基于机器学习的边坡安全稳定性评价及防护措施

Slope safety,stability evaluation,and protective measures based on machine learning

为了更加快捷、高效地判定边坡稳定与否,基于机器学习,融合主成分分析法(PCA)、参数调整、影响因素权重分析等,建立了一种边坡安全稳定性评价体系.研究发现,运用PCA可以在保留80%数据原信息的前提下将输入变量维度从六维降至三维,但此时模型效果有所下降;随机森林及梯度提升(XGBoost)两种学习算法均可搭建有效的边坡安全稳定性评估模型,通过对其预测效果的对比分析,确定XGBoost为最佳评价模型.与此同时,采取卡方检验、F检验以及互信息法3种相关性检验手段,并通过计算评价因子的重要程度且加以可视化展示,明确了容重、坡高、内摩擦角以及内聚力4个内在因素的重要性,最终将评估结果与实际结合提出了边坡安全防护措施.

In recent years,the slope instability has brought immeasurable costs to production and life of human.As a result,it is essential to correctly understand,analyze,and design the slope reasonably,and implement appropriate protective measures to minimize the loss and harm caused by its instability.By far,slope stability can be investigated using theoretical analysis,numerical modeling and machine learning prediction,among them machine learning prediction has been the most encouraging one.Many studies have been performed using machine learning algorithms to predict the slope stability.However,these methods suffers from poor accuracy and poor generalisation capbility,so its real-life application has been limited.In the current study,a machine learning-based slope safety and stability evaluation system is established by integrating principal component analysis,parameter adjustment,and influence factor weight analysis.It is shown that PCA can reduce the dimensions of the input variables from six to three while retaining 80%of the information;however,at the cost of the model’s effectiveness.The random forest and XGBoost(eXtreme Gradient Boosting)learning algorithms can both be employed to develop effective evaluation models for slope safety and stability.The comparative analysis of algorithms’prediction effects established XGBoost as the best evaluation model,which can achieve the average accuracy of 92%,precision of 91%,recall of 96%,and the area under the receiver operating characteristic curve(AUC)of 0.95.In addition,this study employs three types of test methods:the chi-square test,F test correlation,and mutual information method,meanwhile by calculating and visualizing the importance of influencing factors,the influence of unit weight,slope height,internal friction angle and cohesion on slope stability is demonstrated.It has been shown that the unit weight is the most influencing factor for the slope stability.Finally,the slope safety protection measures are proposed by combining the evaluation results with the actual project.