An Intelligent Model for the Prediction of Bond Strength of FRP Bars in Concrete: A Soft Computing Approach
An Intelligent Model for the Prediction of Bond Strength of FRP Bars in Concrete: A Soft Computing Approach
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DOI：
10.3390/technologies7020042
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发表时间：
2019-06-01
期刊：
影响因子：
3.6
通讯作者：
Alavi, Amir H.
中科院分区：
文献类型：
--
作者：
Bolandi, Hamed;Banzhaf, Wolfgang;Alavi, Amir H.
准确预测纤维增强聚合物（FRP）混凝土的粘结行为在建筑行业中具有举足轻重的作用。本文提出了一种称为多基因遗传编程（MGGP）的软计算方法，用于开发混凝土中 FRP 筋粘结强度的智能预测模型。与其他类似方法相比，MGGP 方法的主要优点是它可以通过结合标准遗传规划和经典回归的功能来制定键强度。确定了许多影响 FRP 筋粘合强度的参数，并将其输入 MGGP 算法中。该算法使用实验数据库进行训练，其中包括从文献中收集的 223 个测试结果。所提出的 MGGP 模型可以准确预测混凝土中 FRP 筋的粘结强度。人们发现新定义的预测变量可以有效地表征粘合强度。推导的方程比广泛使用的美国混凝土协会（ACI）模型具有更好的性能。
Accurate prediction of bond behavior of fiber reinforcement polymer (FRP) concrete has a pivotal role in the construction industry. This paper presents a soft computing method called multi-gene genetic programming (MGGP) to develop an intelligent prediction model for the bond strength of FRP bars in concrete. The main advantage of the MGGP method over other similar methods is that it can formulate the bond strength by combining the capabilities of both standard genetic programming and classical regression. A number of parameters affecting the bond strength of FRP bars were identified and fed into the MGGP algorithm. The algorithm was trained using an experimental database including 223 test results collected from the literature. The proposed MGGP model accurately predicts the bond strength of FRP bars in concrete. The newly defined predictor variables were found to be efficient in characterizing the bond strength. The derived equation has better performance than the widely-used American Concrete Institute (ACI) model.