基于神经网络的边坡安全系数快速预测研究

Study on fast prediction of slope safety factor based on neural network

快速准确获取边坡安全系数对预防边坡发生滑坡等地质灾害具有非常重要的意义。在影响边坡稳定性的各种因素的基础上进行综合考虑,利用计算机科学和人工智能科学,以神经网络方法为基础,运用BP模型,进行边坡安全系数快速预测,建立了快速预测系统并实现了界面化操作,以黄土边坡实例为样本进行了模拟验证。结果表明:该方法对快速准确获取边坡的安全系数有一定的参考价值,可为边坡工程研究智能化提供帮助。

It is of great importance to get the slope safety factor quickly and accurately to prevent the shope geological hazard.In this paper,considering the influence of various factors on the slope stability,by using computer science and artificial intelligence and basing on neural network method,BP model is used for rapid prediction of slope safety factor,establishment of a rapid prediction system and realization of the interface operation.A case study of loess slope was used as a sample to verify the model.The results show this method has a certain reference value for quickly and accurately obtaining of slope safety factor,which can also provide help for the intelligentizing of the research of slope engineering.