Amin Muhammad Nasir, Iqbal Mudassir, Althoey Fadi, Khan Kaffayatullah, Faraz Muhammad Iftikhar, Qadir Muhammad Ghulam, Alabdullah Anas Abdulalim, Ajwad Ali
Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
Department of Civil Engineering, University of Engineering and Technology Peshawar, Peshawar 25120, Pakistan.
Polymers (Basel). 2022 Jul 24;14(15):2992. doi: 10.3390/polym14152992.
In recent times, the use of fibre-reinforced plastic (FRP) has increased in reinforcing concrete structures. The bond strength of FRP rebars is one of the most significant parameters for characterising the overall efficacy of the concrete structures reinforced with FRP. However, in cases of elevated temperature, the bond of FRP-reinforced concrete can deteriorate depending on a number of factors, including the type of FRP bars used, its diameter, surface form, anchorage length, concrete strength, and cover thickness. Hence, accurate quantification of FRP rebars in concrete is of paramount importance, especially at high temperatures. In this study, an artificial intelligence (AI)-based genetic-expression programming (GEP) method was used to predict the bond strength of FRP rebars in concrete at high temperatures. In order to predict the bond strength, we used failure mode temperature, fibre type, bar surface, bar diameter, anchorage length, compressive strength, and cover-to-diameter ratio as input parameters. The experimental dataset of 146 tests at various elevated temperatures were established for training and validating the model. A total of 70% of the data was used for training the model and remaining 30% was used for validation. Various statistical indices such as correlation coefficient (R), the mean absolute error (MAE), and the root-mean-square error (RMSE) were used to assess the predictive veracity of the GEP model. After the trials, the optimum hyperparameters were 150, 8, and 4 as number of chromosomes, head size and number of genes, respectively. Different genetic factors, such as the number of chromosomes, the size of the head, and the number of genes, were evaluated in eleven separate trials. The results as obtained from the rigorous statistical analysis and parametric study show that the developed GEP model is robust and can predict the bond strength of FRP rebars in concrete under high temperature with reasonable accuracy (i.e., R, RMSE and MAE 0.941, 2.087, and 1.620, and 0.935, 2.370, and 2.046, respectively, for training and validation). More importantly, based on the FRP properties, the model has been translated into traceable mathematical formulation for easy calculations.
近年来，纤维增强塑料（FRP）在混凝土结构加固中的应用有所增加。FRP钢筋的粘结强度是表征FRP加固混凝土结构整体效能的最重要参数之一。然而，在温度升高的情况下，FRP增强混凝土的粘结会因多种因素而恶化，这些因素包括所用FRP钢筋的类型、直径、表面形式、锚固长度、混凝土强度和保护层厚度。因此，准确量化混凝土中的FRP钢筋至关重要，尤其是在高温情况下。在本研究中，基于人工智能（AI）的基因表达式编程（GEP）方法被用于预测高温下混凝土中FRP钢筋的粘结强度。为了预测粘结强度，我们将破坏模式温度、纤维类型、钢筋表面、钢筋直径、锚固长度、抗压强度和保护层与直径比作为输入参数。建立了146个不同高温试验的数据集用于训练和验证模型。总共70%的数据用于训练模型，其余30%用于验证。使用了各种统计指标，如相关系数（R）、平均绝对误差（MAE）和均方根误差（RMSE）来评估GEP模型的预测准确性。经过试验，最佳超参数分别为染色体数150、头部大小8和基因数4。在11次单独试验中评估了不同的遗传因素，如染色体数、头部大小和基因数。严格的统计分析和参数研究结果表明，所开发的GEP模型具有鲁棒性，能够以合理的精度预测高温下混凝土中FRP钢筋的粘结强度（即训练和验证时的R、RMSE和MAE分别为0.941、2.087和1.620，以及0.935、2.370和2.046）。更重要的是，基于FRP特性，该模型已转化为可追溯的数学公式以便于计算。
