The layered pavements usually exhibit complicated mechanical properties with the effect of complex material properties under external environment.In some cases,such as launching missiles or rockets,layered pavements a...The layered pavements usually exhibit complicated mechanical properties with the effect of complex material properties under external environment.In some cases,such as launching missiles or rockets,layered pavements are required to bear large impulse load.However,traditional methods cannot non-destructively and quickly detect the internal structural of pavements.Thus,accurate and fast prediction of the mechanical properties of layered pavements is of great importance and necessity.In recent years,machine learning has shown great superiority in solving nonlinear problems.In this work,we present a method of predicting the maximum deflection and damage factor of layered pavements under instantaneous large impact based on random forest regression with the deflection basin parameters obtained from falling weight deflection testing.The regression coefficient R^(2)of testing datasets are above 0.94 in the process of predicting the elastic moduli of structural layers and mechanical responses,which indicates that the prediction results have great consistency with finite element simulation results.This paper provides a novel method for fast and accurate prediction of pavement mechanical responses under instantaneous large impact load using partial structural parameters of pavements,and has application potential in non-destructive evaluation of pavement structure.展开更多
Machine learning is a good method for predicting fracture by integrating multi-source information. Post-stack seismic attributes are commonly used to predict medium to large fractures, while pre-stack seismic attribut...Machine learning is a good method for predicting fracture by integrating multi-source information. Post-stack seismic attributes are commonly used to predict medium to large fractures, while pre-stack seismic attributes are proven to be more sensitive to small and micro sized fractures through forward modeling. Using machine learning algorithm to fuse information from different scales to predict fracture can greatly improve the accuracy of fracture prediction. On the basis of In-Situ stress prediction, the paper conducted post-stack seismic attribute analysis and pre-stack seismic attribute analysis, further studied on the sensitivity of seismic attributes to fracture and selected sensitive attributes, used the sensitivity log of well-bore fractures as the target log for learning, ultimately obtained a comprehensive body of fracture. Through blind well verification, the prediction results match well with the we1l data and the prediction results is highly consistent with the production data. The results of fracture prediction are reliable, and the research method has certain reference significance for fracture prediction.展开更多
基金Project supported in part by the National Natural Science Foundation of China(Grant No.12075168)the Fund from the Science and Technology Commission of Shanghai Municipality(Grant No.21JC1405600)。
文摘The layered pavements usually exhibit complicated mechanical properties with the effect of complex material properties under external environment.In some cases,such as launching missiles or rockets,layered pavements are required to bear large impulse load.However,traditional methods cannot non-destructively and quickly detect the internal structural of pavements.Thus,accurate and fast prediction of the mechanical properties of layered pavements is of great importance and necessity.In recent years,machine learning has shown great superiority in solving nonlinear problems.In this work,we present a method of predicting the maximum deflection and damage factor of layered pavements under instantaneous large impact based on random forest regression with the deflection basin parameters obtained from falling weight deflection testing.The regression coefficient R^(2)of testing datasets are above 0.94 in the process of predicting the elastic moduli of structural layers and mechanical responses,which indicates that the prediction results have great consistency with finite element simulation results.This paper provides a novel method for fast and accurate prediction of pavement mechanical responses under instantaneous large impact load using partial structural parameters of pavements,and has application potential in non-destructive evaluation of pavement structure.
文摘Machine learning is a good method for predicting fracture by integrating multi-source information. Post-stack seismic attributes are commonly used to predict medium to large fractures, while pre-stack seismic attributes are proven to be more sensitive to small and micro sized fractures through forward modeling. Using machine learning algorithm to fuse information from different scales to predict fracture can greatly improve the accuracy of fracture prediction. On the basis of In-Situ stress prediction, the paper conducted post-stack seismic attribute analysis and pre-stack seismic attribute analysis, further studied on the sensitivity of seismic attributes to fracture and selected sensitive attributes, used the sensitivity log of well-bore fractures as the target log for learning, ultimately obtained a comprehensive body of fracture. Through blind well verification, the prediction results match well with the we1l data and the prediction results is highly consistent with the production data. The results of fracture prediction are reliable, and the research method has certain reference significance for fracture prediction.
