Abstract:If not well-managed, a mine-tailings facility may become a major source of risks, endangering the community and environment, and damaging the reputation of the minerals industry regarding sustainability. Identifying, characterizing, and mitigating the hazards and risks associated with tailings facilities have been critical to the maintenance of community-safe and environmentally sound mine-tailings facilities. Herein, a complex network model for characterizing the hazards and risks associated with the lifecycle of tailings facilities is presented. In this approach, the hazards are modeled as vertices of the complex network, and the interactions among the hazards are modeled as edges of the complex network. The complex network for modeling the hazard and risk spreading of mine-tailings impoundments is analyzed and characterized by using network metrics such as the network density, geometrical characteristics, characteristic path length, network efficiency, and clustering coefficient. The degree distribution of the network obeys a power-law distribution, indicating that the network for characterizing the risk spreading associated with a tailings facility is scale-free. According to the results of calculations and existing research results, the network is ultrasmall-world. By analyzing the change of the global network efficiency under four kinds of different methods to remove network nodes and edges, network nodes with higher between centrality (BC) are identified as critical. The removal of those critical nodes helps mitigate risks associated with a tailings facility and reveals the vulnerabilities to BC attacks.
The tailings dam system is complex, and the dam structure changes continuously over time, which can make it difficult to identify key hazards of failure and characterize the accident formation process. To solve the above problems, based on complex network theory, the paper uses the identified hazards and the relationship between hazards to construct the propagation network of tailings dam failure risk (PNTDFR). The traditional analysis methods of network centrality usually focus on one aspect of the information of the network, while it cannot take into account to absorb the advantages of different methods, resulting in the difference between identified key nodes and real key hazards. To find the key hazards of tailing dam failure, based on the characteristics of multi-stage propagation of failure risk, the paper proposes a multi-stage collaborative hazard remediation method (MCHRM) to determine the importance of hazard nodes by absorbing the advantages of different centrality methods under different hazard remediation (deletion) ratios. The paper applies the above methods to Feijão Dam I. It can be found that when the priority remediation range is increased to 45%, the key hazards obtained by the MCHRM will cover all the causes of accidents proposed by the Dam I failure investigation expert group. Besides, the paper compares the monitoring data, daily inspection results and safety evaluation information of key hazards with the ‘Grading standards of hazard indicators’, and obtains the formation process of the Dam I failure and 30 key hazards in trigger state.
The tailings dam system is complex, and the dam structure changes continuously over time, which makes it difficult to identify hazards and analyze the causes of failure accidents. This paper uses hazards to represent the nodes, and the relationship between hazards to represent the edges. Based on the complex network theory, the propagation network of tailings dam failure risk is constructed. The traditional identification methods usually focus on one aspect of the information of the network, while it cannot take into account to absorb the advantages of different methods, resulting in the lack of information, which will lead to a certain difference between identified key hazards and real key hazards. In order to solve this problem, by absorbing the advantages of different methods under different hazard remediation (deleted) ratios, combined with the characteristics of multi-stage propagation of tailings dam failure risk, this paper proposes a multi-stage collaborative hazard remediation method (MCHRM) to determine the importance of hazard nodes. When the important nodes of this network that affect the network efficiency are found, by consulting the monitoring data, daily inspection results and safety evaluation information of each hazard before the dam failure, we can determine the real cause of the accident from the above important nodes according to the grading standards of hazard indicators. In the application example of Feijão Dam I, this article compares the key hazards obtained by the above methods with the conclusions of the accident investigation team. It can be found that the above method has a very good effect on finding the key causes of tailings dam failure.
Based on the consideration of the time effect and connecting with typical instances, the modified Caputo fractional time-varying damage model is proposed in this paper. The parameters of creep constitutive model of slope are determined by using the powerful mathematical software and the influence of the parameters of time varying viscus element and damage element, α and ξ , on strain are shown. As α increases, the strain rates of the first and second stages of creep attenuation and steady-state stages gradually decrease and the strain rate in the acceleration stage is reflected in ξ . The fitting curve is in good agreement with the experimental data, which proves that the damage model proposed in this paper is reasonable and accurate. It provides useful reference and guidance for further study of slope creep failure and practical applications.
The seepage accident of a tailings pond poses a serious threat to the stability of tailings dams and the surrounding environment. To reduce the occurrence of seepage accidents, this paper studies the identification of seepage hazards, the propagation law of seepage risk, the importance of hazards, and the priority of hazard treatment. To overcome the subjectivity and omission of hazard identification, according to the complexity and dynamics of tailings seepage, this paper proposes the evidence-based identification method of three-dimensional seepage hazards (EIMTSH) to identify the hazards of the tailings seepage system and the relationship between hazards. Then, on the basis of identifying the hazards of the tailings seepage system, the propagation network of seepage risk in tailing ponds (PNSRTP) is constructed based on the complex network theory. By analyzing the characteristics of the PNSRTP, it can be found that the propagation of seepage risk is scale-free and small-world. Through the node deletion method, this paper finds that the nodes with a higher degree value can reduce the network efficiency more quickly and should be governed first. By giving priority to the treatment of hazards with higher degree, the propagation of seepage risk can be reduced more quickly and the risk management level of tailings ponds can be improved, which is helpful to realize the sustainable development of mining production.
Accidents have occurred periodically in the tailings ponds where mine solid waste is stored in recent years, and thus their safety has become one of the constraints restricting the sustainable development of the mining industry. Reclamation is an important way to treat tailings ponds, but improper reclamation methods and measures not only cannot reduce the accident risk of tailings ponds, but will further increase the pollution to the surrounding environment. The influencing factors of reclamation accidents in tailings ponds are complex, and the existing models cannot characterize them. In order to study the propagation process of tailings pond reclamation risk, this paper proposes a three-dimensional identification framework for accident hazards based on evidence (TDIFAHE) to identify all potential hazards that may occur during the reclamation stage, and obtain a list of hazards. Based on the complex network theory, this paper uses identified hazards as network nodes and the correlation between hazards as the edges of the network. Based on the identified hazard data, the evolution network of reclamation risk in tailings ponds (ENRRTP) is constructed. By analyzing the statistical characteristics of ENRRTP, it can be found that ENRRTP has small world and scale-free characteristics. The above characteristics show that the reclamation risk of tailings ponds is coupled with multiple factors and the disaster path is short. Giving priority to those hub hazards that have a dominant impact on the reclamation risk can significantly reduce the reclamation risk of the tailings pond.
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