An analytical solution for evaluating the safety of an exposed face in a paste backfill stope incorporating the arching phenomenon

In current underground mining, the stability of the exposed backfill face is a basic issue associated with mining design and has been the subject of considerable research in mining safety and efficiency. In this study, an improved analytical solution for evaluating the safety of vertically exposed faces in backfilling was proposed. Based on a differential slice method, the proposed solution emphasizes the arching effect as having the advantages of more rigor and wider scalability. Feasibility of the proposed solution was validated with classic centrifuge results. Good agreement between compared results indicated that the proposed solution skillfully predicts the behavior of the paste centrifuge model. Additionally, calculation of exposed face safety in sequential filling was presented. The proposed solution has practical significance in mine backfill design.

Temperature variation characteristics in flocculation settlement of tailings and its mechanism

Rapid flocculation and settlement(FS)of mine tailings is significant for the improvement and development of the filling process,whereas the settlement velocity(SV)of tailings in FS has been recognized as a key parameter to evaluate the settlement effect.However,the influence of temperature on the SV and its mechanism have not been studied.FS experiments on tailings with various ambient temperatures were carried out.The SVs of tailings with a solid waste content of 10wt%and an anionic polyacrylamide content of 20 g·t^−1 were measured at different temperatures.The SV presented an“N”-shaped variation curve as the temperature changed from 5 to 40℃.The mechanism of these results can be explained from the perspective of the electric double-layer repulsive force,molecular dynamics,and the polymer flocculation principle,as revealed from the scanning electron microscopy of floc particles.The findings will be beneficial in the design of tailings dewatering processes and save costs in the production of cemented paste backfill.

Artificial intelligence model for studying unconfined compressive performance of fiber-reinforced cemented paste backfill

To reduce the difficulty of obtaining the unconfined compressive strength(UCS) value of fiber-reinforced cemented paste backfill(CPB) and analyze the comprehensive impact of conventional and fiber variables on the compressive property, a new artificial intelligence model was proposed by combining a newly invented meta-heuristics algorithm(salp swarm algorithm, SSA) and extreme learning machine(ELM) technology. Aiming to test the reliability of that model, 720 UCS tests with different cement-to-tailing mass ratio, solid mass concentration, fiber content, fiber length, and curing time were carried out, and a strength evaluation database was collected. The obtained results show that the optimized SSA-ELM model can accurately predict the uniaxial compressive strength of the fiber-reinforced CPB, and the model performance of SSA-ELM model is better than ANN, SVR and ELM models. Variable sensitivity analysis indicates that fiber content and fiber length have a significant effect on the UCS of fiber-reinforced CPB.

Mechanics and safety issues in tailing-based backfill:A review

Voids(referred to as "stopes") are generally created during underground mining activities and can lead to both local and regional geotechnical instabilities. To assist in managing the stability of mining-related voids and improving the recovery of orebodies, tailing-based backfill technology has been widely used around the world. In the design of tailing-based backfill strategy, the specific function and engineering requirements of the filling are intimately dependent on the stress distribution within the backfilled stope. In this paper, the main mechanics involved in tailing-based backfill in underground mines, which include arching, consolidation, hydration process, and movement of surrounding rocks, are reviewed. Research on the safety of a barricade and stability of an exposed fill face are also presented. In conclusion, the backfilling process should be performed on the basis of a better understanding of the complicated interactions of the mechanisms of filling, consolidation, and hydration process(when cement is added).

Big data management in the mining industry

The mining industry faces a number of challenges that promote the adoption of new technologies.Big data,which is driven by the accelerating progress of information and communication technology,is one of the promising technologies that can reshape the entire mining landscape.Despite numerous attempts to apply big data in the mining industry,fundamental problems of big data,especially big data management(BDM),in the mining industry persist.This paper aims to fill the gap by presenting the basics of BDM.This work provides a brief introduction to big data and BDM,and it discusses the challenges encountered by the mining industry to indicate the necessity of implementing big data.It also summarizes data sources in the mining industry and presents the potential benefits of big data to the mining industry.This work also envisions a future in which a global database project is established and big data is used together with other technologies(i.e.,automation),supported by government policies and following international standards.This paper also outlines the precautions for the utilization of BDM in the mining industry.

