Jingping Qiu

This paper proposes a novel fusion paradigm integrating information entropy with deep learning architectures to address the challenges of uncertainty, robustness, and subjectivity in intelligent open-pit mining management. Methodologically, we develop three integrated models: an IE-LSTM model for dynamic ore blending that quantifies system uncertainty to balance efficiency with robustness, an IE-CNN model that utilizes local image entropy to filter false alarms for robust visual detection, and an IE-DNN model employing entropy-weighted objective evaluation for equipment performance. These models form the computational core of an intelligent control system deployed at the Qidashan Iron Mine. Industrial validation conducted over a two-year period demonstrated that the framework led to a blending accuracy of 93.6%, a 73.7% reduction in visual detection false alarms, and objective equipment performance scoring. System-wide outcomes included a resource utilization rate of 98.64%, improvements in overall equipment effectiveness between 4.07% and 22.70%, and cumulative direct economic benefits exceeding 43 million RMB. This research establishes a systematic framework that transitions mine operations from experience-based to data-driven intelligent control, distinguishing itself by explicitly quantifying and managing uncertainty through entropy–deep learning fusion. This provides a replicable blueprint for the mining industry’s digital transformation. It effectively addresses the high-dimensional complexity and dynamic uncertainties inherent in open-pit mining environments.

Fiber-reinforced cemented paste backfill (FR-CPB) has attracted considerable attention in modern mining applications due to its superior mechanical properties and adaptability. Despite its potential, understanding its rheological behavior remains limited, largely because of the absence of quantitative methods for assessing fiber packing behavior within CPB. This study develops a rheology-based approach to determine the maximum packing fraction of polypropylene fibers in fresh CPB, revealing that shorter fibers (3 mm) achieve a maximum packing fraction of 0.661, significantly higher than longer fibers (12 mm) with 0.534. Building on these findings, a quantitative model for the static yield stress of FR-CPB was developed, showing that under a high fiber content (0.9%) and with longer fibers (12 mm), the yield stress reached 274.34 kPa, a 40% increase compared to shorter fibers. Additionally, the study modeled the time-dependent evolution of yield stress, achieving a prediction accuracy with a correlation coefficient of 0.92. These advancements enable the optimization of FR-CPB composition, which can reduce material usage, enhance pipeline transport efficiency, and improve backfill stability in underground voids. By minimizing the risk of structural failure and optimizing resource allocation, this research provides a theoretical foundation for safer and more cost-effective mining operations.

In this paper iron tailing sand (TS) are used as aggerate to develop ultra high-performance concrete (UHPC). The mix proportion of UHPC is designed and TS were added by 25%, 50%, 75% and 100% (wt.%, i.e., weight percentage) to replace natural river sand. Firstly, the influence of TS on the slurry behavior was carried out. The experimental result indicates that with the continuously increasing content of TS, the workability of slurry decreases, while the air content increases. Considering the workability, the optimal replacing dosage of TS should be less than 50%. Then, tests for the hardened specimens were taken. The compressive behavior and micro-porosity deteriorate with increasing content of TS, and the compressive strength had a positive linear relationship with the workability, which indicated that the decline the compressive behavior is mainly due to the loss of flowability. Finally, autogenous shrinkages of UHPC with different TS dosage were also tested. At the same time, the micro-structure of specimens was discussed, which was deteriorate with the increasing dosage of TS. Therefore, comprehensively considering the compressive behavior, micro-structure and shrinkage behavior, as much as 50% of the aggregate could be replaced by TS when developing UHPC.

This work is a systematic study of the strength and ultrasonic properties of cemented paste backfill incorporating a foaming agent, known as foam-cemented paste backfill (FCPB). Based on determining the optimal admixture contents (foaming stabilizer, thickening agent, and foaming agent), a series of uniaxial compressive strength (UCS) tests were conducted to determine the relationship between the UCS of FCPB and four influencing factors, i.e., cement–tailings ratio (CTR), solid content (SC), curing time (T), and foaming agent content (FC). To analyze the sensitivity of UCS to these four factors, grey relational analysis (GRA) was introduced. Moreover, UCS results were correlated with the corresponding ultrasonic pulse velocity (UPV) parameters. The results indicate that the optimal contents of foaming stabilizer, thickening agent and foaming agent are 0.5%, 0.6%, and 1–3%, respectively. The UCS of FCPB exponentially increases with CTR and SC, while it logarithmically and linearly increases with T and FC, respectively. CTR has the most significant influence, followed by T, SC, and FC. There exists an evidently linear relationship between UPV and UCS of FCPB regardless of CTR, SC, T and FC. These results contribute to understanding the properties of hardened FCPB and to sound designs in practice.