基于改进的PSO-BP神经网络的边坡稳定性研究

Research on Slope Stability Based on Improved PSO-BP Neural Network

边坡稳定性研究对于重大地质灾害防治极其重要,但由于影响边坡稳定性的因素具有非线性、多样性以及模糊性等特征,边坡稳定性分析一直是地质灾害防治领域的热难点问题。已有研究表明神经网络预测模型可有效应用边坡稳定性分析,但同时存在预测精度低、鲁棒性差、收敛速度慢等缺点。为改善上述问题,在粒子群算法优化的BP神经网络(简称PSO-BP神经网络)算法基础上提出一种改进的边坡稳定性预测模型。该模型以容重、内聚力、内摩擦角、边坡角、高度、孔隙压力比作为输入参数,以安全系数作为输出参数。通过借鉴遗传算法中的变异思想来提升模型全局寻优的能力,利用能量函数负梯度下降原理提高模型的收敛速度。将所收集到百余条边坡数据进行数据清洗,最终得到80条高质量边坡数据,随机选取其中的50条边坡数据作为模型的试验数据。最后采用十折交叉验证的方法对模型的准确性进行验证,并在多维度与其余边坡稳定性神经网络预测模型进行对比分析。结果表明:①该模型相比于其余模型收敛速度、准确率、鲁棒性均有明显提高;②将K折交叉验证应用在小样本数据下的边坡稳定性神经网络预测模型,可有效避免结果的偶然性。③该模型的预测误差仅为4.31%,满足工程精度需求,可在实际工程中为边坡稳定性分析与灾害防治提供参考。

Slope stability research is extremely important for the prevention and control of geologic hazards,but since factors affecting slope stability are rather diverse,indefinite,and nonlinear,slope stability analysis is always a hot but difficult problem.Studies have shown that neural network predic-tion models can be effectively applied in slope stability analysis.However,such models also have dis-advantages of low accuracy in prediction,poor robustness,and slow convergence.Thus,an improved slope stability prediction model is proposed based on the PSO-BP model.In this model,input parame-ters include bulk density,cohesion,internal friction angle,slope angle,slope height and pore pres-sure ratio,and the output parameter is safety factor.The model borrows the idea of mutation in genet-ic algorithm to improve the global optimization,and applies the negative gradient descent principle of the energy function to improve the convergence speed.With the data cleaning process,eighty high-quality slope data are obtained from over a hundred pieces of raw data.Then fifty data are randomly se-lected as the test data.Finally,a ten-fold cross-validation method is used to verify the accuracy of the model.Comparisons in multiple dimensions are also made with other models.The results show that:(1)Compared with traditional models,the presented model has significant improvement in aspects of convergence speed,accuracy,and robustness;(2)With small sample data,applying K-fold cross-validation to the slope stability neural network prediction model can effectively avoid the contingency of the results;(3)The model has a small error of 4.31%,which meets the engineering accuracy re-quirements,so the model can provide reference for slope stability analysis and disaster prevention in real engineering projects.