期刊论文

Su, Miao;Li, Shaofan

英文

Construction and Building Materials

0950-0618

2021

270

121456

10.1016/j.conbuildmat.2020.121456

Two collected datasets containing the SST results for FRP laminates with EB and EBG technology, reported by Ref. [37] and Ref. [38], are used as examples for comparing the selected ML approaches. Dataset 1 includes a total of 122 IBS values (referred to as cases) for FRPs externally bonded on concrete, which were reported by Wu et al. [37]. As shown in Fig. 1(a), the features (referred to as variables) of Dataset 1 include the elastic modulus (Ef), tensile strength (ff), thickness (tf) and

本校为第一且通讯机构

Accurately predicting the interfacial bond strength (IBS) between concrete and fiber reinforced polymers (FRPs) has been a challenging problem in the evaluation and maintenance of reinforced concrete (RC) structures strengthened by FRP laminates. In this work, we employ three different machine learning (ML) approaches, including a multiple linear regression (MLR), a support vector machine (SVM), and an artificial neural network (ANN), to establish the correlation between influencing variables and the IBS and then to predict the IBS. Two dataset...