基于人工神经网络的钢筋混凝土受弯构件正截面抗裂预测  

Predicting for Cracking Resistance of Normal Section of Reinforced Concrete Flexural Members Based on Artificial Neural Network

作　　者：程晓天 卢军燕[2] 马伟丽 殷柯柯 聂金荣[2] 

机构地区：[1]河南省产品质量监督检验院,河南郑州450004 [2]郑州交通职业学院交通工程系,河南郑州450000 [3]许昌中原建设集团房地产开发公司,河南许昌461000

出　　处：《水利与建筑工程学报》2010年第1期138-139,143,共3页Journal of Water Resources and Architectural Engineering

摘　　要：钢筋混凝土构件抗烈度预测受多种条件因素的影响,现有的方法由试验实测数据建立的数学模型误差较大,因此有必要寻求一种精度较高的方法进行抗烈度预测。通过实测试验数据,训练形成一个三层BP网络,其中78组数据作为学习样本,另外9组数据则作为测试样本,建立了人工神经网络预测钢筋混凝土正截面抗裂性能的方法,还对其他模型抗裂性能的计算值与实测值进行了比较。该方法预测值与试验值吻合良好。结果表明,提出的人工神经网络预测钢筋混凝土正截面抗裂度预测方法具有对直接参与训练的数据仿真效果好,整体预测精度高,与理论分析得出的结论基本一致,可用于受弯构件抗烈度预测。Based on test results,a three-1ayer back-propagation network is trained and formed by using the experimental data,among which 78 groups are used for training samples,while the remaining 9 groups are used for testing samples.The method for predicting the cracking resistance of normal section of reinforced concrete flexural members is set up with the method of artificial neural network（ANN）.Good agreement is reached between the predicted results and the test data.It is concluded that the ANN method is feasible to the estimation for the cracking resistance of normal section of reinforced concrete flexural members in practice.

关 键 词：钢筋混凝土 受弯构件 人工神经网络 正截面抗裂 非线性 

分 类 号：TU375[建筑科学—结构工程]