Dual Polynomial Response Surface-Based Robust Design Optimization of Structure Under Stochastic Blast Load

The present study deals with robust design optimization (RDO) of an underground bunker under stochastic blast-induced ground motion. Since the direct Monte Carlo Simulation (MCS) requires extensive computational time to solve an RDO problem, the polynomial response surface method (RSM) is highly appreciated as an alternative to reduce the computational burden. Thus, the present study attempts to explore the advantage of the moving least-squares method (MLSM) based dual RSM to take into account the record-to-record variation in the blast load in place of the conventional least-squares method (LSM). The blast-induced ground shock has been artificially generated by considering the Tajimi-Kanai power spectral density function (PSDF) and incorporating uncertainty in the blast parameters, viz. explosion distance and explosive charge weight. The application of the proposed dual RSM in RDO not only evades several finite element analyses runs in the simulation loop during the optimization process, but also improve the computational efficiency significantly. The RDO is formulated by simultaneously optimizing the expected value and variance of the performance function by using the weighted sum approach. The results show that the present MLSM-based RDO strategy yields more accurate and robust solutions than the conventional LSM-based approach when compared with the direct MCS results.