Vol14 No.9: 1739-1750
【Title】A back-propagation neural-network-based displacement back analysis for the identification of the geomechanical parameters of the Yonglang landslide in China
【Author】YU Fang-wei1; PENG Xiong-zhi2*; SU Li-jun1,3,4
【Addresses】1 Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China; 2 Department of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China; 3 CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China; 4 University of Chinese Academy of Sciences, Beijing 100049, China
【Citation】Yu FW, Peng XZ, Su LJ (2017) A back-propagation neural-network-based displacement back analysis for the identification of the geomechanical parameters of the Yonglang landslide in China. Journal of Mountain Science 14(9). https://doi.org/10.1007/s11629-016-4193-y
【Abstract】Xigeda formation is a type of hundred-meter-thick lacustrine sediments of being prone to triggeringlandslides along the trunk channel and tributaries of the upper Yangtze River in China. The Yonglang landslide located near Yonglang Town of Dechang County in Sichuan Province of China, which was a typical Xigeda formation landslide, was stabilized by anti-slide piles. Loading tests on a loading-test pile were conducted to measure the displacements and moments. The uncertainty of the tested geomechanical parameters of the Yonglang landslide over certain ranges would be problematic during the evaluation of the landslide. Thus, uniform design was introduced in the experimental design, and by which, numerical analyses of the loading-test pile were performed using Fast Lagrangian Analysis of Continua (FLAC3D) to acquire a database of the geomechanical parameters of the Yonglang landslide and the corresponding displacements of the loading-test pile. A three-layer back-propagation neural network was established and trained with the database, and then tested and verified for its accuracy and reliability in numerical simulations. Displacement back analysis was conducted by substituting the displacements of the loading-test pile to the well-trained three-layer back-propagation neural network so as to identify the geomechanical parameters of the Yonglang landslide. The neural-network-based displacement back analysis method with the proposed methodology is verified to be accurate and reliable for the identification of the uncertain geomechanical parameters of landslides.
【Keywords】Back-propagation neural network; Displacement back analysis; Geomechanical parameters; Landslide; Numerical analysis; Uniformdesign; Xigeda formation