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Prediction of shear strength and behavior of RC beams strengthened with externally bonded FRP sheets using machine learning techniques

机译：使用机器学习技术预测用外部粘结的FRP片加固的RC梁的抗剪强度和性能

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摘要

This paper presents the use Machine Learning (ML) techniques to study the behavior of shear-deficient reinforced concrete (RC) beams strengthened in shear with side-bonded and U-wrapped fiber-reinforced polymers (FRP) laminates. An extensive database consisting of 120 tested specimen and 15 parameters was collected. The resilient back-propagating neural network (RBPNN) was used as a regression tool and the recursive feature elimination (RFE) algorithm and neural interpretation diagram (NID) were employed within the validated RBPNN to identify the parameters that greatly influence the prediction of FRP shear capacity. The results indicated that the RBPNN with the selected parameters was capable of predicting the FRP shear capacity more accurately (r(2) = 0.885; RMSE = 8.1 kN) than that of the RBPNN with the original 15 parameters (r(2) = 0.668; RMSE = 16.6 kN). The model also outperformed previously established standard predictions of ACI 440.R-17, fib14 and CNRDT200. A comprehensive parametric study was conducted and it concluded that the implementation of RBPNN with RFE and NID, separately, is a viable tool for assessing the strength and behavior of FRP in shear strengthened beams.
机译：本文介绍了使用机器学习（ML）技术研究侧向粘结和U型包裹的纤维增强聚合物（FRP）层合板在剪切作用下增强的抗剪钢筋混凝土（RC）梁的性能。收集了一个由120个测试样本和15个参数组成的广泛数据库。使用弹性反向传播神经网络（RBPNN）作为回归工具，并在经过验证的RBPNN中使用递归特征消除（RFE）算法和神经解释图（NID）来识别对FRP剪切预测有很大影响的参数容量。结果表明，与具有原始15个参数的RBPNN（r（2）= 0.668）相比，具有所选参数的RBPNN能够更准确地预测FRP剪切能力（r（2）= 0.885; RMSE = 8.1 kN）。 ； RMSE = 16.6 kN）。该模型还优于先前建立的ACI 440.R-17，fib14和CNRDT200的标准预测。进行了全面的参数研究，得出的结论是，分别使用RFE和NID实施RBPNN是评估抗剪梁中FRP强度和性能的可行工具。

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