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.

中文翻译：

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尾矿储存设施安全监测传感器技术：工业4.0时代的解决方案

最近世界各地记录的尾矿储存设施（TSF）大坝溃坝引起了整个社会的关注，迫使采矿业提高其运营标准，投入更多的经济资源，并实施最佳可用技术（BAT）来控制尾矿储存设施以达到安全目的避免溢出、事故和倒塌。在此背景下，随着数字化和工业4.0时代的继续，基于传感器的监控技术在采矿业中变得越来越普遍。本文研究了采用传感器技术来监测 TSF 结构健康和安全管理问题的最新技术，通过回顾有关该主题的科学文献，重点介绍了进展和经验。本文中应用的方法遵循系统评价和荟萃分析的首选报告项目 (PRISMA) 指南，并利用科学地图进行数据可视化。为此，实施了三个步骤：（i）定量文献计量分析，（ii）对文献进行定性系统回顾，以及（iii）综合回顾以整合（i）和（ii）的研究结果。因此，本文介绍了数字化时代用于监测 TSF 结构健康和安全管理的传感器技术的主要特征的主要进展、差距和未来趋势。根据结果​​，现有研究主要研究某些TSF传感器技术，例如无线实时监测、遥感器（RS）、无人机（UAV）、无人勘测船（USV）、人工智能（AI）、云计算（CC）和物联网（IoT）方法等。这些技术因其在改善尾矿安全管理监测方面的潜力而脱颖而出，这在气候变化相关危害的背景下尤为重要，并可降低 TSF 故障的风险。它们被认为是新兴的智能采矿解决方案，具有可靠、简单、可扩展、安全和竞争的特点。