Structural damage from sample preparation processes such as cutting and polishing may change the pore structure of rocks.However,changes in pore structure caused by this structural damage from crushing and its effect ...Structural damage from sample preparation processes such as cutting and polishing may change the pore structure of rocks.However,changes in pore structure caused by this structural damage from crushing and its effect on marine continental transitional shale have not been well documented.The changes of microscopic pore structure in marine continental transitional shale during the sample preparation have important research value for subsequent exploration and development of shale gas.In this study,the pore structures of transitional shale samples from the Shanxi-Taiyuan Formation of the Southern North China Basin under different degrees of damage were analyzed through low-temperature N;adsorption experiments,combined with X-ray diffraction,total organic carbon,vitrinite reflectance analysis,and scanning electron microscopy.The results showed that(1)With increasing structural damage,the specific surface area(SSA)changed within relatively tight bounds,while the pore volume(PV)varied significantly,and the growth rate(maximum)exhibited a certain critical value with the crushing mesh number increasing from 20 to 200.(2)The ratio of SSA to PV can be used as a potential proxy for evaluating the influence of changes in the pore structure.(3)Correlation analysis revealed that the microscopic pore structure of marine continental transitional shale from the Shanxi-Taiyuan Formations is mainly controlled by organic matter and clay minerals.Clay minerals play a leading role in the development of microscopic pores and changes in pore structure.展开更多
Displacement is a critical indicator for mechanical systems and civil structures.Conventional vision-based displacement recognition methods mainly focus on the sparse identification of limited measurement points,and t...Displacement is a critical indicator for mechanical systems and civil structures.Conventional vision-based displacement recognition methods mainly focus on the sparse identification of limited measurement points,and the motion representation of an entire structure is very challenging.This study proposes a novel Nodes2STRNet for structural dense displacement recognition using a handful of structural control nodes based on a deformable structural three-dimensional mesh model,which consists of control node estimation subnetwork(NodesEstimate)and pose parameter recognition subnetwork(Nodes2PoseNet).NodesEstimate calculates the dense optical flow field based on FlowNet 2.0 and generates structural control node coordinates.Nodes2PoseNet uses structural control node coordinates as input and regresses structural pose parameters by a multilayer perceptron.A self-supervised learning strategy is designed with a mean square error loss and L2 regularization to train Nodes2PoseNet.The effectiveness and accuracy of dense displacement recognition and robustness to light condition variations are validated by seismic shaking table tests of a four-story-building model.Comparative studies with image-segmentation-based Structure-PoseNet show that the proposed Nodes2STRNet can achieve higher accuracy and better robustness against light condition variations.In addition,NodesEstimate does not require retraining when faced with new scenarios,and Nodes2PoseNet has high self-supervised training efficiency with only a few control nodes instead of fully supervised pixel-level segmentation.展开更多
基金the financial support by the National Natural Science Foundation of China(Grant No.41927801)。
文摘Structural damage from sample preparation processes such as cutting and polishing may change the pore structure of rocks.However,changes in pore structure caused by this structural damage from crushing and its effect on marine continental transitional shale have not been well documented.The changes of microscopic pore structure in marine continental transitional shale during the sample preparation have important research value for subsequent exploration and development of shale gas.In this study,the pore structures of transitional shale samples from the Shanxi-Taiyuan Formation of the Southern North China Basin under different degrees of damage were analyzed through low-temperature N;adsorption experiments,combined with X-ray diffraction,total organic carbon,vitrinite reflectance analysis,and scanning electron microscopy.The results showed that(1)With increasing structural damage,the specific surface area(SSA)changed within relatively tight bounds,while the pore volume(PV)varied significantly,and the growth rate(maximum)exhibited a certain critical value with the crushing mesh number increasing from 20 to 200.(2)The ratio of SSA to PV can be used as a potential proxy for evaluating the influence of changes in the pore structure.(3)Correlation analysis revealed that the microscopic pore structure of marine continental transitional shale from the Shanxi-Taiyuan Formations is mainly controlled by organic matter and clay minerals.Clay minerals play a leading role in the development of microscopic pores and changes in pore structure.
文摘Displacement is a critical indicator for mechanical systems and civil structures.Conventional vision-based displacement recognition methods mainly focus on the sparse identification of limited measurement points,and the motion representation of an entire structure is very challenging.This study proposes a novel Nodes2STRNet for structural dense displacement recognition using a handful of structural control nodes based on a deformable structural three-dimensional mesh model,which consists of control node estimation subnetwork(NodesEstimate)and pose parameter recognition subnetwork(Nodes2PoseNet).NodesEstimate calculates the dense optical flow field based on FlowNet 2.0 and generates structural control node coordinates.Nodes2PoseNet uses structural control node coordinates as input and regresses structural pose parameters by a multilayer perceptron.A self-supervised learning strategy is designed with a mean square error loss and L2 regularization to train Nodes2PoseNet.The effectiveness and accuracy of dense displacement recognition and robustness to light condition variations are validated by seismic shaking table tests of a four-story-building model.Comparative studies with image-segmentation-based Structure-PoseNet show that the proposed Nodes2STRNet can achieve higher accuracy and better robustness against light condition variations.In addition,NodesEstimate does not require retraining when faced with new scenarios,and Nodes2PoseNet has high self-supervised training efficiency with only a few control nodes instead of fully supervised pixel-level segmentation.
