Nanomodification analysis of pore structure in GO-enhanced CWRB based on metal intrusion and BSE imaging with deep learning
Understanding the microstructural reinforcing mechanism benefits for graphene oxide (GO) on cemented waste rock backfill (CWRB) strengthening. However, quantitatively characterizing the reinforcing effects of GO and locating the modified nano/microscale features remain critical challenges due to the...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Case Studies in Construction Materials |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S221450952500097X |
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| author | Jiajian Yu Yi Gong Yuan Gao Hao Sui Xiaoli Xu Yanming Liu |
| author_facet | Jiajian Yu Yi Gong Yuan Gao Hao Sui Xiaoli Xu Yanming Liu |
| author_sort | Jiajian Yu |
| collection | DOAJ |
| description | Understanding the microstructural reinforcing mechanism benefits for graphene oxide (GO) on cemented waste rock backfill (CWRB) strengthening. However, quantitatively characterizing the reinforcing effects of GO and locating the modified nano/microscale features remain critical challenges due to the disorderliness of the composites. This work proposes an innovative approach based on metal intrusion technology, backscattered electron (BSE) images, and deep learning to analyze the micro/nanoscale GO-modified characteristics of the microstructure of CWRB. The results imply that by nucleation and pore-infilling effects, GO can promote the hydrate reaction and cooperate with the generated hydration products to split the large pores into independent units, thus optimizing the microstructure of CWRB. The reinforcing effects of GO tend to be more efficient under a low Talbot grading index. The proposed BSE characterization combined with the deep learning-based approach can achieve up to 91 % recognition accuracy to identify the GO-reinforced specimens. The deep Taylor decomposition (DTD) algorithm successfully locates the reinforced characteristics of the GO modification in CWRB specimens under the resolution of 340 nm. The extracting feature analysis proves the GO reinforcement is inclined in the matrix's ITZ, with about 10.3 % optimizing efficiency improvement compared with the other regions. This study not only boards the understanding of the GO reinforcing mechanisms in cement composites but could also provide insights into GO modification for structural application. |
| format | Article |
| id | doaj-art-174f01bb72da43c380996852b560717a |
| institution | Kabale University |
| issn | 2214-5095 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj-art-174f01bb72da43c380996852b560717a2025-01-26T05:03:53ZengElsevierCase Studies in Construction Materials2214-50952025-07-0122e04298Nanomodification analysis of pore structure in GO-enhanced CWRB based on metal intrusion and BSE imaging with deep learningJiajian Yu0Yi Gong1Yuan Gao2Hao Sui3Xiaoli Xu4Yanming Liu5School of Transportation and Civil Engineering, Nantong University, Nantong 226019, ChinaCollege of Arts and Sciences, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USASchool of Transportation and Civil Engineering, Nantong University, Nantong 226019, China; Department of Structural Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China; Corresponding author at: School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China.Department of Civil Engineering, Monash University, Clayton, Victoria 3800, AustraliaSchool of Transportation and Civil Engineering, Nantong University, Nantong 226019, ChinaSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria 3004, Australia; Corresponding author.Understanding the microstructural reinforcing mechanism benefits for graphene oxide (GO) on cemented waste rock backfill (CWRB) strengthening. However, quantitatively characterizing the reinforcing effects of GO and locating the modified nano/microscale features remain critical challenges due to the disorderliness of the composites. This work proposes an innovative approach based on metal intrusion technology, backscattered electron (BSE) images, and deep learning to analyze the micro/nanoscale GO-modified characteristics of the microstructure of CWRB. The results imply that by nucleation and pore-infilling effects, GO can promote the hydrate reaction and cooperate with the generated hydration products to split the large pores into independent units, thus optimizing the microstructure of CWRB. The reinforcing effects of GO tend to be more efficient under a low Talbot grading index. The proposed BSE characterization combined with the deep learning-based approach can achieve up to 91 % recognition accuracy to identify the GO-reinforced specimens. The deep Taylor decomposition (DTD) algorithm successfully locates the reinforced characteristics of the GO modification in CWRB specimens under the resolution of 340 nm. The extracting feature analysis proves the GO reinforcement is inclined in the matrix's ITZ, with about 10.3 % optimizing efficiency improvement compared with the other regions. This study not only boards the understanding of the GO reinforcing mechanisms in cement composites but could also provide insights into GO modification for structural application.http://www.sciencedirect.com/science/article/pii/S221450952500097XGraphene oxideNano modificationPore structureReinforcing feature characterizationDeep learning |
| spellingShingle | Jiajian Yu Yi Gong Yuan Gao Hao Sui Xiaoli Xu Yanming Liu Nanomodification analysis of pore structure in GO-enhanced CWRB based on metal intrusion and BSE imaging with deep learning Case Studies in Construction Materials Graphene oxide Nano modification Pore structure Reinforcing feature characterization Deep learning |
| title | Nanomodification analysis of pore structure in GO-enhanced CWRB based on metal intrusion and BSE imaging with deep learning |
| title_full | Nanomodification analysis of pore structure in GO-enhanced CWRB based on metal intrusion and BSE imaging with deep learning |
| title_fullStr | Nanomodification analysis of pore structure in GO-enhanced CWRB based on metal intrusion and BSE imaging with deep learning |
| title_full_unstemmed | Nanomodification analysis of pore structure in GO-enhanced CWRB based on metal intrusion and BSE imaging with deep learning |
| title_short | Nanomodification analysis of pore structure in GO-enhanced CWRB based on metal intrusion and BSE imaging with deep learning |
| title_sort | nanomodification analysis of pore structure in go enhanced cwrb based on metal intrusion and bse imaging with deep learning |
| topic | Graphene oxide Nano modification Pore structure Reinforcing feature characterization Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S221450952500097X |
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