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The physical composition of the tailings dam. The original dam, kilas, tail floury soil, tail silty clay, and tail sand silt are represented with different colors.1 to 9 indicate region ID, their physical and mechanical parameters are listed in Table I.
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The tailings dam, a necessary facility to maintain the normal operation of mining enterprises, is a hazard source of human-caused debris flow with high potential energy. The real-time pre-alarm for the instability of tailings dam is vital to ensure the normal mining and safety of human lives and properties. Based on the internet of things and 5G wi...
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Context 1
... sensor networks are arranged to monitoring the physical data of the tailings dam. The data of the displacement of the monitoring points, the sedimentation of the monitoring points, and the water level of the monitoring holes are obtained. The physical composition of the tailings dam is shown in Fig. 4, and the main physical and mechanical parameters of the tailings dam are listed in Table 1. The variation tendency for the water level of the phreatic line is shown in Fig. ...
Context 2
... sensor networks are arranged to monitoring the physical data of the tailings dam. The data of the displacement of the monitoring points, the sedimentation of the monitoring points, and the water level of the monitoring holes are obtained. The physical composition of the tailings dam is shown in Fig. 4, and the main physical and mechanical parameters of the tailings dam are listed in Table 1. The variation tendency for the water level of the phreatic line is shown in Fig. ...
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... The ability to compare present or future signals with predefined variation ranges and, based on this comparison, generate an alarm when these ranges are exceeded or expected to be exceeded based on the calculations can be added to these functions [35]. In all of these cases, the monitoring systems involve only signal processing, although variants of these systems incorporate image processing, or the transformation of signals and images into, for example, the frequency domain [36]. ...
... Cloud computing, the on-demand use of computer system resources-especially their data storage and processing capacity-without the user's direct active management, has also been implemented in these systems [35]. This technology has generally been implemented to make data centers available from anywhere to many Internet users from any mobile or fixed device [36]. Positioning the real-time monitoring of TSFs in the cloud allows any authorized user to access the information from any device [35]. ...
The recent tailings storage facility (TSF) dam failures recorded around the world have concerned society in general, forcing the mining industry to improve its operating standards, invest greater economic resources, and implement the best available technologies (BATs) to control TSFs for safety purposes and avoid spills, accidents, and collapses. In this context, and as the era of digitalization and Industry 4.0 continues, monitoring technologies based on sensors have become increasingly common in the mining industry. This article studies the state of the art of implementing sensor technologies to monitor structural health and safety management issues in TSFs, highlighting advances and experiences through a review of the scientific literature on the topic. The methodology applied in this article adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and utilizes scientific maps for data visualization. To do so, three steps were implemented: (i) a quantitative bibliometric analysis, (ii) a qualitative systematic review of the literature, and (iii) a mixed review to integrate the findings from (i) and (ii). As a result, this article presents the main advances, gaps, and future trends regarding the main characteristics of the sensor technologies applied to monitor TSF structural health and safety management in the era of digitalization. According to the results, the existing research predominantly investigates certain TSF sensor technologies, such as wireless real-time monitoring, remote sensors (RS), unmanned aerial vehicles (UAVs), unmanned survey vessels (USVs), artificial intelligence (AI), cloud computing (CC), and Internet of Things (IoT) approaches, among others. These technologies stand out for their potential to improve the safety management monitoring of mine tailings, which is particularly significant in the context of climate change-related hazards, and to reduce the risk of TSF failures. They are recognized as emerging smart mining solutions with reliable, simple, scalable, secure, and competitive characteristics.
... With the demand for online monitoring and real-time early warning of engineering safety [36], SHM system is required to be able to automatically identify the real mode of the structure online [37]. The automatic modal parameter identification of vibration signals has two main advantages [38]: on the one hand, it reduces human participation and technical difficulty, thereby reducing costs; on the other hand, it greatly improves the efficiency of structural modal real-time monitoring and offers further possibilities. ...
... In FDD, the system's unknown input excitation force is denoted as signal x(t), while the measured output response signal is represented by y(t). The relationship between the system's output PSD matrix G yy (jω), its frequency response function matrix H(jω), and the input signal autocorrelation matrix G xx (jω) is shown in equation (36) [58,59]: ...
