边坡岩土体抗剪强度的逆向迭代修正反演方法

针对已滑动或有明显变形的边坡，取定稳定系数值进行反演分析是确定岩土体抗剪强度的重要手段。当边坡滑动面穿越多层岩土体时，盲目地抗剪强度试算反演明显不合理。为解决此问题，构造以多层岩土体抗剪强度为输入，以GeoSlope计算得到的稳定系数、滑面剪入口和剪出口位置为输出的BP神经网络，基于取定的稳定系数和现场测定的滑动面剪入口和剪出口位置，通过重复执行“逆向反推-误差校验-样本修正”实现岩土体抗剪强度的逆向迭代修正反演。工程实例验证结果表明，该方法获取的岩土体抗剪强度基本合理，可供小型边坡防护工程设计参考。该方法成功地规避了BP神经网络以已知信息为输入、以待反演参数为输出的传统做法在解决该问题时为欠定的局限性，对样本库样本数量的要求降低且具有较好精度。

Inversion iterative correction method for estimating shear strength of rock and soil mass in slope engineering

For slopes that has failed or deformed significantly, the shear strength of rock and soil mass is frequently inversely estimated based on a factor of safety assumed. For the slope with a sliding surface passing through multi-layer rock and soil mass, it is unreasonable to achieve this goal by trial and error. To solve this issue, back propagation (BP) neural network is constructed using shear strength of multi-layer rock and soil mass as the input and the factor of safety of the slope, and the entry and exit positions of the sliding surface obtained by GeoSlope as the outputs. Then, based on the assumed factor of safety and the entry and exit positions measured in site, the shear strength is acquired by carrying out the “reverse back analysis-error check-sample correction” procedure repeatedly. The result of a case study verifies that the shear strength obtained by this method is reasonable and can be used as a reference when designing prevention measures for small-scale slopes. BP neural network usually considers the known information as the input, and the information to be determined as the output, which will induce a mathematical underdetermined problem when solving this issue. The proposed method avoids this demerit successfully, and has a lower requirement on the number of samples in the library and a higher precision compared to the classical BP neural network.