ORIGINAL RESEARCH article
Front. Mater.
Sec. Computational Materials Science
Physics-Informed PINN-Transformer for Dual-Objective Prediction and Mix Optimization of Backfill Materials
Provisionally accepted
- Xi'an Kedagaoxin University, Xian, China
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To address the issues of traditional backfill material mix design relying on experience and low efficiency, this study proposes a Physics-Informed PINN-Transformer method that integrates physical constraints. A dual-task prediction framework is constructed considering both material strength and slump, embedding strength development monotonicity, convexity constraints, and slump rheological principles into model training to improve the accuracy and physical reasonableness of prediction results. Experimental results show that this method improves the MAE metric by 6.0% compared to Transformer in strength prediction and improves the slump prediction MAE metric by 6.5%. Based on prediction results and economic analysis, a multi-objective mix optimization system is established, proposing three optimization strategies adapted to different engineering requirements. This method breaks through the limitations of traditional empirical design and provides efficient and reliable technical support for scientific mix design and engineering decision-making of mine backfill materials.
Keywords: Backfill materials, Dual-Objective Prediction, Engineering economic analysis, Multi-ObjectiveMix Optimization, PINN, transformer
Received: 02 Nov 2025; Accepted: 05 Dec 2025.
Copyright: © 2025 Liang, Zhang, Xiang, Fei, Wei, Han and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yueying Zhang
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