Modal analysis is a fundamental and essential research direction in the field of structural engineering. The ultimate goal is to determine the modal parameters of the structures. However, the existing modal analysis algorithms often require a large number of parameter adjustments and manual intervention during operation, which cannot be fully automated. In order to realize the automatic identification of modal parameters, the automatic operational modal identification method (AOMI) is proposed based on the interpolated power spectral density estimation (IPSE). To achieve more precise spectrum analysis in the low-frequency band, IPSE is employed to perform Fourier transform on the original frequency domain segment with optimized frequency resolution. This enhances the sharpness of the obtained spectrum in the low-frequency range, making peak frequencies more discernible. Subsequently, the scale-space peak picking algorithm is used to automatically obtain the peak of the power spectral density (PSD), thus enabling the automatic identification of the natural frequency. Finally, the frequency domain decomposition method (FDD) is used to identify modal parameters based on the natural frequencies. The effectiveness of AOMI is verified through the modal identification of the old steel truss bridge and the three layer framework. Under the environmental excitation, the frequencies identified by the IPSE method is close to that of FDD, Bayesian fast fourier transform (FFT) and covariance driven stochastic subspace identification (SSI-COV). Furthermore, the PSD obtained through IPSE has sharper peak than that of FDD and the Welch’s method. The damping ratio identification accuracy and modal assurance criterion (MAC) are satisfactory in AOMI, which can improve the automatic performance.
... This data can be used to monitor the dam in real-time and identify potential issues early. (ii) Remote monitoring (Youssef et al., 2019;Janet et al., 2019;Ganesh et al., 2022;Dong et al., 2017;Smith et al., 2022): IoT devices can be used to remotely monitor dams, providing engineers with realtime data. This can help reduce the need for on-site inspections and maintenance, which can be costly and time-consuming. ...
... In addition, the data from different sources sometimes are not consistent and they may tell a different story. For this reason, dam stability monitoring systems using the internet of things and cloud computing are becoming more and more popular [11,12]. One of the main tasks of an engineer is to manage all of the inconsistencies, complexity, and uncertainties and deliver solutions that will work. ...
The article presents the main functionality of the DSS class system for monitoring and stability assessment of tailings storage facilities. The presented demonstrator was deployed on the example of the Żelazny Most reservoir-the largest active facility of this type in Europe and the second one in the world. Considered a high-risk structure, it demands a system capable of online analysis of big data from various sources using machine learning, 3D modeling, or GIS mapping with the functions of interactive time-spatial analysis. In this paper, we present a complex calculation module with an integrated user interface.
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... Based on the three-dimension risk assessment principle of field theory, if the risk value of any point in the spatial assessment area can be calculated, then a three-dimensional risk field model can be established [37]. The parameters such as magnitude, peak ground velocity (PGV), peak ground acceleration (PGA), energy, etc., should be obtained in the monitored area. ...
With the gradual depletion of surface resources, rock instability caused by deep high stressand mining disturbance seriously affects safe mining. To create effective risk management, a rockinstability risk field model using microseismic monitoring data is proposed in this study. Rockinstability risk was presented visually in 3D visualization. The in-situ microseismic monitoringdata was collected and analyzed to make calculation of peak ground velocity (PGV), peak groundacceleration (PGA), energy flux, energy and seismic moment. Indicator weights of PGV, PGA, energyflux are confirmed by using the analytic hierarchy process (AHP) to calculate risk severity. The Copulafunction is then used to solve the joint probability distribution function of energy and seismic moment.Then the spatial distribution characteristics of risk can be obtained by data fitting. Subsequently, thethree-dimensional (3D) risk field model was established. Meanwhile, the established risk field isverified by comparing monitoring data without disturbance and the blasting data with disturbance.It is suggested that the proposed risk field method could evaluate the regional risk of rock instabilityreasonably and accurately, which lays a theoretical foundation for the risk prediction and managementof rock instability in deep mining.
... Determining the ML model parameters is crucial, as they directly affect the model's predictive performance [43]. The parameters can be optimized by intelligent algorithms [44]. ...
