Cemented high-concentration backfill (CHB) is an indispensable solution for mitigating risks associated with underground mining voids and surface tailings ponds. The accurate prediction of pressure drop of CHB in pipe flow is crucial for the design of backfilling systems. In this study, full factorial loop tests were conducted to obtain observed pressure drop data and rheological parameters of CHB, while considering the variables of binder content, solid fraction, and flow velocity. The rheometer method was also utilized to acquire the rheological parameters of CHB for comparison. Three analytical models and one numerical simulation method, which are considered highly accurate in the literature, were employed to predict the pressure drop of CHB in pipe flow. The findings indicate that the Buckingham model and the Darby–Melson model produce identical results as they are fundamentally equivalent. The Swamee–Aggarwal model and the single-phase flow simulation employ a similar mechanism as the Buckingham model, albeit with minor variations in mathematical treatment. The rheological parameters of CHB obtained through the rheometer method are considerably greater than those acquired by the loop test method, leading to significantly higher predicted pressure drop values from both the three analytical models and single-phase flow simulation when compared to the measured values. Whereas the mean deviation of the three analytical models is within 6.5% when employing rheological parameters of CHB determined by the loop test, with the Swamee–Aggarwal model being the most accurate, the mean error of single-phase flow simulation remains within 10%. It is suggested that the rheological parameters of CHB be determined through small-diameter loop testing. The Buckingham model and single-phase flow simulation are subsequently recommended for predicting pressure drop in industrial straight horizontal pipelines and complex piping systems, respectively. The results of this study facilitate the selection of the simplest method for accurately predicting the pressure drop of CHB in pipe flow.

Cemented paste backfill (CPB) in metal mining offers substantial advantages in safety, environmental protection, economic viability, and operational efficiency. However, pipeline transport of CPB faces serious challenges due to pipe wear. This article provides a comprehensive review of advances related to pipeline wear. CPB-induced pipeline wear arises from the synergistic interactions of erosion, abrasion, and corrosion. Various wear test apparatuses are recommended for different study scenarios. The effects of particle characteristics, CPB properties, particle-wall contact conditions, and pipe material properties on wear in CPB transport pipelines are summarized. A detailed comparison is conducted of the similarities and differences in the effects of these four categories of factors on the wear of pipelines transporting CPB vs low-concentration slurry. The synergistic effects of abrasion-corrosion and erosion-corrosion interactions contribute significantly to the overall wear process. Methodologies for risk assessment (including machine learning algorithms such as BP neural network and multi-criteria evaluation approaches) and wear rate prediction (e.g., Finnie's erosion model, Archard's abrasion model, and phenomenological models) are outlined. Strategies for pipe wear control are listed in detail. Three specific areas are identified for advancing the understanding of wear mechanisms in CPB transport pipelines. A multifunctional Slurry Pot Tester capable of simulating pipe wear under high-pressure conditions has been developed. Notably, predicting the wear rate of full-flow pipelines based on CPB rheology theory shows substantial potential. A logical framework for wear rate prediction has been established, and two critical challenges associated with this approach have been identified. These insights inform practical applications, including optimized CPB mix designs, strategic pipeline layouts, wear-resistant materials, operational strategies, and real-time wear-risk assessment utilizing machine learning algorithms. This review provides a basis for understanding CPB pipeline wear failure and supports the development of cost-effective, durable backfill systems in mining operations.

In the context of deep mining and green low-carbon transition, this study characterizes the thermo-mechanical evolution and fracture mechanisms of cemented tailings backfill (CTB) through systematic experiments conducted at 20–60 °C across 3–28 days. Results demonstrate that strength and elastic modulus follow a unimodal dependence on temperature, peaking at 40 °C. Gaussian modeling reveals that curing times narrow the thermal tolerance window, with the elastic modulus exhibiting higher sensitivity to overheating. A consistent “pre-peak activity window” is identified in AE responses, characterized by b-value drops and an increase in tensile event proportions from 66% to 83%. A composite AE damage index (ADI) is introduced to systematically precede macroscopic failure, with thresholds of ADI ≥ 0.60 and 0.70 indicating accelerated crack propagation and imminent instability, respectively. Microstructural analysis confirms that 40 °C promotes C-S-H and fine ettringite bridging, whereas temperatures ≥ 50 °C induce Ca(OH)₂ coarsening and enhanced pore connectivity, triggering early tensile-dominated degradation. This study establishes a “temperature → hydration/porosity → AE response → mechanical evolution” pathway, providing an optimal curing window of 40 ± 5 °C and an ADI-based early-warning criterion for temperature-adaptive CTB design and on-site safety management.