Matches in SemOpenAlex for { <https://semopenalex.org/work/W4379056901> ?p ?o ?g. }
- W4379056901 endingPage "284" @default.
- W4379056901 startingPage "273" @default.
- W4379056901 abstract "Accurately identifying the high-temperature history experienced by rocks is essential for understanding their behaviour and predicting properties. However, current approaches are limited by the heterogeneity of rocks, test scale and costs. Here, we proposed an economical, efficient and accurate approach to identifying the rocks after high-temperature deterioration via deep learning. This deep learning-based method exhibited superior abilities in distinguishing the heat-treated rock. Using a scanning electron microscopy (SEM) image covering a size of 14.6 μm × 14.6 μm, the high-temperature deterioration history of rocks can be recognized with an accuracy of 80.2%. Features such as cracks, rock patterns, and cleavage steps in SEM images would further improve the recognition accuracy. For example, SEM images with higher fractal box dimensions show a higher recognization accuracy, especially for temperatures under 600 °C. Besides, using the deep Taylor decomposition algorithm, the high-temperature deterioration regions of the rocks in the microscale were successfully located, extracted, and characterized for the first time. This study highlights the vast potential of the deep learning-based approach in damage deterioration identification of rock after high temperature, which significantly extends the application of deep learning in underground projects." @default.
- W4379056901 created "2023-06-02" @default.
- W4379056901 creator A5005283972 @default.
- W4379056901 creator A5011359844 @default.
- W4379056901 creator A5039660301 @default.
- W4379056901 creator A5045950851 @default.
- W4379056901 creator A5056747450 @default.
- W4379056901 creator A5092069684 @default.
- W4379056901 date "2023-07-01" @default.
- W4379056901 modified "2023-10-01" @default.
- W4379056901 title "Recognition of rock materials after high-temperature deterioration based on SEM images via deep learning" @default.
- W4379056901 cites W1983272964 @default.
- W4379056901 cites W1985736729 @default.
- W4379056901 cites W1985984046 @default.
- W4379056901 cites W1992162669 @default.
- W4379056901 cites W2013534382 @default.
- W4379056901 cites W2015368901 @default.
- W4379056901 cites W2032790021 @default.
- W4379056901 cites W2195388612 @default.
- W4379056901 cites W2401839597 @default.
- W4379056901 cites W2587828093 @default.
- W4379056901 cites W2611896437 @default.
- W4379056901 cites W2659748680 @default.
- W4379056901 cites W2768188264 @default.
- W4379056901 cites W2783256174 @default.
- W4379056901 cites W2789403976 @default.
- W4379056901 cites W2789643644 @default.
- W4379056901 cites W2790195878 @default.
- W4379056901 cites W2791475906 @default.
- W4379056901 cites W2793420978 @default.
- W4379056901 cites W2795063467 @default.
- W4379056901 cites W2804800641 @default.
- W4379056901 cites W2807122532 @default.
- W4379056901 cites W2883672710 @default.
- W4379056901 cites W2896567611 @default.
- W4379056901 cites W2908794876 @default.
- W4379056901 cites W2917129338 @default.
- W4379056901 cites W2920898355 @default.
- W4379056901 cites W2936638828 @default.
- W4379056901 cites W2943245563 @default.
- W4379056901 cites W2944677899 @default.
- W4379056901 cites W2945479473 @default.
- W4379056901 cites W2945571166 @default.
- W4379056901 cites W2979647032 @default.
- W4379056901 cites W2991338081 @default.
- W4379056901 cites W2998566301 @default.
- W4379056901 cites W3001320114 @default.
- W4379056901 cites W3005105502 @default.
- W4379056901 cites W3094471572 @default.
- W4379056901 cites W3096113367 @default.
- W4379056901 cites W3112982354 @default.
- W4379056901 cites W3113317199 @default.
- W4379056901 cites W3118352424 @default.
- W4379056901 cites W3129402520 @default.
- W4379056901 cites W3129819102 @default.
- W4379056901 cites W3134037881 @default.
- W4379056901 cites W3137589278 @default.
- W4379056901 cites W3143855377 @default.
- W4379056901 cites W3155090629 @default.
- W4379056901 cites W3174574064 @default.
- W4379056901 cites W3178629447 @default.
- W4379056901 cites W3191766937 @default.
- W4379056901 cites W3195144499 @default.
- W4379056901 cites W4200221270 @default.
- W4379056901 cites W4210279748 @default.
- W4379056901 cites W4213165316 @default.
- W4379056901 cites W4224305738 @default.
- W4379056901 doi "https://doi.org/10.1016/j.jmrt.2023.05.271" @default.
- W4379056901 hasPublicationYear "2023" @default.
- W4379056901 type Work @default.
- W4379056901 citedByCount "17" @default.
- W4379056901 countsByYear W43790569012023 @default.
- W4379056901 crossrefType "journal-article" @default.
- W4379056901 hasAuthorship W4379056901A5005283972 @default.
- W4379056901 hasAuthorship W4379056901A5011359844 @default.
- W4379056901 hasAuthorship W4379056901A5039660301 @default.
- W4379056901 hasAuthorship W4379056901A5045950851 @default.
- W4379056901 hasAuthorship W4379056901A5056747450 @default.
- W4379056901 hasAuthorship W4379056901A5092069684 @default.
- W4379056901 hasBestOaLocation W43790569011 @default.
- W4379056901 hasConcept C108583219 @default.
- W4379056901 hasConcept C127313418 @default.
- W4379056901 hasConcept C134306372 @default.
- W4379056901 hasConcept C145420912 @default.
- W4379056901 hasConcept C153180895 @default.
- W4379056901 hasConcept C154945302 @default.
- W4379056901 hasConcept C159985019 @default.
- W4379056901 hasConcept C179428855 @default.
- W4379056901 hasConcept C192562407 @default.
- W4379056901 hasConcept C199289684 @default.
- W4379056901 hasConcept C26771246 @default.
- W4379056901 hasConcept C33923547 @default.
- W4379056901 hasConcept C40636538 @default.
- W4379056901 hasConcept C41008148 @default.
- W4379056901 hasConceptScore W4379056901C108583219 @default.
- W4379056901 hasConceptScore W4379056901C127313418 @default.
- W4379056901 hasConceptScore W4379056901C134306372 @default.
- W4379056901 hasConceptScore W4379056901C145420912 @default.