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首页> 外文期刊>Journal of structural engineering >Prediction of Punching Shear Capacity for Fiber-Reinforced Concrete Slabs Using Neuro-Nomographs Constructed by Machine Learning
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Prediction of Punching Shear Capacity for Fiber-Reinforced Concrete Slabs Using Neuro-Nomographs Constructed by Machine Learning

机译：利用机器学习构建的神经语音素对纤维钢筋混凝土板冲孔剪切容量的预测

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摘要

Punching shear capacity is an important parameter in designing structural elements. Accurate estimation of punching shear capacity typically requires rigorous calculation schemes. Especially for fiber-reinforced slabs, traditional design methods may not be sufficient to predict the interaction between different influencing parameters affecting punching shear capacity for such slabs. In this study, multiple state-of-the-art machine learning (ML) algorithms were utilized, namely, regression learner, ensemble tree (bagged and boosted), support vector machine (SVM), regression decision tree, Gaussian process regression (GPR), and artificial neural networks (ANN). A comprehensive evaluation of the six ML techniques was conducted with respect to model accuracy and computational efficiency. The results demonstrated that the ANN-based algorithms outperformed other ML approaches based on the values of root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R2). Furthermore, the analysis of the results has shown that the slab effective depth has the most significant effect on the predicted punching shear, followed by the width of applied load and concrete compressive strength. Python coding (with the assist of Pynomo software) was utilized to create nomographs integrated with weights resulting from the neural network model. Such neuro-nomographs can be used to simulate the results of the developed ANN model. Moreover, the values of tested punching shear capacities over predicted values (V-test/V-pred) using the neuro-nomograph have shown mean and coefficient of variation (COV) values of 1.00 and 0.05, respectively, indicating remarkably minor scatter in the prediction. (C) 2021 American Society of Civil Engineers.
机译：冲压剪切容量是设计结构元素的重要参数。精确估计冲孔剪切容量通常需要严格的计算方案。特别是对于纤维增强的板坯，传统的设计方法可能不足以预测影响这种板坯冲压剪切容量的不同影响参数之间的相互作用。在这项研究中，利用了多个最先进的机器学习（ML）算法，即回归学习者，集成树（袋装和提升），支持向量机（SVM），回归决策树，高斯过程回归（GPR ）和人工神经网络（ANN）。对模型精度和计算效率进行了六种ML技术的综合评价。结果表明，基于ANN的算法基于根均方误差（RMSE）的值，平均绝对误差（MAE）和相关系数（R2），基于ANN的算法优于其他ML方法。此外，结果的分析表明，板坯有效深度对预测的冲孔剪切具有最显着的影响，然后是施加负载和混凝土抗压强度的宽度。 Python编码（具有Pynomo软件的辅助），用于创建与神经网络模型产生的权重集成的那样。这种神经图表可用于模拟开发的ANN模型的结果。此外，使用神经图表的预测值（V-Test / V-pred）上测试的冲压剪切容量的值显示出平均值和变异系数（COV）值，分别为1.00和0.05，表明在此中的显着轻微分散预言。 （c）2021年美国土木工程师协会。

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  • 来源
    《Journal of structural engineering》 |2021年第6期|04021075.1-04021075.11|共11页
  • 作者

    Alotaibi Emran; Mostafa Omar; Nassif Nadia; Omar Maher; Arab Mohamed G.;

  • 作者单位

    Univ Sharjah Res Inst Sci & Engn Sharjah 27272 U Arab Emirates|Univ Sharjah Coll Engn Dept Civil & Environm Engn Sharjah 27272 U Arab Emirates;

    Univ Sharjah Coll Engn Dept Civil & Environm Engn Sharjah 27272 U Arab Emirates;

    Univ Sharjah Res Inst Sci & Engn Sharjah 27272 U Arab Emirates|Univ Sharjah Coll Engn Dept Civil & Environm Engn Sharjah 27272 U Arab Emirates;

    Univ Sharjah Coll Engn Dept Civil & Environm Engn Sharjah 27272 U Arab Emirates;

    Univ Sharjah Coll Engn Dept Civil & Environm Engn Sharjah 27272 U Arab Emirates|Mansoura Univ Dept Struct Engn Fac Engn D3118 Mansoura Egypt;

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