Microwave-enhanced cemented tailings backfill: rheological properties, mechanical behaviors, and physics-informed strength prediction
Case Studies in Construction Materials, vol.24, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 24
- Publication Date: 2026
- Doi Number: 10.1016/j.cscm.2026.e06117
- Journal Name: Case Studies in Construction Materials
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
- Keywords: Cemented tailings backfill, Mechanical behaviors, Microwave heating, Physics-informed symbolic regression (PISR), Rheological properties
- Middle East Technical University Affiliated: Yes
Abstract
This study aims to enhance the safety of goafs and ensure ground pressure stability through energy-saving, clean, low-carbon, and sustainable technologies. Laboratory experiments, theoretical analysis, and machine learning methods were employed in this paper. The effects of microwave heating time on the rheological properties of slurry and the mechanical characteristics of cemented tailings backfill (CTB) were investigated. A strength prediction model was developed using the physics-informed symbolic regression (PISR) method. The findings reveal that microwave heating time significantly influenced the slurry rheology. During 0–2 min of microwave heating, electromagnetic effects dominated, disrupting the flocculation network and reducing the apparent viscosity. When extended to 2–6 min, the thermal effects accelerate hydration reactions, leading to increased viscosity and reduced fluidity. Moreover, microwave heating time enhances the early strength of CTB. accelerates the hydration rate of cementitious materials, and improves the morphology and microstructure of hydration products. However, excessive heating or low cement-sand ratios may impair later strength due to heterogeneous product formation and structural integrity issues. Based on the AI Feynman 2.0 methodology, a novel PISR framework was developed, achieving a strength prediction accuracy of 99.8%, thereby establishing a robust framework for optimizing CTB performance via microwave technology in sustainable mining.