The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix(FRCM).through both physical models and Deep Neu...The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix(FRCM).through both physical models and Deep Neural Network model(artificial neural network(ANN)with double and triple hidden layers).The database of 330 samples collected for the training model contains many important parameters,i.e.,section type(circle or square),corner radius rc,unconfined concrete strength fco,thickness nt,the elastic modulus of fiber Ef,the elastic modulus of mortar Em.The results revealed that the proposed ANN models well predicted the compressive strength of FRCM with high prediction accuracy.The ANN model with double hidden layers(APDL-1)was shown to be the best to predict the compressive strength of FRCM confined columns compared with the ACI design code and five physical models.Furthermore,the results also reveal that the unconfined compressive strength of concrete,type of fiber mesh for FRCM,type of section,and the corner radius ratio,are the most significant input variables in the efficiency of FRCM confinement prediction.The performance of the proposed ANN models(including double and triple hidden layers)had high precision with R higher than 0.93 and RMSE smaller than 0.13,as compared with other models from the literature available.展开更多
It is well known that structural properties degrade under long-term environmental exposure and loading and that the degradation rate is controlled by inherent physical and chemical degradation mechanisms.The elucidati...It is well known that structural properties degrade under long-term environmental exposure and loading and that the degradation rate is controlled by inherent physical and chemical degradation mechanisms.The elucidation of the degradation mechanisms and the realization of effective long-term performance degradation control have been a research frontier in the field of civil engineering in recent years.Currently,the major topics that concern this research frontier include revealing the physical and chemical mechanisms of structural performance evolution under long-term environmental exposure and loading and developing structural performance degradation control technologies based on fiber-reinforced materials,for example,fiber-reinforced polymers(FRPs)and fabric-reinforced cementitious matrix(FRCM).In addition,there are novel structural performance control technologies,such as using a shape memory alloy(SMA)and self-healing concrete.This paper presents a brief state-of-the-art review of this topic,and it is expected to provide a reference for subsequent research.展开更多
基金This research was funded by the Vietnam National Foundation for Science and Technology Development(NAFOSTED)(No.107.01-2017.03).
文摘The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix(FRCM).through both physical models and Deep Neural Network model(artificial neural network(ANN)with double and triple hidden layers).The database of 330 samples collected for the training model contains many important parameters,i.e.,section type(circle or square),corner radius rc,unconfined concrete strength fco,thickness nt,the elastic modulus of fiber Ef,the elastic modulus of mortar Em.The results revealed that the proposed ANN models well predicted the compressive strength of FRCM with high prediction accuracy.The ANN model with double hidden layers(APDL-1)was shown to be the best to predict the compressive strength of FRCM confined columns compared with the ACI design code and five physical models.Furthermore,the results also reveal that the unconfined compressive strength of concrete,type of fiber mesh for FRCM,type of section,and the corner radius ratio,are the most significant input variables in the efficiency of FRCM confinement prediction.The performance of the proposed ANN models(including double and triple hidden layers)had high precision with R higher than 0.93 and RMSE smaller than 0.13,as compared with other models from the literature available.
基金The authors would like to acknowledge financial support from the Natural Science Foundation of Jiangsu Province(BK20190369 and BK20191146)the National Natural Science Foundation of China(Grant Nos.51908118 and 51525801)the Fundamental Research Funds for the Central Universities(2242020K40087).
文摘It is well known that structural properties degrade under long-term environmental exposure and loading and that the degradation rate is controlled by inherent physical and chemical degradation mechanisms.The elucidation of the degradation mechanisms and the realization of effective long-term performance degradation control have been a research frontier in the field of civil engineering in recent years.Currently,the major topics that concern this research frontier include revealing the physical and chemical mechanisms of structural performance evolution under long-term environmental exposure and loading and developing structural performance degradation control technologies based on fiber-reinforced materials,for example,fiber-reinforced polymers(FRPs)and fabric-reinforced cementitious matrix(FRCM).In addition,there are novel structural performance control technologies,such as using a shape memory alloy(SMA)and self-healing concrete.This paper presents a brief state-of-the-art review of this topic,and it is expected to provide a reference for subsequent research.
