针对遗传算法（GA）存在早熟现象和局部寻优能力较差等缺陷，引入具有很强局部搜索能力的模拟退火算法（SA），组成改进的遗传模拟退火算法（GSA）提高优化问题的能力和求解质量。针对BP神经网络容易陷入局部最小和收敛速度慢等方面的不足，应用改进的遗传模拟退火算法搜索BP神经网络的最优权值和阀值，提高BP神经网络的预测精度，建立了围岩力学参数反分析的GSA-BP神经网络模型。将该模型应用于乌东德水电站右岸地下厂房围岩力学参数的反演分析中，根据监测围岩变形数据反演围岩力学参数，反演所得参数应用到正计算分析中，得出的计算位移与实测值吻合较好，说明该方法的有效性和应用于该工程的可行性。

Due to the defects of traditional genetic algorithm(GA) such as premature and poor local search ability, a simulated annealing algorithm(SA) is introduced to modify GA for better optimizing result. Afterwards, the modified genetic simulated annealing algorithm(GSA) is implemented to search for the optimal weight and threshold of BP neural network, which improves the prediction accuracy of BP neural network by overcoming its drawbacks of local minimum and slow convergence. Thus, GSA-BP neural network model is established for the back analysis of surrounding rock mechanical parameters. Finally, the model is applied to an engineering case, Wudongde Power Station, to regress surrounding rock mechanical parameters of the underground powerhouse on the right side with in-situ measured displacement data. By applying the regressive mechanical parameters to numerical model, displacements of surrounding rock are computed; and the computed displacements agree well with the measured ones; which indicates the GSA-BP neural network model is feasible for back analysis of surrounding rock mechanical parameters in real-world engineering cases.