Adaptive drive-based integration technique for predicting rheological and mechanical properties of fresh gangue backfill slurry
The gangue grouting and backfilling (GGB) in the subsequent space of coal mining provides an effective way for green disposal of coal gangue. This study proposes a newly integrated intelligent model of mixing entropy- and congestion degree-based particle swarm optimization support vector regression...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
|
| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509525001445 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849721564812017664 |
|---|---|
| author | Chaowei Dong Jianfei Xu Nan Zhou Jixiong Zhang Hao Yan Zejun Li Yuzhe Zhang |
| author_facet | Chaowei Dong Jianfei Xu Nan Zhou Jixiong Zhang Hao Yan Zejun Li Yuzhe Zhang |
| author_sort | Chaowei Dong |
| collection | DOAJ |
| description | The gangue grouting and backfilling (GGB) in the subsequent space of coal mining provides an effective way for green disposal of coal gangue. This study proposes a newly integrated intelligent model of mixing entropy- and congestion degree-based particle swarm optimization support vector regression (MC-PSO-SVR) to predict the yield stress, plastic viscosity, fluidity and uniaxial compressive strength (UCS) of fresh gangue backfill slurry (FGBS). Analysis demonstrates that the particle swarm optimal (PSO) algorithm based on adaptive adjustment strategy can effectively optimize the hyperparameters of support vector regression (SVR), and the MC-PSO-SVR model exhibits better predictive capability (R2> 0.88) and lower error coefficients (MAE, RSE, and RMSE values approaching 0) and narrower widths of 95 % confidence intervals for yield stress, plastic viscosity, fluidity, and UCS. Furthermore, the R2 value surpasses 0.95 for external datasets, indicating enhanced generalization capability and robustness. Model significance analysis indicates that water content, 0–0.075 mm gangue sand (GS), and 0.075–0.15 mm GS are key factors controlling the rheological properties and mechanical strength of FGBS. The content of 0–0.075 mm GS and 0.075–0.15 mm GS has a significant positive effect on yield stress, plastic viscosity, and UCS, while showing a negative effect on fluidity. Nevertheless, the impact of water content exhibits a contrasting outcome. This is mainly because fine-grained (0–0.15 mm) GS improves the uniformity and density between particles in the slurry, while excess water content disrupts the balance provided by fine-grained GS, and the uneven distribution of particles in the slurry can be caused. This research facilitates the assessment and regulation of engineering properties of FGBS in practical engineering applications to meet different working conditions, providing a theoretical basis for further ratio optimization of backfill materials. |
| format | Article |
| id | doaj-art-ab1b4989032344fba87bd82efe3fd1c6 |
| institution | DOAJ |
| issn | 2214-5095 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj-art-ab1b4989032344fba87bd82efe3fd1c62025-08-20T03:11:37ZengElsevierCase Studies in Construction Materials2214-50952025-07-0122e0434610.1016/j.cscm.2025.e04346Adaptive drive-based integration technique for predicting rheological and mechanical properties of fresh gangue backfill slurryChaowei Dong0Jianfei Xu1Nan Zhou2Jixiong Zhang3Hao Yan4Zejun Li5Yuzhe Zhang6School of Mines, China University of Mining and Technology, Xuzhou 221116, China; State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, Xuzhou 221116, China; Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou 221116, China; State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, Xuzhou 221116, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou 221116, China; State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, Xuzhou 221116, China; Jiangsu Key Laboratory for Clean Utilization of Carbon Resources, Xuzhou 221116, China; Corresponding author at: School of Mines, China University of Mining and Technology, Xuzhou 221116, China.School of Mines, China University of Mining and Technology, Xuzhou 221116, China; State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, Xuzhou 221116, China; Jiangsu Key Laboratory for Clean Utilization of Carbon Resources, Xuzhou 221116, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou 221116, China; State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, Xuzhou 221116, China; Jiangsu Key Laboratory for Clean Utilization of Carbon Resources, Xuzhou 221116, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou 221116, China; State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, Xuzhou 