Abood Emad A, Abdallah Marwa Hameed, Alsaadi Mahmood, Imran Hamza, Bernardo Luís Filipe Almeida, De Domenico Dario, Henedy Sadiq N
Department of Material Engineering, College of Engineering, Al-Shatrah University, Al-Shatrah 64007, Iraq.
Department of Civil Engineering, Najaf Technical Institute, Al-Furat Al-Awsat Technical University, Najaf Munazira Str., Najaf 54003, Iraq.
Materials (Basel). 2024 Aug 9;17(16):3964. doi: 10.3390/ma17163964.
Fiber-reinforced polymers (FRPs) are increasingly being used as a composite material in concrete slabs due to their high strength-to-weight ratio and resistance to corrosion. However, FRP-reinforced concrete slabs, similar to traditional systems, are susceptible to punching shear failure, a critical design concern. Existing empirical models and design provisions for predicting the punching shear strength of FRP-reinforced concrete slabs often exhibit significant bias and dispersion. These errors highlight the need for more reliable predictive models. This study aims to develop gradient-boosted regression tree (GBRT) models to accurately predict the shear strength of FRP-reinforced concrete panels and to address the limitations of existing empirical models. A comprehensive database of 238 sets of experimental results for FRP-reinforced concrete slabs has been compiled from the literature. Different machine learning algorithms were considered, and the performance of GBRT models was evaluated against these algorithms. The dataset was divided into training and testing sets to verify the accuracy of the model. The results indicated that the GBRT model achieved the highest prediction accuracy, with root mean square error (RMSE) of 64.85, mean absolute error (MAE) of 42.89, and coefficient of determination (R) of 0.955. Comparative analysis with existing experimental models showed that the GBRT model outperformed these traditional approaches. The SHapley Additive exPlanation (SHAP) method was used to interpret the GBRT model, providing insight into the contribution of each input variable to the prediction of punching shear strength. The analysis emphasized the importance of variables such as slab thickness, FRP reinforcement ratio, and critical section perimeter. This study demonstrates the effectiveness of the GBRT model in predicting the punching shear strength of FRP-reinforced concrete slabs with high accuracy. SHAP analysis elucidates key factors that influence model predictions and provides valuable insights for future research and design improvements.
由于纤维增强聚合物（FRP）具有高强度重量比和耐腐蚀性能，它们在混凝土板中越来越多地被用作复合材料。然而，与传统体系类似，FRP增强混凝土板容易发生冲切剪切破坏，这是一个关键的设计问题。现有的用于预测FRP增强混凝土板冲切剪切强度的经验模型和设计规定往往存在显著的偏差和离散性。这些误差凸显了对更可靠预测模型的需求。本研究旨在开发梯度提升回归树（GBRT）模型，以准确预测FRP增强混凝土板的抗剪强度，并解决现有经验模型的局限性。从文献中收集了一个包含238组FRP增强混凝土板实验结果的综合数据库。考虑了不同的机器学习算法，并针对这些算法评估了GBRT模型的性能。将数据集分为训练集和测试集以验证模型的准确性。结果表明，GBRT模型实现了最高的预测精度，均方根误差（RMSE）为64.85，平均绝对误差（MAE）为42.89，决定系数（R）为0.955。与现有实验模型的对比分析表明，GBRT模型优于这些传统方法。使用SHapley加法解释（SHAP）方法对GBRT模型进行解释，深入了解每个输入变量对冲切剪切强度预测的贡献。分析强调了板厚、FRP配筋率和临界截面周长等变量的重要性。本研究证明了GBRT模型在高精度预测FRP增强混凝土板冲切剪切强度方面的有效性。SHAP分析阐明了影响模型预测的关键因素，并为未来的研究和设计改进提供了有价值的见解。
