Lu Shasha, Koopialipoor Mohammadreza, Asteris Panagiotis G, Bahri Maziyar, Armaghani Danial Jahed
Civil Engineering College, Liaoning Technical University, Fuxin 123000, China.
Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran 15914, Iran.
Materials (Basel). 2020 Sep 3;13(17):3902. doi: 10.3390/ma13173902.
When designing flat slabs made of steel fiber-reinforced concrete (SFRC), it is very important to predict their punching shear capacity accurately. The use of machine learning seems to be a great way to improve the accuracy of empirical equations currently used in this field. Accordingly, this study utilized tree predictive models (i.e., random forest (RF), random tree (RT), and classification and regression trees (CART)) as well as a novel feature selection (FS) technique to introduce a new model capable of estimating the punching shear capacity of the SFRC flat slabs. Furthermore, to automatically create the structure of the predictive models, the current study employed a sequential algorithm of the FS model. In order to perform the training stage for the proposed models, a dataset consisting of 140 samples with six influential components (i.e., the depth of the slab, the effective depth of the slab, the length of the column, the compressive strength of the concrete, the reinforcement ratio, and the fiber volume) were collected from the relevant literature. Afterward, the sequential FS models were trained and verified using the above-mentioned database. To evaluate the accuracy of the proposed models for both testing and training datasets, various statistical indices, including the coefficient of determination (R) and root mean square error (RMSE), were utilized. The results obtained from the experiments indicated that the FS-RT model outperformed FS-RF and FS-CART models in terms of prediction accuracy. The range of R and RMSE values were obtained as 0.9476-0.9831 and 14.4965-24.9310, respectively; in this regard, the FS-RT hybrid technique demonstrated the best performance. It was concluded that the three hybrid techniques proposed in this paper, i.e., FS-RT, FS-RF, and FS-CART, could be applied to predicting SFRC flat slabs.
在设计钢纤维增强混凝土（SFRC）平板时，准确预测其冲切抗剪承载力非常重要。使用机器学习似乎是提高该领域当前使用的经验公式准确性的好方法。因此，本研究利用树预测模型（即随机森林（RF）、随机树（RT）和分类与回归树（CART））以及一种新颖的特征选择（FS）技术，引入了一种能够估算SFRC平板冲切抗剪承载力的新模型。此外，为了自动创建预测模型的结构，本研究采用了FS模型的顺序算法。为了对所提出的模型进行训练阶段，从相关文献中收集了一个由140个样本组成的数据集，这些样本具有六个影响因素（即板厚、板的有效深度、柱长、混凝土抗压强度、配筋率和纤维体积）。之后，使用上述数据库对顺序FS模型进行训练和验证。为了评估所提出模型对测试和训练数据集的准确性，使用了各种统计指标，包括决定系数（R）和均方根误差（RMSE）。实验结果表明，FS-RT模型在预测准确性方面优于FS-RF和FS-CART模型。R和RMSE值的范围分别为0.9476 - 0.9831和14.4965 - 24.9310；在这方面，FS-RT混合技术表现最佳。得出的结论是，本文提出的三种混合技术，即FS-RT、FS-RF和FS-CART，可应用于预测SFRC平板。
