自适应神经一模糊推理系统在水库边坡稳定性评价中的应用  

Applying Adaptive Neuro-Fuzzy Inference System to Stability Assessment of Reservoir Slope

作　　者：肖治宇[1] 陈昌富[1] 季永新 

机构地区：[1]湖南大学岩土工程研究所,湖南长沙410082 [2]贵州省建筑科学研究检测中心,贵州贵阳550001

出　　处：《水土保持通报》2011年第5期186-190,共5页Bulletin of Soil and Water Conservation

基　　金：国家自然科学基金项目"残坡积土边坡锚固系统水稳性与时变可靠性分析"(50878082);交通部西部交通科技项目"贵州山区浅变质岩系风化层路基边坡稳定性研究"(200631880237);湖南省自然科学基金重点项目"水对残坡积土中锚杆锚固性能影响及其长期可靠性分析研究"(09JJ3104)

摘　　要：针对水库边坡稳定性影响因子之间复杂的非线性关系,利用自适应神经模糊推理系统(ANFIS)能够同时处理确定性和不确定性信息以及动态非线性分析的能力,提出了基于ANFIS的水库边坡稳定性评价方法。将渗透系数、水位降速、孔隙压力比、坡角、坡高、凝聚力、内摩擦角、重度8个参数作为输入,以水库边坡稳定性系数作为输出,基于21个工程实例,建立了基于ANFIS的水库边坡稳定性评价模型。该模型对训练样本拟合的相关系数为0.999 96,对检测样本的预测相关系数为0.977 48,优于BP神经网络模型。对江西省某水库边坡稳定性进行了预测,结果发现所建立的ANFIS模型对考虑多影响因子耦合作用的水库边坡稳定性有较好的预报功能。The relationship among the controlling factors of reservoir slope stability are highly non-linear.Meanwhile,the adaptive neuro—fuzzy inference system（ANFIS） has been widely recognized for its capability of nonlinear dynamic analysis and processing both certain and uncertain information at the same time.Hence,the employment of ANFIS to assess the stability of reservoir slope was proposed.With eight parameters including permeability coefficient,declining rate of the water level,pore pressure ratio,slope angle,slope height,cohesion,internal friction angle,and severity as the inputs,and the reservoir slope stability coefficient as the output,a ANFIS model has been constructed based on 21 project cases.The training correlation coefficient of the model was 0.999 96,and the correlation coefficient of the validation was 0.977 48,which was significantly better than the BP neural network model.The successful prediction on the slope stability of a dam reservoir in Jiangxi Province illustrated a desirable forecasting ability of the established ANFIS model for coupling multiple impact factors.

关 键 词：边坡 水库 ANFIS 稳定性 

分 类 号：U213[交通运输工程—道路与铁道工程]