黄莹, 李俊才, 张鹏, 周煜程. 2016: 基于L-M神经网络的陡崖层状危岩失稳模式预测. 工程地质学报, 24(s1): 734-741. DOI: 10.13544/j.cnki.jeg.2016.s1.107
    引用本文: 黄莹, 李俊才, 张鹏, 周煜程. 2016: 基于L-M神经网络的陡崖层状危岩失稳模式预测. 工程地质学报, 24(s1): 734-741. DOI: 10.13544/j.cnki.jeg.2016.s1.107
    HUANG Ying, LI Juncai, ZHANG Peng, ZHOU Yucheng. 2016: ANALYZING METHOD OF DEFORMATION INSTABiILLY MODE OF DANGEROUS ROCKS AT CLIFF FACES BASED ON L-M ARTIFICIAL NEURAL NETWORK. JOURNAL OF ENGINEERING GEOLOGY, 24(s1): 734-741. DOI: 10.13544/j.cnki.jeg.2016.s1.107
    Citation: HUANG Ying, LI Juncai, ZHANG Peng, ZHOU Yucheng. 2016: ANALYZING METHOD OF DEFORMATION INSTABiILLY MODE OF DANGEROUS ROCKS AT CLIFF FACES BASED ON L-M ARTIFICIAL NEURAL NETWORK. JOURNAL OF ENGINEERING GEOLOGY, 24(s1): 734-741. DOI: 10.13544/j.cnki.jeg.2016.s1.107

    基于L-M神经网络的陡崖层状危岩失稳模式预测

    ANALYZING METHOD OF DEFORMATION INSTABiILLY MODE OF DANGEROUS ROCKS AT CLIFF FACES BASED ON L-M ARTIFICIAL NEURAL NETWORK

    • 摘要: 明确危岩变形失稳模式是预防和治理研究的前提,但如今的传统预测方法存在成本高昂、实用性不强等缺点,尤其是在强烈地震诱发下。本文采用基于Levenberg-Marquardt法的神经网络结构,并借助三维离散元数值模拟手段,综合考虑了陡崖层状危岩的节理倾角、危岩边长、危岩高宽比、危岩堆积层数等影响变形失稳模式因素,以危岩变形失稳模式为研究对象,影响因素为切入点,建立了陡崖层状危岩变形失稳模式预测的神经网络模型。并基于由数值模拟计算得到的危岩变形失稳模式样本训练所建立的神经网络,最后分析了该预测模型的准确性。结果表明:该模型具有较好的学习和泛化能力,预测精度达到86.7%,验证了基于Levenberg-Marquardt法的神经网络预测危岩变形失稳模式的方法是有效且实际可行的。

       

      Abstract: Clearing deformation instability mode of dangerous rocks is the premise of prevention and treatment research, but traditional forecasting methods have shortcomings of high-cost and weak practicality, especially under strong earthquake. in this paper, Adopting methods of neural network structure based on Levenberg-Marquardt and three dimensional discrete element numerical simulation, also considering deformation instability mode of dangerous rocks under four main varying conditions, including joint dip angle, length of rock, depth-width ratio and stacking layers of dangerous rocks, and taking deformation instability mode of dangerous rocks for research objects and influencing factors as the breakthrough point, the BP neural network model to predict deformation instability mode of dangerous rocks is established. Then the neural network is trained with samples of deformation instability mode of dangerous rocks calculated by numerical simulation. Finally, the paper analyzes the accuracy of the prediction model. the results show that this model is provided with good learning and generalization ability, and the accuracy of prediction is 86.7%. All these indexes validate that neural network based on Levenberg-Marquardt method to predict deformation instability mode of dangerous rocks is effective and feasible.

       

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