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基于BP神经网络的FRP筋与混凝土界面黏结强度预测

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陈健,黄丽华,曲激婷.基于BP神经网络的FRP筋与混凝土界面黏结强度预测[J].,2021,61(3):272-279
基于BP神经网络的FRP筋与混凝土界面黏结强度预测
Prediction of interface bond strength between FRP bars and concrete based on BP neural network
DOI:10.7511/dllgxb202103007
中文关键词:ANNBP神经网络模型FRP筋混凝土黏结强度预测
英文关键词:ANNBP neural network modelFRP reinforced concretebond strengthprediction
基金项目:国家自然科学基金资助项目(5177811351678115).
作者单位
陈健,黄丽华,曲激婷
摘要点击次数:243
全文下载次数:211
中文摘要:
纤维筋增强混凝土材料的界面黏结强度是评价其力学性能的重要指标之一.基于现有文献中的试验数据,建立了292组FRP筋混凝土拉拔试验的数据库,利用人工神经网络对FRP筋与混凝土之间的黏结强度进行预测.数据库被随机分成两个数据集,其中242组数据用于训练,50组数据用于仿真预测.利用反向传播算法训练了一个3层人工神经网络模型,该模型的输入层包括7个参数:FRP筋类型、表面形式、FRP筋直径、锚固长度、破坏模式、混凝土抗压强度和归一化的混凝土保护层厚度.输出层为FRP筋与混凝土界面黏结强度.结果表明,BP神经网络模型具有良好的预测和泛化能力,预测误差较小.该方法能够综合考虑众多FRP筋与混凝土界面黏结强度的影响因素,给出精确的预测结果.
英文摘要:
The interface bond strength of FRP (fiber reinforced polymer) reinforced concrete is one of the important indicators to evaluate its mechanical properties. Based on the experimental data available in the literature, a database including 292 groups of pull out test results of FRP reinforced concrete is established, and thus the bond strength between FRP bars and concrete is predicted by using artificial neural network. The database is randomly divided into two data sets, of which 242 groups of data are used for training and 50 groups of data are used for simulation prediction. A three layer artificial neural network model is trained by using the back propagation algorithm, where seven parameters are considered in the input layer, namely FRP type, surface form, FRP rebar diameter, anchorage length, failure mode, concrete compressive strength and normalized concrete cover thickness. The output layer is specified as the interface bond strength between FRP bars and concrete. The results indicate that the BP neural network model has strong capability of prediction and generalization, and the predicting error is minor. This method can integrate many considerations those influence the interface bond strength between FRP bars and concrete, and give out accurate predicting results.
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