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Assessment of shear capacity of adhesive anchors for structures using neural network based model

机译：基于神经网络的模型对结构锚固剪切力的评估

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摘要

In this study, an artificial neural network (NN) based explicit formulation for predicting the edge breakout shear capacity of single adhesive anchors post-installed into concrete member was proposed. To this aim, a comprehensive experimental database of 98 specimens tested in shear was used to train and test NN model as well as to assess the accuracy of the existing equations given by American Concrete Institute and prestressed/precast concrete Institute. Moreover, the proposed NN model was compared with another existing model which had been derived from gene expression programming by the authors in a previous study. The prediction parameters utilized for derivation of the model were anchor diameter, type of anchor, edge distance, embedment depth, clear clearance of the anchor, type of chemical adhesive, method of injection of the chemical, and compressive strength of the concrete. The proposed model yielded correlation coefficients of 0.983 and 0.984 for training and testing data sets, respectively. It was found that the predictions obtained from NN agreed well with experimental observations, yielding approximately 5 % mean absolute percent error. Moreover, in comparison to the existing models, the proposed NN model had all of the predicted values in +/- 20 % error bands while the others estimated up to %160 error.
机译：在这项研究中，提出了一种基于人工神经网络（NN）的显式公式，用于预测安装在混凝土构件中的单个胶粘锚的边缘断裂剪切能力。为此，我们使用了一个完整的实验数据库，该数据库包含98个经过剪切测试的试样，用于训练和测试NN模型，以及评估由美国混凝土学会和预应力/预制混凝土学会给出的现有方程式的准确性。此外，将拟议的NN模型与另一项现有模型进行了比较，该模型是作者在先前的研究中从基因表达编程得出的。用于模型推导的预测参数是锚的直径，锚的类型，边缘距离，包埋深度，锚的净间隙，化学粘合剂的类型，化学剂的注入方法以及混凝土的抗压强度。对于训练和测试数据集，该模型得出的相关系数分别为0.983和0.984。发现从NN获得的预测与实验观察非常吻合，平均绝对误差百分比约为5％。此外，与现有模型相比，建议的NN模型在+/- 20％的误差带中具有所有预测值，而其他模型的误差估计高达％160。

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