FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4376272803> ?p ?o ?g. }

• W4376272803 endingPage "102001" @default.
• W4376272803 startingPage "102001" @default.
• W4376272803 abstract "Reasonable dam materials’ gradation design for asphalt-core rock-fill dams is one of the main ways to control permeability. It is a challenge to test whether it can meet the requirements of dam construction. The computer vision method provides a new idea for asphalt-core rock-fill dam material gradation testing. However, due to the characteristics of densely overlapping and multi-scale sizes of dam material particles, the traditional image segmentation methods and algorithms cannot achieve accurate segmentation of dam materials’ images, and it is hard to apply the segmentation result to quantify the gradation curve. In this research, the enhanced Cascade Mask R-CNN with ResNet and PAFPN (Path Aggregation Feature Pyramid Networks) is proposed. Multi-scale features extracted by ResNet and feature ensemble can be realized using PAFPN. Data augmentation (DA) and online hard example mining (OHEM) are also applied in segmentation model training. Moreover, the GCNet is proposed to calibrate the gradation curve. The nonlinear relationship between the real gradation and the one based on the segmentation results can be revealed and the model of dam materials’ gradation analysis can be established. In the research, the enhanced Cascade Mask R-CNN can achieve 84.2 mAP, which is higher than that of Cascade Mask R-CNN with 74.9 mAP. The effectiveness of the proposed module and training strategies is proved using ablation experiments. The average error of each level for the gradation calibration using GCNet is 0.55%, 1.87%, 2.22%, 1.18%, and 2.42% respectively. The accuracy can meet the requirements of hydraulic engineering construction, which verifies the effectiveness of the GCNet network for gradation calibration, and the research provides a new method and technology for intelligent gradation testing of the asphalt-core rock-fill dam." @default.
• W4376272803 created "2023-05-13" @default.
• W4376272803 creator A5015105684 @default.
• W4376272803 creator A5019277923 @default.
• W4376272803 creator A5029471902 @default.
• W4376272803 creator A5082117229 @default.
• W4376272803 date "2023-04-01" @default.
• W4376272803 modified "2023-10-18" @default.
• W4376272803 title "Intelligent analysis method of dam material gradation for asphalt-core rock-fill dam based on enhanced Cascade Mask R-CNN and GCNet" @default.
• W4376272803 cites W1977114321 @default.
• W4376272803 cites W1979491360 @default.
• W4376272803 cites W1981537987 @default.
• W4376272803 cites W1993131354 @default.
• W4376272803 cites W2020648798 @default.
• W4376272803 cites W2035419878 @default.
• W4376272803 cites W2046126508 @default.
• W4376272803 cites W2046138051 @default.
• W4376272803 cites W2054297546 @default.
• W4376272803 cites W2057995542 @default.
• W4376272803 cites W2520857975 @default.
• W4376272803 cites W2767793647 @default.
• W4376272803 cites W2905163589 @default.
• W4376272803 cites W2919115771 @default.
• W4376272803 cites W2922309069 @default.
• W4376272803 cites W2944668789 @default.
• W4376272803 cites W2963516811 @default.
• W4376272803 cites W2968422936 @default.
• W4376272803 cites W2989676862 @default.
• W4376272803 cites W3010144798 @default.
• W4376272803 cites W3014818928 @default.
• W4376272803 cites W3024003582 @default.
• W4376272803 cites W3044334638 @default.
• W4376272803 cites W3049161665 @default.
• W4376272803 cites W3081596635 @default.
• W4376272803 cites W3082945422 @default.
• W4376272803 cites W3097930952 @default.
• W4376272803 cites W3109292884 @default.
• W4376272803 cites W3150252068 @default.
• W4376272803 cites W3156930162 @default.
• W4376272803 cites W3166101415 @default.
• W4376272803 cites W3168244790 @default.
• W4376272803 cites W3209641232 @default.
• W4376272803 cites W4206395781 @default.
• W4376272803 cites W4214703446 @default.
• W4376272803 cites W4221122912 @default.
• W4376272803 cites W4228999611 @default.
• W4376272803 cites W4281727886 @default.
• W4376272803 doi "https://doi.org/10.1016/j.aei.2023.102001" @default.
• W4376272803 hasPublicationYear "2023" @default.
• W4376272803 type Work @default.
• W4376272803 citedByCount "0" @default.
• W4376272803 crossrefType "journal-article" @default.
• W4376272803 hasAuthorship W4376272803A5015105684 @default.
• W4376272803 hasAuthorship W4376272803A5019277923 @default.
• W4376272803 hasAuthorship W4376272803A5029471902 @default.
• W4376272803 hasAuthorship W4376272803A5082117229 @default.
• W4376272803 hasConcept C127413603 @default.
• W4376272803 hasConcept C138885662 @default.
• W4376272803 hasConcept C142575187 @default.
• W4376272803 hasConcept C153180895 @default.
• W4376272803 hasConcept C154945302 @default.
• W4376272803 hasConcept C205649164 @default.
• W4376272803 hasConcept C2524010 @default.
• W4376272803 hasConcept C2776401178 @default.
• W4376272803 hasConcept C2778755073 @default.
• W4376272803 hasConcept C29314403 @default.
• W4376272803 hasConcept C31972630 @default.
• W4376272803 hasConcept C33923547 @default.
• W4376272803 hasConcept C34146451 @default.
• W4376272803 hasConcept C41008148 @default.
• W4376272803 hasConcept C41895202 @default.
• W4376272803 hasConcept C42360764 @default.
• W4376272803 hasConcept C58640448 @default.
• W4376272803 hasConcept C89600930 @default.
• W4376272803 hasConceptScore W4376272803C127413603 @default.
• W4376272803 hasConceptScore W4376272803C138885662 @default.
• W4376272803 hasConceptScore W4376272803C142575187 @default.
• W4376272803 hasConceptScore W4376272803C153180895 @default.
• W4376272803 hasConceptScore W4376272803C154945302 @default.
• W4376272803 hasConceptScore W4376272803C205649164 @default.
• W4376272803 hasConceptScore W4376272803C2524010 @default.
• W4376272803 hasConceptScore W4376272803C2776401178 @default.
• W4376272803 hasConceptScore W4376272803C2778755073 @default.
• W4376272803 hasConceptScore W4376272803C29314403 @default.
• W4376272803 hasConceptScore W4376272803C31972630 @default.
• W4376272803 hasConceptScore W4376272803C33923547 @default.
• W4376272803 hasConceptScore W4376272803C34146451 @default.
• W4376272803 hasConceptScore W4376272803C41008148 @default.
• W4376272803 hasConceptScore W4376272803C41895202 @default.
• W4376272803 hasConceptScore W4376272803C42360764 @default.
• W4376272803 hasConceptScore W4376272803C58640448 @default.
• W4376272803 hasConceptScore W4376272803C89600930 @default.
• W4376272803 hasFunder F4320321001 @default.
• W4376272803 hasFunder F4320325573 @default.
• W4376272803 hasLocation W43762728031 @default.
• W4376272803 hasOpenAccess W4376272803 @default.
• W4376272803 hasPrimaryLocation W43762728031 @default.
• W4376272803 hasRelatedWork W1669643531 @default.

• next