In order to investigate and research the fatigue cracking of prestressed concrete fatigue properties and loading and stiffness degeneration process,cyclic loading tests were carried out on six prestressed concrete bea...In order to investigate and research the fatigue cracking of prestressed concrete fatigue properties and loading and stiffness degeneration process,cyclic loading tests were carried out on six prestressed concrete beams and the stiffness degradation under fatigue was investigated. A simulation model of stiffness degradation is proposed based on the stiffness analysis of the fatigue-damaged section. The elastic modulus of damaged concrete and the effective residual area of steel were introduced as well as an adjusted three-stage concrete fatigue damage evolution model. The strip method was used to analyze concrete damage due to changing stress along the depth of the beam section. The simulation and test results were compared and a method of predicting fatigue deflection was presented based on the simulation model. The predicted results were compared with that of the neural network method. It is in good agreement for the simulation results with the test results. It is only less than5% error for the simulation model which can reveal the two-stage degradation of prestressed concrete beams under cyclic loading. It is more precise for the simulation prediction method under proper conditions.展开更多
Intelligent construction has become an inevitable trend in the development of the construction industry.In the excavation project,using machine learning methods for early warning can improve construction efficiency an...Intelligent construction has become an inevitable trend in the development of the construction industry.In the excavation project,using machine learning methods for early warning can improve construction efficiency and quality and reduce the chances of damage in the excavation process.An interpretable gradient boosting based ensemble learning framework enhanced by the African Vultures Optimization Algorithm(AVOA)was proposed and evaluated in estimating the diaphragm wall deflections induced by excavation.We investigated and compared the performance of machine learning models in predicting deflections induced by excavation based on a database generated by finite element simulations.First,we exploratively analyzed these data to discover the relationship between features.We used several state-of-the-art intelligent models based on gradient boosting and several simple models for model selection.The hyperparameters for all models in evaluation are optimized using AVOA,and then the optimized models are assembled into a unified framework for fairness assessment.The comprehensive evaluation results show that the AVOA-CatBoost built in this paper performs well(RMSE=1.84,MAE=1.18,R2=0.9993)and cross-validation(RMSE=2.65±1.54,MAE=1.17±0.23,R2=0.998±0.002).In the end,in order to improve the transparency and usefulness of the model,we constructed an interpretable model from both global and local perspectives.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.5117804251308159+4 种基金51578047)the National High Technology Research and Development Program Project(Grant No.2008AA11Z102)China Railway Corporation Research and Development of Science and Technology Plan Project(Grant No.2014G004-B)China Communications Construction Co.LTD Science and Technology Research and Development Projects(Grant No.2014-ZJKJ-03)
文摘In order to investigate and research the fatigue cracking of prestressed concrete fatigue properties and loading and stiffness degeneration process,cyclic loading tests were carried out on six prestressed concrete beams and the stiffness degradation under fatigue was investigated. A simulation model of stiffness degradation is proposed based on the stiffness analysis of the fatigue-damaged section. The elastic modulus of damaged concrete and the effective residual area of steel were introduced as well as an adjusted three-stage concrete fatigue damage evolution model. The strip method was used to analyze concrete damage due to changing stress along the depth of the beam section. The simulation and test results were compared and a method of predicting fatigue deflection was presented based on the simulation model. The predicted results were compared with that of the neural network method. It is in good agreement for the simulation results with the test results. It is only less than5% error for the simulation model which can reveal the two-stage degradation of prestressed concrete beams under cyclic loading. It is more precise for the simulation prediction method under proper conditions.
基金National Natural Science Foundation of China(Grant Nos.42107214 and 52130905).
文摘Intelligent construction has become an inevitable trend in the development of the construction industry.In the excavation project,using machine learning methods for early warning can improve construction efficiency and quality and reduce the chances of damage in the excavation process.An interpretable gradient boosting based ensemble learning framework enhanced by the African Vultures Optimization Algorithm(AVOA)was proposed and evaluated in estimating the diaphragm wall deflections induced by excavation.We investigated and compared the performance of machine learning models in predicting deflections induced by excavation based on a database generated by finite element simulations.First,we exploratively analyzed these data to discover the relationship between features.We used several state-of-the-art intelligent models based on gradient boosting and several simple models for model selection.The hyperparameters for all models in evaluation are optimized using AVOA,and then the optimized models are assembled into a unified framework for fairness assessment.The comprehensive evaluation results show that the AVOA-CatBoost built in this paper performs well(RMSE=1.84,MAE=1.18,R2=0.9993)and cross-validation(RMSE=2.65±1.54,MAE=1.17±0.23,R2=0.998±0.002).In the end,in order to improve the transparency and usefulness of the model,we constructed an interpretable model from both global and local perspectives.
