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首页> 外文期刊>Arabian Journal for Science and Engineering >Predicting Bond Strength of FRP Bars in Concrete Using Soft Computing Techniques
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Predicting Bond Strength of FRP Bars in Concrete Using Soft Computing Techniques

机译：使用软计算技术预测混凝土中FRP棒的粘合强度

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摘要

Fiber-reinforced plastic (FRP) rebars can be the futuristic potential reinforcing material in place of mild steel (MS) rebarswhich are highly prone to corrosion. However, the bond properties of the FRP rebars are not consistent with those of mildsteel rebars. Therefore, determination of bond strength properties of FRP rebars becomes essential. In this study, an investigationwas conducted on 222 samples for bond strength data set for FRP rebars using various soft computing techniquessuch as multilinear regression, random forests, random tree, M5P, bagged-M5P tree, stochastic-M5P, and Gaussian process.Outcomes of accuracy assessment parameters, i.e., CC, MAE, and RMSE, suggest that bagged-M5P tree-based model isoutperforming than other developed models CC, MAE, and RMSE whose values are 0.9530, 0.8970, and 1.2531, respectively,for testing stages. On assessing the data and the results, it was found that GP_PUK model is more appropriate thanGP_RBF-based model for predicting the bond strength of FRP (MPa). On comparison of the RF and RT models, it wasconcluded that RF-based model performs better than RT models with CC, MAE, and RMSE values of 0.9427, 0.8674, and1.3424, respectively, for testing stages. The results of the study also suggest that bagged-M5P model attains higher correlationwith lesser RMSE values. Taylor diagram also verifies that bagged-M5P model performs better than other developedmodels. Sensitivity analysis suggests that bar embedment length to bar diameter (l/d) is the most influencing parameter forthe prediction of bond strength of FRP.
机译：纤维增强塑料（FRP）钢筋可以是未来派潜在的增强材料，代替轻度钢（MS）钢筋这很容易腐蚀。但是，FRP钢筋的债券属性与温和的债券属性不一致钢钢筋。因此，FRP钢筋的粘合强度特性的测定变得至关重要。在这项研究中，调查在222个样本上进行用于使用各种软计算技术的FRP钢筋的粘合强度数据集如多线性回归，随机林，随机树，M5p，袋装-m5p树，随机-m5p和高斯过程。准确性评估参数的结果，即CC，MAE和RMSE，表明袋装-M5P基于树的模型是优于其他开发的模型CC，MAE和RMSE，其值分别为0.9530,0.8970和1.2531，用于测试阶段。在评估数据和结果时，发现GP_Puk模型比基于GP_RBF的模型预测FRP（MPA）的键合强度。与RF和RT模型的比较，它是得出结论，基于RF的模型比RT模型更好，CC，MAE和0.9427,0.8674和0.8674和RMSE值1.3424分别用于测试阶段。该研究的结果还表明袋装-M5P模型达到更高的相关性具有较小的RMSE值。泰勒图还验证了袋装-M5P模型比其他开发的更好楷模。敏感性分析表明，条形嵌入长度（L / D）是最多的影响FRP键合强度的预测。

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