem model based on time history analysis and using Gaussian process regression surrogate models, expected improvement, and constrained expected improvement criteria, the structural optimization design is achieved. Through validation with two examples, it is demonstrated that the method is efficient and effective in optimizing structures under earthquake loads.