铁道科学与工程学报Issue(1)：66-71,6.
基于粒子群 BP 网络混合算法的边坡稳定性评价
Slope stability evaluation based on hybrid algorithm of particle swarm optimization and BP neural network
摘要
Abstract
It is highly nonlinear and uncertain to evaluate and predict slope stability,and also difficult to express using accurate mathematical model.Firstly,the multiple slope engineering instances were adopted to constitute a learning sample set.The six main influence factors,including soil density,internal friction angle,cohesion, slope angle,slope height,void ratio,composed slope stability evaluation index.Then BP neural network model was optimized using particle swarm optimization algorithm to realize the hybrid algorithm.When maintaining the BP network algorithm of error back propagation correction weight,the network weights and threshold values were particles and updated using particle swarm algorithm global searching.At the same time the convergence speed was accelerated and the convergence precision was improved.The “premature”phenomenon for the BP network algorithm combining with traditional particle swarm was avoided.Finally,the feasibility and rationality of the proposed approach in the paper were verified in comparison with other slope stability evaluation algorithms.关键词
边坡稳定性/粒子群算法/BP 神经网络/混合算法/优化Key words
slope stability/particle swarm algorithm/BP neural network/hybrid algorithm/optimization分类
信息技术与安全科学引用本文复制引用
胡卫东,曹文贵..基于粒子群 BP 网络混合算法的边坡稳定性评价[J].铁道科学与工程学报,2015,(1):66-71,6.基金项目
国家自然科学基金资助项目（51378198）；高等学校博士学科点专项科研基金资助项目（20130161110017）；湖南省教育厅资助项目 （）
