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Data management and geotechnical models

机译:数据管理和岩土技术模型

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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.
机译:股东价值是由我们作为矿业设计,计划和矿井的良好的推动。建立了良好的和解过程和报告标准,以确保实际采矿活动和不同的规划周期对于特定的矿山计划是有效和有效的。它的成功是由设计中规范的稳健性驱动的。从岩土直观的角度来看,这意味着对如何在特定设计选项中控制不同认可机制的良好理解。为了成功控制机制,了解需要潜在的失败机制,并且可以考虑作为现实改善选项的原因。这提出了具有几个挑战的岩土工程师:他们可以自信地了解岩石质量和了解如何对采矿过程中施加的装载条件做出反应?可用数据有多可靠以及在设计期间将要做的假设?可有效有效地如何评估和评估可用信息?岩土工程如何从简单地提供设计指导,成为通过风险效益方法优化业务的组成部分?解决方案从具有可靠数据开始。岩土数据不是很复杂的,然而,适当和准确的数据和数据量的可用性,驱动岩土设计中的假设次数,从而使岩土工程师面临的关键挑战之一是各种形式和质量的复杂性。在运营和项目中提供的岩土工程数据,特别是更成熟的数据。数据周围的固有不确定性会影响如何评估和评估。假设数据是可靠的,岩土工程师面临进一步的挑战,以完成可重复和可审计的设计。这首先是用于评估和评估数据的过程和软件。本文涉及建筑块,导致实际设计,讨论框架获取可靠数据并评估数据。最终,作者旨在向读者提供深入了解在Anglogold Ashanti的国际业务中实施的框架,这让我们允许从业者:确保他们的数据是可靠的。正式化可重复和可审计的流程以评估和评估其数据。使用可靠的数据和流程来评估他们的设计选项和风险。

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