摘要
Robotics&Machine Learning Daily News的一位新闻记者兼工作人员新闻编辑每日新闻-机器学习的新数据在一份新的报告中呈现。根据Ne wsRx记者在伊利诺伊Urbana的新闻报道，研究表明：“PET(PET)纤维增强聚合物(FRR)最近被开发出来，它具有双线性的几十应力应变关系和(LRS)的断裂应变能力，本文提出了一种利用机器学习(ML)技术准确预测PET FRP约束混凝土应力应变特性的新方法。”新闻记者从伊利诺伊大学的研究中获得了一句话：“利用154个轴向压缩试验参数的综合数据集，包括圆形和非圆形情况，用于训练和测试ML模型，三种先进的ML模型，即极端梯度boosting(XGBoost)、随机森林回归(RFR)和K近邻(KNN)。”XGBoost在预测圆形和非圆形试件的力学性能方面始终优于RFR和KNN，在预测两种试件的应力-应变曲线方面表现出了优越的准确性，性能评价依赖于确定系数(2)、均方根误差(MSE)、均绝对误差(MAE)等关键指标。将XGBoost生成的应力-应变曲线与实验数据和机理模型相结合，突出了XGBoost在捕捉临界曲线点方面的优越性，强调了其准确性和稳定性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news reporting from Urbana, Illinois, by Ne wsRx journalists, research stated, “Polyethylene terephthalate (PET) fiber-reinf orced polymer (FRR) has been recently developed, which possesses a bilinear tens ile stressstrain relationship and a large rupture strain (LRS) capacity. This s tudy presents a novel approach for accurately predicting the stress-strain behav ior of PET FRP-confined concrete using machine learning (ML) techniques.” The news correspondents obtained a quote from the research from the University o f Illinois, “A comprehensive dataset comprising 154 axial compression test speci mens, including both circular and noncircular cases, was utilized for training a nd testing ML models. Three advanced ML models, namely extreme gradient boosting (XGBoost), random forest regression (RFR), and k-nearest neighbors (KNN), were applied to predict mechanical properties for both circular and noncircular speci mens. XGBoost consistently outperformed RFR and KNN, demonstrating superior accu racy in predicting stress-strain curves for both specimen types. Performance eva luation relied on key metrics such as coefficient of determination (R2), mean sq uare error (MSE), root mean square error (RMSE), and mean absolute error (MAE). Furthermore, the predicted stress-strain curves generated by XGBoost were compar ed to experimental data and a mechanism model, highlighting the superiority of X GBoost in capturing critical curve points and emphasizing its accuracy and consi stency.”
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