Performance comparison of neural network training algorithms in the modeling properties of steel fiber reinforced concrete
Performance comparison of neural network training algorithms in the modeling properties of steel fiber reinforced concrete
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DOI：
10.1016/j.heliyon.2018.e01115
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发表时间：
2019-01-01
期刊：
影响因子：
4
通讯作者：
Aluko, O. G.
中科院分区：
文献类型：
--
作者：
Awolusi, T. F.;Oke, O. L.;Aluko, O. G.
我们的研究旨在模拟三个贡献因素，即长径比，水灰比和水泥含量对钢纤维混凝土的吸水/吸水，抗压强度，抗折强度，劈裂抗拉强度和坍落度性能的影响。人工神经网络（ANN）作为一个多层感知器正常前馈网络集成开发上述性能的预测模型。五个训练算法，属于三类：梯度下降，Levenberg马夸特（拟牛顿）和遗传算法（GA）。人工神经网络配置由三个节点的输入层，一个单一的隐藏层的输出层的五个节点的十个节点。该研究还比较了所有算法的预测能力。通过将实验数据分成训练集和测试集来完成ANN训练。监测测试集的输出值和目标值之间的RMSE的偏差，并将其用作停止训练的标准。虽然遗传算法的收敛速度远远高于所有其他训练算法，它表现出更好的预测吸水率/吸水率，劈裂抗拉强度和坍落度性能。然而，增量反向传播（IBP）和批量反向传播（BBP）在预测抗压强度和抗折强度分别优于遗传算法。训练算法的整体性能进行了评估，使用系数的决定和绝对分数的方差得到的测试数据集和GA被发现有最高值分别为0.94和0.92。在确定性能纤维增强混凝土根据GA-ANN实现，水/水泥比发挥更主导的作用比纵横比，其次是水泥含量。
Our study is aimed at modeling the effect of three contributory factors, namely aspect ratio, water cement ratio and cement content on the water intake/absorption, compressive strength, flexural strength, split tensile strength and slump properties of steel fiber reinforced concrete. Artificial neural network (ANN) as a multilayer perceptron normal feed forward network was integrated to develop a predictive model for the aforementioned properties. Five training algorithms belonging to three classes: gradient descent, Levenberg Marquardt (quasi Newton) and genetic algorithm (GA). The ANN configuration consists of the input layer with three nodes, a single hidden layer of ten nodes of the output layer with five nodes. The study also compared the performance of all algorithms with regards to their predicting abilities. The ANN training was done by splitting the experimental data into the training and testing set. The divergence of the RMSE between the output and target values of the test set was monitored and used as a criterion to stop training. Although the convergence speed of GA was far higher than all other training algorithm, it performed better in predicting the water intake/absorption, split tensile strength and slump properties. However, incremental back propagation (IBP) and batch back propagation (BBP) outperformed GA in predicting the compressive strength and flexural strength respectively. The overall performance of the training algorithm was assessed using the coefficient of determination and the absolute fraction of variance obtained for the test data set and GA was found to have the highest value of 0.94 and 0.92 respectively. In determining the properties fiber reinforced concrete according to GA-ANN implementation, the water/cement ratio played slightly more dominant role than the aspect ratio and this was followed by cement content.