基于广义回归神经网络的纤维增强聚合物复合材料约束损伤混凝土强度预测  

Strength prediction of fiber reinforced polymer composite confined damaged concrete using general regression neural network

作　　者：曹玉贵[1] 赵国旭 尹亚运 Yugui CAO;Guoxu ZHAO;Yayun YIN(Hubei Key Laboratory of Roadway Bridge and Structure Engineering,Wuhan University of Technology,Wuhan 430070,China;School of Civil Engineering and Architecture,Wuhan University of Technology,Wuhan 430070,China)

机构地区：[1]武汉理工大学道路桥梁与结构工程湖北省重点实验室,武汉430070 [2]武汉理工大学土木工程与建筑学院,武汉430070

出　　处：《复合材料学报》2021年第5期1623-1628,共6页Acta Materiae Compositae Sinica

基　　金：国家自然科学基金(51808419);湖北省自然科学基金(2019CFB217);湖北省重大专项研发计划(2018AAA001);武汉理工大学自主创新基金(2019IVA089)。

摘　　要：纤维增强聚合物复合材料(FRP)约束损伤混凝土抗压强度模型对于混凝土柱类构件的修复和加固具有重要指导意义。现有FRP修复混凝土的强度模型适用条件有限,同一模型不能同时应用于不同强弱约束、不同强度混凝土、不同倒角混凝土的强度预测。本文根据广义回归神经网络(GRNN)的特点,基于46个FRP强约束损伤混凝土方柱、210个FRP强约束损伤混凝土圆柱和35个FRP弱约束损伤混凝土圆柱的试验数据,建立了GRNN抗压强度模型,对FRP约束损伤混凝土的强度进行预测,并与现有模型的预测结果进行对比分析,结果表明,新建立的GRNN模型能够准确地预测FRP约束损伤混凝土的强度。The compressive strength of damaged concrete reinforced with fiber reinforced polymer composite(FRP) has an important guiding significance in repairing of concrete columns. However, the existing model cannot capture the compressive strength of FRP hardening and softening confined damaged concrete with circular and square cross section. In order to fill this gap, an experimental database of 46 FRP hardening confined square damaged concrete, 210 FRP hardening confined circular damaged concrete and 35 FRP softening confined circular damaged concrete was established. Based on the characteristics of generalized regression neural network(GRNN) and database, the GRNN compressive strength model of FRP confined damaged concrete was developed. The GRNN model was compared with the existing model. The results show that the GRNN model can accurately predict the strength of FRP confined damaged concrete columns.

关 键 词：广义回归神经网络(GRNN) FRP约束损伤混凝土 抗压强度 强弱约束 不同截面形状 

分 类 号：TU375[建筑科学—结构工程]