中文摘要：

将支持向量回归（SVR）算法引入岩土工程数值计算模型参数的辨识中可以充分发挥SVR算法的小样本、泛化性好和全局最优化的优点。但现阶段标准的SVR算法只能解决一维输出变量的回归问题，这就使其在反分析领域的应用受到限制。引入一种改进的SVR算法，这种算法通过将多维输出变量回归转化为多层标准一维输出变量回归来解决这个问题，并与十进制编码的遗传算法相结合，形成改进的GA-SVR算法，用遗传算法搜索最优的SVR模型参数以建立最优的待辨识参数与位移之间的非线性映射关系，然后用遗传算法进行待辨识参数的最优辨识。为对比这种改进GA-SVR算法的效果，将遗传算法与BP神经网络相结合，形成GA-BP算法且编制相应的计算程序。将这两种算法运用于同样的隧道工程三维弹塑性模型参数的智能辨识，数值算例表明改进的GA-SVR算法较GA-BP算法可以取得更高的辨识精度和更好的计算效率，可运用于类似岩土工程计算参数的辨识。

英文摘要：

The support vector regression（SVR） algorithm has been introduced into parameters identification of numerical model in geotechnical engineering to take advantage of its merits such as small sample, good generalization and global optimization. But, the standard SVR algorithm can only solve one-dimensional output variable regression problem, thus restrict its application in back analysis field. In this paper, an improved SVR algorithm is introduced by decomposing multi-dimension output variables to many one-dimensional output variables, and then the multi-dimensional output variable regression is translated into a multi-layer standard SVR problem. In order to find the optimal parameters of this improved SVR model during sample training course, the genetic algorithm（GA） is combined with it to form the improved GA-SVR algorithm. After the optimal nonlinear mapping between the numerical model parameters and displacement is constructed, GA is used to identify the numerical model parameters within their search interval. In virtue of MATLAB toolbox, GA also is integrated into BP neural network to form the GA-BP algorithm. By comparing the identification results of three-dimensional elastoplastic model parameters in tunnel engineering by the two different algorithms, it can be concluded that the improved GA-SVR algorithm can obtain a higher identification precision and calculation efficiency than the GA-BP algorithm and can be applied to similar calculation parameters identification in geotechnical engineering.