基于智能优化算法的边坡稳定性预测方法研究

Research on Slope Stability Prediction Method Based on Intelligent Optimization Algorith

针对边坡稳定性预测中数据分析片面、模型预测精度低的问题,基于302个边坡案例,选取6个变量特征,利用麻雀搜索算法(SSA)更新BP神经网络的敏感因子,建立SSA-BP边坡稳定性预测模型。采用混淆矩阵、受试者工作特征(ROC)曲线及曲线下面积A_(UC)值作为衡量指标,通过五折交叉验证法提高模型的泛化能力并与RF、BP、SVM、PSO-BP、GA-BP和LSTM 6种机器学习算法进行预测效果对比。结果表明,SSA-BP模型的A_(UC)值、准确率和F_1分数均最高,分别为91.90%、85.81%和85.87%,相较于优化前的BP网络A_(UC)值提高了23%。经典算例证明SSA-BP预测模型与ABAQUS计算的安全系数相近,并可给出可靠的预测结果,为岩土工程中边坡稳定性预测提供了一种新方法。

Aiming at the issues of biased data analysis and poor model accuracy in prediction of slope stability,six variable features are chosen based on 302 slope examples,and the BP neural network's sensitive parameters are updated by the sparrow search algorithm(SSA).The SSA-BP slope stability prediction model is established.As measurement indi-cators,the confusion matrix,the ROC curve,and the area under the curve(AUC)value were employed.The five-fold cross-validation procedure increased the model's capacity for generalization.The five machine learning algorithms of RF,BP,SVM,PSO-BP,GA-BP and LSTM were compared.The results show that the AUC value,accuracy and F1 score of the SSA-BP model were the highest,and the responding values reached 91.90%,85.81%and 85.87%,respectively,which was 23%higher than that of the BP network before optimization.The classical example proved that the SSA-BP prediction model is similar to the safety factor calculated by ABAQUS,which can give reliable prediction results.Thus,it provides a new way for slope stability prediction in geotechnical engineering.