221116, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou 221116, China; State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, Xuzhou 221116, ChinaThe gangue grouting and backfilling (GGB) in the subsequent space of coal mining provides an effective way for green disposal of coal gangue. This study proposes a newly integrated intelligent model of mixing entropy- and congestion degree-based particle swarm optimization support vector regression (MC-PSO-SVR) to predict the yield stress, plastic viscosity, fluidity and uniaxial compressive strength (UCS) of fresh gangue backfill slurry (FGBS). Analysis demonstrates that the particle swarm optimal (PSO) algorithm based on adaptive adjustment strategy can effectively optimize the hyperparameters of support vector regression (SVR), and the MC-PSO-SVR model exhibits better predictive capability (R2> 0.88) and lower error coefficients (MAE, RSE, and RMSE values approaching 0) and narrower widths of 95 % confidence intervals for yield stress, plastic viscosity, fluidity, and UCS. Furthermore, the R2 value surpasses 0.95 for external datasets, indicating enhanced generalization capability and robustness. Model significance analysis indicates that water content, 0–0.075 mm gangue sand (GS), and 0.075–0.15 mm GS are key factors controlling the rheological properties and mechanical strength of FGBS. The content of 0–0.075 mm GS and 0.075–0.15 mm GS has a significant positive effect on yield stress, plastic viscosity, and UCS, while showing a negative effect on fluidity. Nevertheless, the impact of water content exhibits a contrasting outcome. This is mainly because fine-grained (0–0.15 mm) GS improves the uniformity and density between particles in the slurry, while excess water content disrupts the balance provided by fine-grained GS, and the uneven distribution of particles in the slurry can be caused. This research facilitates the assessment and regulation of engineering properties of FGBS in practical engineering applications to meet different working conditions, providing a theoretical basis for further ratio optimization of backfill materials.http://www.sciencedirect.com/science/article/pii/S2214509525001445Grouting and backfillingDisposal of coal gangueFresh gangue backfill slurryEngineering propertiesMachine learning algorithms |
| spellingShingle | Chaowei Dong Jianfei Xu Nan Zhou Jixiong Zhang Hao Yan Zejun Li Yuzhe Zhang Adaptive drive-based integration technique for predicting rheological and mechanical properties of fresh gangue backfill slurry Case Studies in Construction Materials Grouting and backfilling Disposal of coal gangue Fresh gangue backfill slurry Engineering properties Machine learning algorithms |
| title | Adaptive drive-based integration technique for predicting rheological and mechanical properties of fresh gangue backfill slurry |
| title_full | Adaptive drive-based integration technique for predicting rheological and mechanical properties of fresh gangue backfill slurry |
| title_fullStr | Adaptive drive-based integration technique for predicting rheological and mechanical properties of fresh gangue backfill slurry |
| title_full_unstemmed | Adaptive drive-based integration technique for predicting rheological and mechanical properties of fresh gangue backfill slurry |
| title_short | Adaptive drive-based integration technique for predicting rheological and mechanical properties of fresh gangue backfill slurry |
| title_sort | adaptive drive based integration technique for predicting rheological and mechanical properties of fresh gangue backfill slurry |
| topic | Grouting and backfilling Disposal of coal gangue Fresh gangue backfill slurry Engineering properties Machine learning algorithms |
| url | http://www.sciencedirect.com/science/article/pii/S2214509525001445 |
| work_keys_str_mv | AT chaoweidong adaptivedrivebasedintegrationtechniqueforpredictingrheologicalandmechanicalpropertiesoffreshganguebackfillslurry AT jianfeixu adaptivedrivebasedintegrationtechniqueforpredictingrheologicalandmechanicalpropertiesoffreshganguebackfillslurry AT nanzhou adaptivedrivebasedintegrationtechniqueforpredictingrheologicalandmechanicalpropertiesoffreshganguebackfillslurry AT jixiongzhang adaptivedrivebasedintegrationtechniqueforpredictingrheologicalandmechanicalpropertiesoffreshganguebackfillslurry AT haoyan adaptivedrivebasedintegrationtechniqueforpredictingrheologicalandmechanicalpropertiesoffreshganguebackfillslurry AT zejunli adaptivedrivebasedintegrationtechniqueforpredictingrheologicalandmechanicalpropertiesoffreshganguebackfillslurry AT yuzhezhang adaptivedrivebasedintegrationtechniqueforpredictingrheologicalandmechanicalpropertiesoffreshganguebackfillslurry |