Data management and geotechnical models

Abstract

Shareholder value is driven by how well we, as the mining industry, can design, plan and mine. Well established reconciliation processes and reporting standards are utilised to ensure that the actual mining activities and the different planning cycles are effective and efficient for the specific life-of-mine plan. Its success is driven by the robustness of the specifications set out in the design. From a geotechnical perspective this means that there is a good understanding on how the different identified mechanisms can be controlled in a specific design option. In order to successfully control the mechanisms, an understanding of what drives the potential failure mechanisms and what can be considered as realistic amelioration options is required. This presents the geotechnical engineers with several challenges:  How confident can they be in their knowledge of the rock mass and understanding on how it will react to the loading conditions imposed during mining?  How reliable is the data available and what assumptions will have to be made during design?  How effectively and efficiently can the available information be assessed and evaluated?  How can geotechnical engineering move from simply providing design guidance, to becoming an integral part of optimising the business through a risk-benefit approach? The solution begins with having reliable data. Geotechnical data is not very complex, however, the availability of suitable and accurate data, and quantum of data, drives the number of assumptions within a geotechnical design and thus the complexity thereof. One of the key challenges facing geotechnical engineers is the various forms and quality of geotechnical data available at operations and projects, in particular the more mature ones. The inherent uncertainty surrounding the data impacts how it can be evaluated and assessed. Assuming that the data is reliable, the geotechnical engineer faces a further challenge to complete a repeatable and auditable design. This starts with the processes and software used to evaluate and assess the data. This paper deals with the building blocks leading up to the actual design, discussing frameworks to obtain reliable data and to assess the data. Ultimately, the authors aim to provide the reader with an insight into the frameworks being implemented in AngloGold Ashanti's International Operations, which allow the practitioners to:  Ensure that their data is reliable.  Formalise a repeatable and auditable process to evaluate and assess their data.  Use the reliable data and processes to assess their design options and risks.

Using qualitative risk assessment as a leading indicator for geotechnical risk in mining

“…The problem statement and associated requirements and constraints dictate how detailed and comprehensive the collection of information, or overall data collection, will be for any specific scenario. In the questionnaire, the model input data, consisting of geology, structure, rock mass, and hydrology (Read & Stacey 2009) is assessed in terms of the data management process flow ( Figure 2) as described by Hamman et al (2017).…”

“…It is costly to collect most types of geotechnical data, and for many geotechnical parameters there is only limited opportunity to do so. All data follows a process flow, starting with collection and ending with a data package used during evaluations (Hamman et al 2017). In order for data to be considered reliable, four key requirements have to be met:…”

“…There are several underlying activities that need to be implemented to achieve these requirements. The process flow illustrated in Figure 4, developed for AngloGold Ashanti's international operations, is discussed in more detail in Hamman et al (2017). However, a brief summary of the main points are given as Figure 4.…”

The role of the geotechnical model for rapid integration in managing operational geotechnical risk

“…A well-constructed rock mass model, similar to a typical ore resource model, forms the basis for the development of a GMRi (Hamman et al 2017). Rock mass characteristics from geotechnical core logging and underground mapping, where available, were converted to describe the rock mass in terms of Barton's Q classification (Barton et al 1974) and a calculated UCS from equotip testing.…”

Geotechnical information system employs spatial analysis to utilize information gathered from Cone Penetration Testing (CPT)

“…This valuable data can significantly reduce the time and cost of a project during the feasibility study phase. Geotechnical engineers rely on soil formation and physical properties to make informed decisions and designs, and information technology applications, such as Geographic Information Systems and database systems, are essential for effectively managing and interpreting data in the geotechnical field [24][25][26][27][28][29][30].…”