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Safety ergonomics is an important branch of safety science and environmental engineering. As humans enter the era of big data, the development of information technology has brought new opportunities and challenges to the innovation, transformation, and upgrading of safety ergonomics, as the traditional safety ergonomics theory has gradually failed to adapt to the need for safe and clean production. Intelligent safety ergonomics (ISE) is regarded as a new direction for the development of safety ergonomics in the era of big data. Unfortunately, since ISE is an emerging concept, there is no research to clarify its basic problems, which leads to a lack of theoretical guidance for the research and practice of ISE. In order to solve the shortcomings of traditional safety ergonomics theories and methods, first of all, this paper answers the basic questions of ISE, including the basic concepts, characteristics, attributes, contents, and research objects. Then, practical application functions of ISE are systematically clarified. Finally, following the life cycle of the design, implementation, operation, and maintenance of the system, it ends with a discussion of the challenges and application prospects of ISE. The conclusion shows that ISE is a cleaner research direction for ergonomics in the era of big data, that it can deepen the understanding of humans, machines, and environment systems, and it can provide a new method for further research on safety and cleaner production. Overall, this paper not only helps safety researchers and practitioners to correctly understand the concept of intelligent safety ergonomics, but it will certainly inject energy and vitality into the development of safety ergonomics and cleaner production.
... Wang et al. (2018) designed a tailings dam safety monitoring system based on the Internet of Things (IoT) and built a software platform integrated with a geographic information system. Dong et al. (2017) constructed a multi-source information monitoring system for tailings dams based on IoT and wireless networks by using multiple sensors to monitor the stability index such as saturation line, reservoir water level, and dam displacement. Dong et al. (2020a) recommended the establishment of a joint real-time monitoring and early warning system for sound-light-electricity and underground-surface-sky to avoid deficiencies in traditional monitoring methods and some coupled monitoring systems. ...
As the most severe damage form of tailings ponds, dam failure causes a serious threat and damage to the surrounding lives and environment. Therefore, based on the systematic collection and consultation of relevant data at home and abroad, the literature source analysis on tailings dam failure disasters is conducted using the CiteSpace scientometric tool. The research on tailings dam failure disasters can be classified into two stages: the preliminary germination stage and rapid development stage. Based on the scientometric knowledge map, the research hotspots of tailings dam failure disasters are analyzed and summarized as three main research directions: environmental impact, risk assessment, and mechanical behavior. With the maturity of the research on ecological problems caused by tailings leakage, ecological restoration has also gradually become a hot research topic. Through the analysis of keyword bursts and co-cited bursts, the research frontier of tailings dam break disaster is explored. “Risk management,” “real-time monitoring,” and “tailings characteristic” represent the current research frontier. Among them, risk management is burst for the longest time and is expected to be a very important research direction in the future. Finally, a tailings pond risk management and control suggestion is proposed with risk management as the core, emphasizing risk monitor, and combined with dynamic risk control, which provides a foundation for the construction of tailings dam safety management and dynamic monitoring systems.
... With the exploitation of Earth resources and the development of the mining industry, a large amount of waste residue and waste water is inevitably produced in the production process, and with the accumulation of waste residue and waste water, tailings reservoirs with high potential energy are formed (Dong et al., 2017;Rotta et al., 2020). ...
Tailings reservoirs are an inevitable part of the production process of metal mines and are typical of post-mining and post-industrial lands. Tailings reservoirs are often located in mountainous areas so earth observation and remote sensing is a necessary monitoring means. However, existing observation are generally based on satellite, air or ground platforms, Combined satellite-air-ground observation is rare, and the coordination ability of processing methods to cope with it is insufficient. Therefore, the response to sudden major environmental and disaster events is slow and lagging. In this study, inspired by the human brain, a satellite-air-ground integrated multi-source earth observation and machine learning processing system was built up for tailings reservoir stereoscopic monitoring and rapid emergency response (SAGTR). Firstly, the brain-inspired human visual multimodal data acquisition by satellite-air-ground integration platform has been established to break through the traditional single-source and single-mode data approach and involves satellite remote sensing images, UAV hyperspectral images, and ground parameters. Moreover, it is unique among current tailings reservoir monitoring systems. In general, our proposed SAGTR framework has promise for improving the comprehensive monitoring and emergency response to tailings reservoir failures.
... With the advent of real-time monitoring, seismic wave velocity tomography enables for precise, intuitive, and quantitative identification and imaging of aberrant areas. Because numerous factors impact tomographic outcomes in small-scale applications, it is vital to explore those effects statistically (e.g., Dong et al, 2017Dong et al, , 2021. To investigate the relation between seismicity and subsurface structure in this study, the Ratana oil field, ∼48 km away from the research site, was selected due to its similar trend of subsurface structure. ...