Immobilization and leaching characteristics of fluoride from phosphogypsumbased cemented paste backfill

Phosphogypsum(PG)is a typical by-product of phosphoric acid and phosphate fertilizers during acid digestion.The application of cemented paste backfill(CPB)has been feasibly investigated for the remediation of PG.The present study evaluated fluorine immobilization mechanisms and attempted to construct a related thermodynamic and geochemical modeling to describe the related stabilization performance.Physico-chemical and mineralogical analyses were performed on PG and hardened PG-based CPB(PCPB).The correlated macro-and micro-structural properties were obtained from the analysis of the combination of unconfined compressive strength and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy imaging.Acid/base-dependent leaching tests were performed to ascertain fluoride leachab-ility.In addition,Gibbs Energy Minimization Software and PHREEQC were applied as tools to characterize the PCPB hydration and deduce its geochemical characteristics.The results proved that multiple factors are involved in fluorine stabilization,among which the calcium silicate hy-drate gel was found to be associated with retention.Although the quantitative comparison with the experimental data shows that further modi-fication should be introduced into the simulation before being used as a predictive implement to determine PG management options,the im-portance of acid/base concentration in regulating the leaching behavior was confirmed.Moreover,the modeling enabled the identification of the impurity phases controlling the stability and leachability.

Safety of barricades in cemented paste-backfilled stopes

In underground mining,there has been an increasing use of"cemented paste"for the backfilling of stopes.As this cemented paste backfill(CPB)enters the stope as a fluid,shotcrete barricades are often used to retain the fill material during and after the filling operations.However,failures of barricades have been reported around the world in recent years.This paper presents an analytical solution based on the elastic thin plate theory for calibrating the design of shotcrete barricades in underground mines using CPB.This solution was used to determine the quantitative relationships between the lateral loading from the paste and the barricade response during the backfilling process.The results show that the proposed solution agrees well with in situ data.According to the actual barricade responses,the acceptable tensile stress and an analysis method of cracks development are proposed.The proposed solution has practical significance for underground mines.

Erratum to: Big data management in the mining industry

The original version of this article unfortunately contained two mistakes due to the PDF file conversion through different softwares.The presentation of Figs.2 and 4 was incorrect.The correct versions are given below.

Editorial for special issue on mitigating the impacts of mining

Dear readers,A special issue(SI)entitled"Mitigating the Impacts of Mining"has been successfully organized at International Journal of Minerals,Metallurgy and Materials(IJMMM).The primary reason for this SI lies in that this topic is gaining increasing attentions worldwide.A collection of high-quality papers will contribute to the sustainable development of the mining industry.

Machine learning-based prediction of soil compression modulus with application of ID settlement

The compression modulus(Es)is one of the most significant soil parameters that affects the compressive deformation of geotechnical systems,such as foundations.However,it is difficult and sometime costly to obtain this parameter in engineering practice.In this study,we aimed to develop a non-parametric ensemble artificial intelligence(AI)approach to calculate the Es of soft clay in contrast to the traditional regression models proposed in previous studies.A gradient boosted regression tree(GBRT)algorithm was used to discern the non-linear pattern between input variables and the target response,while a genetic algorithm(GA)was adopted for tuning the GBRT model's hyper-parameters.The model was tested through 10-fold cross validation.A dataset of 221 samples from 65 engineering survey reports from Shanghai infrastructure projects was constructed to evaluate the accuracy of the new model5 s predictions.The mean squared error and correlation coefficient of the optimum GBRT model applied to the testing set were 0.13 and 0.91,respectively,indicating that the proposed machine learning(ML)model has great potential to improve the prediction of Es for soft clay.A comparison of the performance of empirical formulas and the proposed ML method for predicting foundation settlement indicated the rationality of the proposed ML model and its applicability to the compressive deformation of geotechnical systems.This model,however,cannot be directly applied to the prediction of Es in other sites due to its site specificity.This problem can be solved by retraining the model using local data.This study provides a useful reference for future multi-parameter prediction of soil behavior.