To solve the problem of life estimation of reinforced concrete （RC） members after fire, an analysis is made of the resistance of RC members after fire. On basis of the resistance, the life of RC members after fire i...To solve the problem of life estimation of reinforced concrete （RC） members after fire, an analysis is made of the resistance of RC members after fire. On basis of the resistance, the life of RC members after fire is analyzed by using JC （Jukes and Cantor） method. Then the calculation models for the resistance and the life estimation of RC members after fire are put forward, and an example analysis proves their reliability and accuracy.展开更多
This study provides a comprehensive analysis of collision and impact problems’ numerical solutions, focusing ongeometric, contact, and material nonlinearities, all essential in solving large deformation problems duri...This study provides a comprehensive analysis of collision and impact problems’ numerical solutions, focusing ongeometric, contact, and material nonlinearities, all essential in solving large deformation problems during a collision.The initial discussion revolves around the stress and strain of large deformation during a collision, followedby explanations of the fundamental finite element solution method for addressing such issues. The hourglassmode’s control methods, such as single-point reduced integration and contact-collision algorithms are detailedand implemented within the finite element framework. The paper further investigates the dynamic responseand failure modes of Reinforced Concrete (RC) members under asymmetrical impact using a 3D discrete modelin ABAQUS that treats steel bars and concrete connections as bond slips. The model’s validity was confirmedthrough comparisons with the node-sharing algorithm and system energy relations. Experimental parameterswere varied, including the rigid hammer’s mass and initial velocity, concrete strength, and longitudinal and stirrupreinforcement ratios. Findings indicated that increased hammer mass and velocity escalated RC member damage,while increased reinforcement ratios improved impact resistance. Contrarily, increased concrete strength did notsignificantly reduce lateral displacement when considering strain rate effects. The study also explores materialnonlinearity, examining different materials’ responses to collision-induced forces and stresses, demonstratedthrough an elastic rod impact case study. The paper proposes a damage criterion based on the residual axialload-bearing capacity for assessing damage under the asymmetrical impact, showing a correlation betweendamage degree hammer mass and initial velocity. The results, validated through comparison with theoreticaland analytical solutions, verify the ABAQUS program’s accuracy and reliability in analyzing impact problems,offering valuable insights into collision and impact problems’ nonlinearities and practical strategies for enhancingRC structures’ resilience under dynamic stress.展开更多
Due to the complicated three-dimensional behaviors and testing limitations of reinforced concrete(RC)members in torsion,torsional mechanism exploration and torsional performance prediction have always been difficult.I...Due to the complicated three-dimensional behaviors and testing limitations of reinforced concrete(RC)members in torsion,torsional mechanism exploration and torsional performance prediction have always been difficult.In the present paper,several machine learning models were applied to predict the torsional capacity of RC members.Experimental results of a total of 287 torsional specimens were collected through an overall literature review.Algorithms of extreme gradient boosting machine(XGBM),random forest regression,back propagation artificial neural network and support vector machine,were trained and tested by 10-fold cross-validation method.Predictive performances of proposed machine learning models were evaluated and compared,both with each other and with the calculated results of existing design codes,i.e.,GB 50010,ACI 318-19,and Eurocode 2.The results demonstrated that better predictive performance was achieved by machine learning models,whereas GB 50010 slightly overestimated the torsional capacity,and ACI 318-19 and Eurocode 2 underestimated it,especially in the case of ACI 318-19.The XGBM model gave the most favorable predictions with R^(2)=0.999,RMSE=1.386,MAE=0.86,andλ=0.976.Moreover,strength of concrete was the most sensitive input parameters affecting the reliability of the predictive model,followed by transverse-to-longitudinal reinforcement ratio and total reinforcement ratio.展开更多
文摘To solve the problem of life estimation of reinforced concrete （RC） members after fire, an analysis is made of the resistance of RC members after fire. On basis of the resistance, the life of RC members after fire is analyzed by using JC （Jukes and Cantor） method. Then the calculation models for the resistance and the life estimation of RC members after fire are put forward, and an example analysis proves their reliability and accuracy.
基金the authority of the National Natural Science Foundation of China(Grant Nos.52178168 and 51378427)for financing this research work and several ongoing research projects related to structural impact performance.
文摘This study provides a comprehensive analysis of collision and impact problems’ numerical solutions, focusing ongeometric, contact, and material nonlinearities, all essential in solving large deformation problems during a collision.The initial discussion revolves around the stress and strain of large deformation during a collision, followedby explanations of the fundamental finite element solution method for addressing such issues. The hourglassmode’s control methods, such as single-point reduced integration and contact-collision algorithms are detailedand implemented within the finite element framework. The paper further investigates the dynamic responseand failure modes of Reinforced Concrete (RC) members under asymmetrical impact using a 3D discrete modelin ABAQUS that treats steel bars and concrete connections as bond slips. The model’s validity was confirmedthrough comparisons with the node-sharing algorithm and system energy relations. Experimental parameterswere varied, including the rigid hammer’s mass and initial velocity, concrete strength, and longitudinal and stirrupreinforcement ratios. Findings indicated that increased hammer mass and velocity escalated RC member damage,while increased reinforcement ratios improved impact resistance. Contrarily, increased concrete strength did notsignificantly reduce lateral displacement when considering strain rate effects. The study also explores materialnonlinearity, examining different materials’ responses to collision-induced forces and stresses, demonstratedthrough an elastic rod impact case study. The paper proposes a damage criterion based on the residual axialload-bearing capacity for assessing damage under the asymmetrical impact, showing a correlation betweendamage degree hammer mass and initial velocity. The results, validated through comparison with theoreticaland analytical solutions, verify the ABAQUS program’s accuracy and reliability in analyzing impact problems,offering valuable insights into collision and impact problems’ nonlinearities and practical strategies for enhancingRC structures’ resilience under dynamic stress.
基金The authors are extremely grateful to the funds including the National Natural Science Foundation of China(Grant No.51808258)the Fundamental Research Funds for the Central Universities(No.2022QN1031).
文摘Due to the complicated three-dimensional behaviors and testing limitations of reinforced concrete(RC)members in torsion,torsional mechanism exploration and torsional performance prediction have always been difficult.In the present paper,several machine learning models were applied to predict the torsional capacity of RC members.Experimental results of a total of 287 torsional specimens were collected through an overall literature review.Algorithms of extreme gradient boosting machine(XGBM),random forest regression,back propagation artificial neural network and support vector machine,were trained and tested by 10-fold cross-validation method.Predictive performances of proposed machine learning models were evaluated and compared,both with each other and with the calculated results of existing design codes,i.e.,GB 50010,ACI 318-19,and Eurocode 2.The results demonstrated that better predictive performance was achieved by machine learning models,whereas GB 50010 slightly overestimated the torsional capacity,and ACI 318-19 and Eurocode 2 underestimated it,especially in the case of ACI 318-19.The XGBM model gave the most favorable predictions with R^(2)=0.999,RMSE=1.386,MAE=0.86,andλ=0.976.Moreover,strength of concrete was the most sensitive input parameters affecting the reliability of the predictive model,followed by transverse-to-longitudinal reinforcement ratio and total reinforcement ratio.
