Conference Proceedings
The Australasian Ground Control Conference An ISRM Regional Symposium (AusRock Conference) 2022
Conference Proceedings
The Australasian Ground Control Conference An ISRM Regional Symposium (AusRock Conference) 2022
Multi-factor integrated data analytics and data-driven decision-making for ground control management
With recent advances in machine learning, data-based automated decision-making has been proven successful with promising outcomes in many industries. Its application in the mining sector can be ground-breaking; particularly the data-driven analytics that can support decision-making in the mine and consequently enhance mine safety, efficiency and sustainability. However, the current data science applications in the mining industry are mostly noncomprehensive, especially inadequate in evaluations, thus often requiring further study to be potentially beneficial to operations. Furthermore, it was found that existing literature paid limited attention to understanding the role of such techniques. Hence, in this paper we aim to build an end-to-end machine learning powered data-driven framework for intelligent geo-hazard analytics to support decision-making for underground mining data management, where multiple types of data are managed in an integrated database to support the unified machine-learning model. Here we present the framework as data-driven workflow processes, including data gathering, data preparation, data processing and data presentation. In addition, we present an application scenario of the proposed framework, where falls of ground (FoG) is managed with the historical FoG investigation reports, related panel hazard ratings, compliance data and rock mass characteristics. A demonstration can be dynamic forecasting of high-risk areas for FoG based on both spatial and temporal factors, including a 3D data visualisation. Such a scenario shows the potential of the proposed framework to establish connections among various data fields across a diverse category of data. Furthermore, the results offer important insights for ground instability management and can potentially optimise the underground operation flow.
Contributor(s):
R Liang, C Huang, C Zhang, I Canbulat, L Munsamy, R Carstens and L Prinsloo
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Multi-factor integrated data analytics and data-driven decision-making for ground control managementPDFNormal price $22.00Member price from $0.00
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- Published: 2022
- Pages: 4
- PDF Size: 0.2 Mb.
- Unique ID: P-02384-Q5B3D5