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ANN Model to Predict Fracture Characteristics of High Strength and Ultra High Strength Concrete Beams

机译：人工神经网络模型预测高强和超高强混凝土梁的断裂特性

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摘要

This paper presents fracture mechanics based Artificial Neural Network (ANN) model to predict the fracture characteristics of high strength and ultra high strength concrete beams. Fracture characteristics include fracture energy (G_f), critical stress intensity factor (K_(IC)) and critical crack tip opening displacement (CTOD_c). Failure load of the beam (P_(max)) is also predicated by using ANN model. Characterization of mix and testing of beams of high strength and ultra strength concrete have been described. Methodologies for evaluation of fracture energy, critical stress intensity factor and critical crack tip opening displacement have been outlined. Back-propagation training technique has been employed for updating the weights of each layer based on the error in the network output. Levenberg-Marquardt algorithm has been used for feed-forward back-propagation. Four ANN models have been developed by using MATLAB software for training and prediction of fracture parameters and failure load. ANN has been trained with about 70% of the total 87 data sets and tested with about 30% of the total data sets. It is observed from the studies that the predicted values of P_(max) G_f, failure load, K_(Ic) and CTOD_c are in good agreement with those of the experimental values.
机译：本文提出了基于断裂力学的人工神经网络（ANN）模型来预测高强度和超高强度混凝土梁的断裂特性。断裂特征包括断裂能（G_f），临界应力强度因子（K_（IC））和临界裂纹尖端的开度位移（CTOD_c）。梁的破坏荷载（P_（max））也可以通过ANN模型来预测。已经描述了高强度和超强度混凝土的混合料的表征和梁的测试。概述了评估断裂能，临界应力强度因子和临界裂纹尖端开口位移的方法。反向传播训练技术已经被用于基于网络输出中的误差来更新每一层的权重。 Levenberg-Marquardt算法已用于前馈反向传播。使用MATLAB软件已经开发出了四个神经网络模型，用于训练和预测断裂参数和破坏载荷。对ANN进行了约87个数据集的约70％的培训，并对其进行了约30％数据集的测试。从研究中可以看出，P_（max）G_f，破坏载荷，K_（Ic）和CTOD_c的预测值与实验值非常吻合。

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