基于位移反分析的岩质边坡稳定性分析  

Study on the stability of rock slope based on back analysis of displacement

作　　者：谢良[1] 支学艺[1] 郭钟群[2] 吴乐文[1] 

机构地区：[1]江西理工大学资源与环境工程学院,江西赣州341000 [2]江西理工大学建筑与测绘工程学院,江西赣州341000

出　　处：《有色金属科学与工程》2011年第5期89-92,共4页Nonferrous Metals Science and Engineering

摘　　要：通过工程现场获得边坡位移量等信息,并基于正交试验设计和FLAC3D建立训练样本和测试样本,运用BP神经网络建立起边坡位移与待反演参数之间潜在的映射关系.利用粒子群算法的参数优化功能优化BP神经网络,然后再用粒子群算法从全局空间上搜索出BP神经网络中预测位移与实测位移最接近的一组参数组合,最后采用FLAC3D计算出边坡的安全系数来评价其稳定性.研究表明将BP神经网络与粒子群算法相结合,进行位移反分析是可行的;通过位移反分析得到的参数结果,进行稳定性分析将更准确.Slope displacement data and other information at the project site were used to make the training samples and test samples,based on orthogonal experimental design and FLAC3D numerical simulation.The potential mapping relationship between slope displacement and parameters to be back analyzed was established by the BP neural network.The BP neural network was optimized by particle swarm optimization,which then was used to search out the most likely equivalent parameters between forecast and measured displacement in the global space of BP neural network.At last,the safety factor of slope that was used to evaluate its stability was obtained by FLAC3D.The result shows that it is feasible to carry out the back analysis of displacement,combined with BP neural network and particle swarm optimization;the stability analysis would be more exact when the parameters are gotten by the back analysis.

关 键 词：位移反分析 粒子群算法 BP神经网络 正交试验 安全系数 

分 类 号：TD311[矿业工程—矿井建设]