Optimization-based statistical model calibration is a method of statistically estimating and adjusting unknown input variables of a computational model to improve the agreement of model predictions with experimental results.Since its process of optimization and validation requires a high computational cost for uncertainty propagation analysis,a surrogate model is adapted to reduce the cost.Our goal in this study is to perform concentrated adaptive sampling near the optimum,not in the entire domain,to reduce the computational cost of the surrogate model and improve the solution accuracy.The accuracy of the estimated input variables depends on the accuracy of the surrogate model near the optimal distribution.