The effective management of tailings storage facilities (TSFs) is crucial for managing the environmental risks due to variables, such as erosion, leakage, structural failures, and potential collapse, as well as risks to the environment and mining operations. This review provides a comprehensive analysis of current TSF monitoring strategies, focusing on traditional methods like on-site inspections, alongside advanced technologies, including Internet of Things (IoT) networks, satellite remote sensing (SRS), and unmanned aerial vehicles (UAVs)-based remote sensing. IoT networks facilitate continuous data collection, SRS enables large-scale surface monitoring, and UAVs offer high-resolution imaging of inaccessible areas. Each monitoring technique is evaluated for its effectiveness in assessing TSF failure, highlighting its strengths and limitations in ensuring TSF stability and guiding future improvements. The review also explores the broader implications of these evaluations, emphasising the need for advancements in data integration, intelligent risk detection, and visualisation methods to address the increasing complexity and regulatory demands in the mining sector.
In the control of contaminated soil, cut-off walls are often used beneath existing structures or in areas where contaminated soils are widespread, created by mixing soil in situ with cement. These cut-off walls are designed to limit the spread of contaminants into the groundwater and therefore require low permeability. Recently, the permeability of cement-treated soil subjected to the flow of a magnesium solution was investigated. As a result, it was shown that the permeability decreased significantly with increasing flow rate of the magnesium solution, suggesting that magnesium salts contribute to the reduction of permeability in cement-treated soil. In this study, cement-treated soil with added magnesium sulfate was investigated to develop a low-permeability material. As a result, it was confirmed that the permeability of the cement-treated soil decreased as the magnesium sulfate content increased. Instrumental analysis showed that the amount of ettringite formation increased with higher magnesium sulfate content. The reduction in the permeability of the specimens with magnesium sulfate was attributed to the filling of the pores and the increased complexity of the pore structure caused by the formation of ettringite.
This paper presents a comprehensive statistical analysis to evaluate the uncertainties inherited in the lower–upper matrix (LU) decomposition-based random field theory, which is commonly used in geotechnical engineering to model the random spatial variability of soil properties. To facilitate this study, several realisations of Monte Carlo simulation coupled with LU decomposition-based random field theory are made for varying values of input statistical parameters. Thereafter, the statistical parameters are estimated for each simulated random field using the method of maximum likelihood. The results obtained in this study indicate that the LU decomposition-based random field theory does not yield the random field as it is anticipated to be for given input parameters. Ideally, the statistical parameters estimated from the simulated random fields should be nearly equal to the values of input parameters; however, there is significant randomness in the statistical parameters of the simulated random fields. Furthermore, this paper also presents the analysis to evaluate the factors affecting the uncertainties in the random fields simulated by LU decomposition-based theory by varying the input statistical and geometrical parameters.
This study determined the magnitude of the hydraulic anisotropy ratio (rk = kh/kv) in undisturbed tailings samples with varying fines contents. Constant-head permeability tests were performed under different consolidation stress levels and hydraulic gradients, evaluating the influence of effective stress, void ratio, and fines content on the compression index (Cc), the coefficient of consolidation (Cv), and permeability (k). To obtain rk, the experimental method of cutting samples in different directions was implemented. This method has been applied to obtain rk in various materials such as clays and rocks, but it is not known to have been used for tailings. The literature reports few rk values for tailings. According to different researchers, rk in tailings can vary from 2 for fairly uniform dams to 100 in transition zones, due to the intercalation of fine and coarse particles. In this study, rk values ranging from 1 to 10 were obtained, identifying fines content as the main factor controlling hydraulic anisotropy. Furthermore, three correlations were defined between fines content and initial and final void ratios.
This study aimed at investigating the seismic performance of hollow fibre-reinforced polymer (FRP) piles compared to traditional piles in soft clay deposits using shaking table tests. A laminar shear box with dimensions of 1.0 x 1.0 x 1.0 m was used to contain the soil medium and allow it to respond similarly to the free field. Two types of composite group piles (2 x 2) made of carbon fibre-reinforced polymer and glass fibre-reinforced polymer, along with aluminium piles, were manufactured and embedded as end-bearing piles within the soil. Several monitoring instruments were used to observe the soil-pile response under variety of ground motions adopted from the 2010 Val-des-Bois Earthquake in Canada and the 1995 Kobe Earthquake. Seismic response of the foundation was strongly dependent on the stiffness provided by the soil, which was a function of the degree of softening and intensity of shaking. The foundation motion of model piles was higher than those of the free field mainly because of strong kinematic soil-pile interaction. Among the model piles, FRP piles revealed lower foundation motion due to their lower flexural stiffness compared to aluminium piles. This could make FRP piles a remarkably viable option compared to conventional piles under seismic loading.
