摘要
结合具体工程案例,运用Geo-Studio软件对边坡渗透特性和稳定性进行分析;采用基于粒子群优化算法(PSO)的反向传播BP神经网络算法对降雨条件下边坡最小安全系数进行预测,同时对影响边坡稳定性的参数进行优化。结果表明:坡顶孔隙水压力呈现先增大后减小的规律,坡底孔隙水压力在降雨期间逐渐增大,停雨后保持不变;不同非饱和参数对土体孔隙水压力及边坡稳定性有一定影响;基于PSO的BP神经网络算法,能够较好地对降雨工况下边坡的最小安全系数进行模拟预测和验证。
In relation to specific project case,Geo-Studio software was used to analyze permeability and stability of slope,and particle swarm optimization(PSO)based back propagation(BP)neural network algorithm was used to predict the minimum safety factor of slope under rainfall conditions,at the same time,the parameters influencing the slope stability were optimized.The results show that,the pore water pressure on the slope top presents a law of first increasing and then decreasing,and the pore water pressure at the slope bottom gradually increases in the course of rainfall,and remains unchanged after rainfall.Different unsaturated parameters have certain influence on the pore water pressure of soil mass and the stability of slope.PSO-based BP neural network algorithm may do well in simulation prediction and verification on minimum safety factor of slope under rainfall conditions.
作者
唐淼
桂红生
李选正
李勇义
鲁俊
TANG Miao;Gui Hongsheng;LI Xuanzheng;LI Yongyi;LU Jun(CCCC Second Highway Consultants Co.,Ltd.,Wuhan 430056,China;China Harbor Engineering Company Ltd.,Beijing 100010,China;Capital Construction Department,Shandong University,Jinan 250061,China;Institute of Deep Earth Sciences and Green Energy,Shenzhen University,Shenzhen 518060,Guangdong,China;Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization,Shenzhen University,Shenzhen 518060,Guangdong,China;College of Civil and Transportation Engineering,Shenzhen University,Shenzhen 518060,Guangdong,China)
出处
《路基工程》
2024年第5期26-31,共6页
Subgrade Engineering
基金
国家自然科学基金(52374222)。
关键词
降雨
粒子群优化算法
BP神经网络
最小安全系数
非饱和参数
rainfall
particle swarm optimization
BP neural networks
minimum safety factor
unsaturated parameters
