Cakiroglu Celal, Islam Kamrul, Bekdaş Gebrail, Kim Sanghun, Geem Zong Woo
Department of Civil Engineering, Turkish-German University, Istanbul 34820, Turkey.
Department of Civil, Geological and Mining Engineering, Polytechnique Montréal, Montreal, QC H3C 3A7, Canada.
Materials (Basel). 2022 Apr 8;15(8):2742. doi: 10.3390/ma15082742.
Fiber-reinforced polymer (FRP) rebars are increasingly being used as an alternative to steel rebars in reinforced concrete (RC) members due to their excellent corrosion resistance capability and enhanced mechanical properties. Extensive research works have been performed in the last two decades to develop predictive models, codes, and guidelines to estimate the axial load-carrying capacity of FRP-RC columns. This study utilizes the power of artificial intelligence and develops an alternative approach to predict the axial capacity of FRP-RC columns more accurately using data-driven machine learning (ML) algorithms. A database of 117 tests of axially loaded FRP-RC columns is collected from the literature. The geometric and material properties, column shape and slenderness ratio, reinforcement details, and FRP types are used as the input variables, while the load-carrying capacity is used as the output response to develop the ML models. Furthermore, the input-output relationship of the ML model is explained through feature importance analysis and the SHapely Additive exPlanations (SHAP) approach. Eight ML models, namely, Kernel Ridge Regression, Lasso Regression, Support Vector Machine, Gradient Boosting Machine, Adaptive Boosting, Random Forest, Categorical Gradient Boosting, and Extreme Gradient Boosting, are used in this study for capacity prediction, and their relative performances are compared to identify the best-performing ML model. Finally, predictive equations are proposed using the harmony search optimization and the model interpretations obtained through the SHAP algorithm.
由于纤维增强聚合物（FRP）钢筋具有出色的耐腐蚀能力和增强的力学性能，在钢筋混凝土（RC）构件中越来越多地被用作钢筋的替代品。在过去二十年中，已经进行了大量的研究工作来开发预测模型、规范和指南，以估计FRP-RC柱的轴向承载能力。本研究利用人工智能的力量，开发了一种替代方法，使用数据驱动的机器学习（ML）算法更准确地预测FRP-RC柱的轴向承载力。从文献中收集了一个包含117个轴向加载FRP-RC柱试验的数据库。几何和材料特性、柱的形状和长细比、配筋细节以及FRP类型用作输入变量，而承载能力用作输出响应来开发ML模型。此外，通过特征重要性分析和SHapely加法解释（SHAP）方法解释了ML模型的输入-输出关系。本研究使用了八个ML模型，即核岭回归、套索回归、支持向量机、梯度提升机、自适应提升、随机森林、分类梯度提升和极端梯度提升来进行承载力预测，并比较它们的相对性能以确定性能最佳的ML模型。最后，使用和声搜索优化和通过SHAP算法获得的模型解释提出了预测方程。
