Strength deterioration and energy dissipation characteristics of cemented backfill with different gangue particle size distributions

Yang, Yanbin; Lai, Xingping; Zhang, Yun; Shan, Pengfei; Tong, Liang; Liu, Yongzi; Zhang, Leiming; Wu, Laijun

doi:10.1016/j.jmrt.2023.06.279

Cited by 10 publications

(1 citation statement)

References 7 publications

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“…The rationale behind this lies in the fact that such scenarios often entail regular reciprocating load disturbances. Due to its inherent water absorption, high porosity, and low strength, CGC often exhibits inferior mechanical properties compared to traditional concrete [ 25 , 26 , 27 , 28 , 29 , 30 ]. The deficiency in the mechanical properties of CGC has emerged as a significant obstacle to the widespread promotion and application of this material.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Fatigue Performance of Coal Gangue Concrete (CGC) through Polypropylene Fiber Modification: Experimental Evaluation

Wu,

Jing,

et al. 2024

Polymers

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Coal gangue is a byproduct of coal mining and processing, and according to incomplete statistics, China has amassed a substantial coal gangue stockpile exceeding 2600 large mountains, which poses a serious threat to the ecological environment. Utilizing gangue as a coarse aggregate to produce gangue concrete (GC) presents a promising avenue for addressing the disposal of coal gangue; however, gangue concrete presents several challenges that need to be tackled, such as low strength and poor resistance to repeated loads. In this study, polypropylene fibers (PPFs) were incorporated into gangue concrete to enhance its utilization rate. Uniaxial compressive and repeated loading experiments were then conducted to investigate the uniaxial strength and fatigue properties of polypropylene fiber-reinforced gangue concrete (PGC) with varying gangue substitution rates (20%, 40%, and 60%) and different polypropylene fiber admixtures (0, 0.1%, 0.2%, and 0.3%). The findings indicate that incorporating gangue at a substitution rate of 40% could notably enhance the uniaxial compressive strength of PGC, resulting in a maximum increase of 19.4%. In the repeated loading experiments, the ductility of PGC was enhanced with the incorporation of PPFs, resulting in a reduction of 33.76% in the damage factor and 19.42% in residual strain for PGC-40-0.2 compared to PGC-40-0. A PPF content of 0.2% was found to be optimal for enhancing the fatigue performance of PGC. Scanning electron microscope (SEM) testing proved the improvement effect of polypropylene fiber on gangue concrete from a microscopic perspective. This study provides crucial experimental data and a theoretical foundation for the utilization of gangue concrete in complex stress environments.

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Section: Introductionmentioning

confidence: 99%

Enhancing Fatigue Performance of Coal Gangue Concrete (CGC) through Polypropylene Fiber Modification: Experimental Evaluation

Wu,

Jing,

et al. 2024

Polymers

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Reinforcing effects of polypropylene on energy absorption and fracturing of cement-based tailings backfill under impact loading

Li,

Cao,

Yilmaz

2024

Int J Miner Metall Mater

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Research on coal-rock identification method and data augmentation algorithm of comprehensive working face based on FL-Segformer

Zhang,

Tong,

Lai

et al. 2024

Int J Coal Sci Technol

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Coal-rock interface identification technology was pivotal in automatically adjusting the shearer’s cutting drum during coal mining. However, it also served as a technical bottleneck hindering the advancement of intelligent coal mining. This study aimed to address the poor accuracy of current coal-rock identification technology on comprehensive working faces, coupled with the limited availability of coal-rock datasets. The loss function of the SegFormer model was enhanced, the model’s hyperparameters and learning rate were adjusted, and an automatic recognition method was proposed for coal-rock interfaces based on FL-SegFormer. Additionally, an experimental platform was constructed to simulate the dusty environment during coal-rock cutting by the shearer, enabling the collection of coal-rock test image datasets. The morphology-based algorithms were employed to expand the coal-rock image datasets through image rotation, color dithering, and Gaussian noise injection so as to augment the diversity and applicability of the datasets. As a result, a coal-rock image dataset comprising 8424 samples was generated. The findings demonstrated that the FL-SegFormer model achieved a Mean Intersection over Union (MIoU) and mean pixel accuracy (MPA) of 97.72% and 98.83%, respectively. The FL-SegFormer model outperformed other models in terms of recognition accuracy, as evidenced by an MIoU exceeding 95.70% of the original image. Furthermore, the FL-SegFormer model using original coal-rock images was validated from No. 15205 working face of the Yulin test mine in northern Shaanxi. The calculated average error was only 1.77%, and the model operated at a rate of 46.96 frames per second, meeting the practical application and deployment requirements in underground settings. These results provided a theoretical foundation for achieving automatic and efficient mining with coal mining machines and the intelligent development of coal mines.

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Strength deterioration and energy dissipation characteristics of cemented backfill with different gangue particle size distributions

Cited by 10 publications

References 7 publications

Enhancing Fatigue Performance of Coal Gangue Concrete (CGC) through Polypropylene Fiber Modification: Experimental Evaluation

Enhancing Fatigue Performance of Coal Gangue Concrete (CGC) through Polypropylene Fiber Modification: Experimental Evaluation

Reinforcing effects of polypropylene on energy absorption and fracturing of cement-based tailings backfill under impact loading

Research on coal-rock identification method and data augmentation algorithm of comprehensive working face based on FL-Segformer

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