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.

随机结构的力识别在科学和工程中非常重要，这也导致了计算力学领域的挑战。蒙特卡洛模拟（MCS）方法是一种针对随机结构的动态载荷识别问题的鲁棒且有效的随机模拟技术。然而，由于需要大量样本，因此MCS方法需要大量的计算成本，并且在实际工程应用中效率也不高。为了提高MCS的计算效率，在改进的共轭梯度法和矩阵摄动法的基础上，提出了一种新的算法。第一，新开发的算法利用矩阵摄动法将随机结构的动态载荷识别问题转化为等效的确定性动态载荷识别问题。利用改进的共轭梯度法可以实现动载荷识别。最后，分析了识别力的统计特征。通过几个数值示例证明了新开发的计算方法的准确性和效率。已经发现，新提出的算法可以显着提高MCS的计算效率，并且被认为是解决随机结构的动态载荷识别的有力工具。最后，分析了识别力的统计特征。通过几个数值示例证明了新开发的计算方法的准确性和效率。已经发现，新提出的算法可以显着提高MCS的计算效率，并且被认为是解决随机结构的动态载荷识别的有力工具。最后，分析了识别力的统计特征。通过几个数值示例证明了新开发的计算方法的准确性和效率。已经发现，新提出的算法可以显着提高MCS的计算效率，并且被认为是解决随机结构的动态载荷识别的有力工具。