An efficient inverse algorithm for load identification of stochastic structures

Force identification of stochastic structures is very important in science and engineering, which also leads to the challenges in the field of computational mechanics. Monte-Carlo simulation (MCS) method is a robust and effective random simulation technique for the dynamic load identification problem of the stochastic structure. However, the MCS method needs large computational cost and is also inefficient for practical engineering applications because of the requirement of a large quantity of samples. In this paper, in order to improve computational efficiency of MCS, a novel algorithm is proposed based on the modified conjugate gradient method and matrix perturbation method. First, the new developed algorithm exploits matrix perturbation method to transform dynamic load identification problems for stochastic structures into equivalent deterministic dynamic load identification problems. Then the dynamic load identification can be realized using modified conjugate gradient method. Finally, the statistical characteristics of identified force are analyzed. The accuracy and efficiency of the newly developed computational method are demonstrated by several numerical examples. It has been found that the newly proposed algorithm can significantly improve the computational efficiency of MCS and it is believed to be a powerful tool for solving the dynamic load identification for stochastic structures.