Barkhordari Mohammad Sadegh, Armaghani Danial Jahed, Sabri Mohanad Muayad Sabri, Ulrikh Dmitrii Vladimirovich, Ahmad Mahmood
Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran 1591634311, Iran.
Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, 454080 Chelyabinsk, Russia.
Materials (Basel). 2022 Apr 21;15(9):3019. doi: 10.3390/ma15093019.
Fiber-reinforced polymer (FRP) has several benefits, in addition to excellent tensile strength and low self-weight, including corrosion resistance, high durability, and easy construction, making it among the most optimum options for concrete structure restoration. The bond behavior of the FRP-concrete (FRPC) interface, on the other hand, is extremely intricate, making the bond strength challenging to estimate. As a result, a robust modeling framework is necessary. In this paper, data-driven hybrid models are developed by combining state-of-the-art population-based algorithms (bald eagle search (BES), dynamic fitness distance balance-manta ray foraging optimization (dFDB-MRFO), RUNge Kutta optimizer (RUN)) and artificial neural networks (ANN) named "BES-ANN", "dFDB-MRFO -ANN", and "RUN-ANN" to estimate the FRPC interfacial-bond strength accurately. The efficacy of these models in predicting bond strength is examined using an extensive database of 969 experimental samples. Compared to the BES-ANN and dFDB-MRFO models, the RUN-ANN model better estimates the interfacial-bond strength. In addition, the SHapley Additive Explanations (SHAP) approach is used to help interpret the best model and examine how the features influence the model's outcome. Among the studied hybrid models, the RUN-ANN algorithm is the most accurate model with the highest coefficient of determination (R = 92%), least mean absolute error (0.078), and least coefficient of variation (18.6%). The RUN-ANN algorithm also outperformed mechanics-based models. Based on SHAP and sensitivity analysis method, the FRP bond length and width contribute more to the final prediction results.
纤维增强聚合物（FRP）除具有出色的拉伸强度和低自重外，还有诸多优点，包括耐腐蚀、高耐久性和易于施工，使其成为混凝土结构修复的最佳选择之一。另一方面，FRP与混凝土（FRPC）界面的粘结行为极其复杂，使得粘结强度难以估计。因此，需要一个强大的建模框架。本文通过结合基于种群的先进算法（秃鹰搜索（BES）、动态适应度距离平衡-蝠鲼觅食优化（dFDB-MRFO）、龙格-库塔优化器（RUN））和人工神经网络（ANN），开发了数据驱动的混合模型，即“BES-ANN”、“dFDB-MRFO-ANN”和“RUN-ANN”，以准确估计FRPC界面粘结强度。使用包含969个实验样本的广泛数据库检验了这些模型在预测粘结强度方面的有效性。与BES-ANN和dFDB-MRFO模型相比，RUN-ANN模型能更好地估计界面粘结强度。此外，使用SHapley加性解释（SHAP）方法来帮助解释最佳模型，并研究特征如何影响模型结果。在所研究的混合模型中，RUN-ANN算法是最准确的模型，具有最高的决定系数（R = 92%）、最小的平均绝对误差（0.078）和最小的变异系数（18.6%）。RUN-ANN算法也优于基于力学的模型。基于SHAP和敏感性分析方法，FRP粘结长度和宽度对最终预测结果的贡献更大。
