Landslides are regarded as significant geological hazards across the world, causing serious economic losses and casualties. The understanding on deformation characteristics and failure mechanisms of landslides plays the vital roles in slope stability evaluation and reinforcement design. In this study, the deformation characteristics and failure mechanism of the Xiaomiaoling talus slope were analyzed based on field monitoring data. In addition, as it was difficult to measure the shear strength parameters of the rock–soil mixture due to its complex spatial structure and variable material composition, a displacement back analysis based on the back-propagation neural network (DBA-BPNN) was proposed to determine the shear strength parameters of the rock–soil mixture. The analytical results show that deformation of the Xiaomiaoling talus slope was that of a typical traction landslide, which has the characteristics of progressive failure, and major slope deformation was triggered by excavation and rainfall. According to field monitoring data, the shear strength parameters of the rock–soil mixture could be determined. The predicted cohesion and internal friction angle of the rock–soil mixture were 10.84 kPa and 19.51°, respectively, and the predicted and test values were in good agreement. The method proposed in this paper can provide references for the design and construction in geotechnical engineering.

中文翻译：

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基于反向传播神经网络的距坡变形综合监测及力学参数反演分析

滑坡在世界范围内被视为重大的地质灾害，造成了严重的经济损失和人员伤亡。了解滑坡的变形特征和破坏机制，对边坡稳定性评价和加固设计具有重要意义。本研究基于现场监测数据，对小庙岭距骨边坡的变形特征及破坏机制进行了分析。此外，由于岩土混合体空间结构复杂、材料成分多变，难以测量其抗剪强度参数，提出了一种基于反向传播神经网络（DBA-BPNN）的位移反分析方法。确定岩土混合物的抗剪强度参数。分析结果表明，小庙岭距坡体的变形为典型的牵引滑坡，具有渐进破坏特征，主要由开挖和降雨引发的边坡变形。根据现场监测数据，可以确定岩土混合物的抗剪强度参数。预测的岩土混合物的黏聚力和内摩擦角分别为10.84 kPa和19.51°，预测值与试验值吻合较好。本文提出的方法可为岩土工程的设计和施工提供参考。可以确定岩土混合物的剪切强度参数。预测的岩土混合物的黏聚力和内摩擦角分别为10.84 kPa和19.51°，预测值与试验值吻合较好。本文提出的方法可为岩土工程的设计和施工提供参考。可以确定岩土混合物的剪切强度参数。预测的岩土混合物的黏聚力和内摩擦角分别为10.84 kPa和19.51°，预测值与试验值吻合较好。本文提出的方法可为岩土工程的设计和施工提供参考。