Identification of the interfacial cohesive law parameters of FRP strips externally bonded to concrete using machine learning techniques
Identification of the interfacial cohesive law parameters of FRP strips externally bonded to concrete using machine learning techniques
复制标题
使用机器学习技术识别外部粘合到混凝土的 FRP 条带的界面粘结规律参数
DOI：
10.1016/j.engfracmech.2021.107643
复制
发表时间：
2021-03-06
影响因子：
5.4
通讯作者：
Li, Shaofan
中科院分区：
文献类型：
--
作者：
Su, Miao;Peng, Hui;Li, Shaofan
提出了一种基于机器学习的人工神经网络（ANN）方法来自动识别纤维增强聚合物（FRP）与混凝土之间的界面粘结参数。采用内聚区模型建立了一个精细的有限元（FE）模型来模拟界面Ⅱ型断裂。根据负载数据库？通过对有限元模型产生的位移响应的分析，训练好的神经网络模型可以准确地同时识别粘聚力规律参数。此外，基于一组有限的训练数据，所提出的方法显示出较高的准确性的情况下，其界面性质属于差距内或外的训练数据集。
A machine learning-based artificial neural network (ANN) approach is developed to automatically identify the interfacial cohesive parameters between fiber-reinforced polymers (FRPs) and concrete. A refined finite element (FE) model employing a cohesive zone model is established to simulate the interfacial Mode-II fracture. According to the database of load?displacement responses generated from the FE model, the trained ANN model can accurately and concurrently identify the cohesive law parameters. Moreover, based on a finite set of training data, the proposed approach shows high accuracy for the cases whose interfacial properties fall within the gap in or outside of the training dataset.