Reliability-Based Design Optimization of Structures Considering Uncertainties of Earthquakes Based on Efficient Gaussian Process Regression Metamodeling

The complexity of earthquakes and the nonlinearity of structures tend to increase the calculation cost of reliability-based design optimization (RBDO). To reduce computational burden and to effectively consider the uncertainties of ground motions and structural parameters, an efficient RBDO method for structures under stochastic earthquakes based on adaptive Gaussian process regression (GPR) metamodeling is proposed in this study. In this method, the uncertainties of ground motions are described by the record-to-record variation and the randomness of intensity measure (IM). A GPR model is constructed to obtain the approximations of the engineering demand parameter (EDP), and an active learning (AL) strategy is presented to adaptively update the design of experiments (DoE) of this metamodel. Based on the reliability of design variables calculated by Monte Carlo simulation (MCS), an optimal solution can be obtained by an efficient global optimization (EGO) algorithm. To validate the effectiveness and efficiency of the developed method, it is applied to the optimization problems of a steel frame and a reinforced concrete frame and compared with the existing methods. The results show that this method can provide accurate reliability information for seismic design and can deal with the problems of minimizing costs under the probabilistic constraint and problems of improving the seismic reliability under limited costs.

Automated summary

An efficient reliability-based design optimization (RBDO) method, based on adaptive Gaussian process regression (GPR) metamodeling, is proposed in this study to reduce the computational burden and effectively consider the uncertainties of ground motions and structural parameters. The method uses a GPR model to approximate the engineering demand parameter (EDP) and an active learning strategy to update the design of experiments (DoE) of this metamodel. The reliability of design variables is calculated using Monte Carlo simulation (MCS), and an optimal solution is obtained using an efficient global optimization (EGO) algorithm. The developed method is validated and compared with existing methods by applying it to the optimization problems of a steel frame and a reinforced concrete frame. The results demonstrate that the method can provide accurate reliability information for seismic design and can handle the problems of minimizing costs under the probabilistic constraint and improving seismic reliability under limited costs.