准确预测用纤维增强聚合物(FRP)增强的混凝土在压缩条件下的反应对于微调结构设计至关重要。确保建筑符合安全标准,避免过度设计,从而最大限度地减少材料费用和环境影响。因此,本研究探讨了梯度提升回归树(GBRT)的可行性,随机森林(RF),人工神经网络-多层感知器(ANNMLP)和人工神经网络-径向基函数(ANNRBF)在最终预测纤维增强聚合物(FRP)约束混凝土的压缩行为中。通过将所提出的机器学习方法与涉及三种不同度量的几个经验模型进行比较,来评估所提出的机器学习方法的准确性。包括均方根误差(RMSE),平均绝对误差(MAE),和决定系数(R2)。在这项研究中,评估是使用大量的轴向压缩测试数据进行的,这些数据涉及从公开来源组装的765个FRP约束混凝土圆形标本。结果表明,所提出的GBRT算法大大提高了机器学习模型和经验方法的性能,用于预测约束强度比(f\'cc/f\'co),RMSE平均提高了17.3%,0.65%,66.81%,46.12%,46.31%,与射频相比,46.87%和69.94%,ANNMLP,ANNRBF,和四个应用的经验模型,分别。还发现,与其他模型相比,所提出的ANNMLP算法在将RMSE值降低为9.67%方面具有显着优势。11.29%,75.11%,68.83%,73.64%,与GBRT相比,分别为69.49%和83.74%,射频,ANNRBF和四个应用的经验模型,用于预测约束应变比(εcc/εco),分别。与其他方法相比,GBRT和ANNMLP在预测强度和应变比约束方面的优越性对于评估结构完整性非常重要。保证安全的功能,并简化工程计划,以便在建设项目中有效利用玻璃钢约束。
Precisely forecasting how concrete reinforced with fiber-reinforced polymers (FRP) responds under compression is essential for fine-tuning structural designs, ensuring constructions fulfill safety criteria, avoiding overdesigning, and consequently minimizing material expenses and environmental impact. Therefore, this study explores the viability of gradient boosting regression tree (GBRT), random forest (RF), artificial neural network-multilayer perceptron (ANNMLP) and artificial neural network-radial basis function (ANNRBF) in predicting the compressive behavior of fiber-reinforced polymer (FRP)-confined concrete at ultimate. The accuracy of the proposed machine learning approaches was evaluated by comparing them with several empirical models concerning three different measures, including root mean square errors (RMSE), mean absolute errors (MAE), and determination coefficient (R2). In this study, the evaluations were conducted using a substantial collection of axial compression test data involving 765 circular specimens of FRP-confined concrete assembled from published sources. The results indicate that the proposed GBRT algorithm considerably enhances the performance of machine learning models and empirical approaches for predicting strength ratio of confinement (f\'cc/f\'co) by an average improvement in RMSE as 17.3%, 0.65%, 66.81%, 46.12%, 46.31%, 46.87% and 69.94% compared to RF, ANNMLP, ANNRBF, and four applied empirical models, respectively. It is also found that the proposed ANNMLP algorithm exhibits notable superiority compared to other models in terms of reducing RMSE values as 9.67%, 11.29%, 75.11%, 68.83%, 73.64%, 69.49% and 83.74% compared to GBRT, RF, ANNRBF and four applied empirical models for predicting strain ratio of confinement (εcc/εco), respectively. The superior performance of the GBRT and ANNMLP compared to other methods in predicting the strength and strain ratio confinements is important in evaluating structural integrity, guaranteeing secure functionality, and streamlining engineering plans for effective utilization of FRP confinement in building projects.