作　　者： ; ; ;

机构地区： 华南理工大学土木与交通学院

出　　处： 《铁道科学与工程学报》 2007年第6期30-34,共5页

摘　　要： 在碳纤维薄板(CFL)增强RC梁中,加固后构件的疲劳寿命受到被加固结构本身性能、加固材料性能以及荷载水平等因素的影响,加固效果具有明显的非线性,加固后疲劳寿命难以显式表达。本文采用AFN法预测加固梁的疲劳寿命:用层次分析法(Analytic hierarchy process)对影响疲劳寿命的主要因素进行分析;用模糊聚类法(Fuzzy clustering)确定训练样本;运用神经网络方法(Neural network),建立疲劳寿命隐函数关系式,从而预测CFL加固梁抗弯疲劳寿命。以疲劳试验数据为样本,用层次分析法选取最大荷载、CFL的应力水平、初始挠度、循环次数为200时跨中挠度的试验数据作为疲劳寿命隐函数的随机变量输入单元,以CFL加固梁的抗弯疲劳寿命作为输出单元。通过预测值与试验值的对比分析,验证了AFN法预测CFL增强RC梁抗弯疲劳寿命的合理性。 The fatigue life of the reinforced concrete（RC） beams strengthened with carbon fiber laminate（CFL） was influenced by the properties of the RC beam,the strengthening materials and the loading conditions,etc.Therefore,the strengthening effect has high nonlinearity.In order to predict the fatigue life of the strengthened beams,the AFN（analytic hierarchy process-fuzzy clusteringneural network）method was introduced.Main factors influencing the fatigue life were analyzed by analytic hierarchy process（AHP） and the training sample was selected by fuzzy clustering method.The training samples were chosen from the test results obtained by the authors＇ research group.The neural network model included four input elements,which were the maximum load,the stress range in CFL,the initial deflection and the deflection when the number of fatigue cycles to 200,and one output element,the fatigue life.A BP Neural Network model was established to predict the fatigue life of the strengthened beams.The agreement between the experiment results and the prediction values confirms the validity of the proposed mode.

关 键 词： 法 疲劳寿命 碳纤维薄板 梁 神经网络 层次分析 模糊聚类

领　　域： [理学] [理学] [建筑科学] [交通运输工程]