Rock mass assessment — what goes wrong?

Duran, Alex

doi:10.36487/acg_rep/1604_32_duran

Cited by 2 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some quantitative approaches have been utilized to gain more exact values by using quantitative methods, such as RQD and block volume (Duran, 2016). Hoek et al (1998) correlated the GSI with cohesion and angle of friction.…”

Section: Discussionmentioning

confidence: 99%

Influence of Rock Properties in Estimating Rock Strength for Shallow Underground Structures in Weak Rocks

Agustawijaya¹

2018

Indonesian J. Geosci.

View full text Add to dashboard Cite

Two popular rock strength criteria, the linear Coulomb and non-linear Hoek-Brown, are widely used in underground designs. These two criteria may be applied differently depending on rock conditions. Weak rocks may have different properties compared to hard rocks. Both criteria have been applied in a current research to practically determine the applicability of the criteria in estimating the strength of weak rock masses of five shallow underground structures. Results show that both criteria are able to model the strength of the five weak rock masses, but as expected the criteria provide quite different values for each type of rocks. The strength of rock masses around underground structures depends on uniaxial compressive strength and confinement; but the linear criterion very much depends on shear characteristics of rock materials. Whereas, the non-linear criterion relies on the geological strength index (GSI). Although the GSI may have served practical descriptions for rock masses, some difficulties were found when using the GSI for very weak pyroclastic rocks. The GSI seems to provide underestimated indexes for these rock types. Estimations show that the non-linear criterion may not really exhibit curved strength envelopes rather linear in some sense, for five weak rock masses. Thus in general, when an underground structure is reasonably shallow, has a lack of confinement, and where the shear behaviour dominates rock failures, the linear criterion is more preferable than the non-linear criterion in modelling the strength of weak rock masses.

show abstract

Section: Discussionmentioning

confidence: 99%

Influence of Rock Properties in Estimating Rock Strength for Shallow Underground Structures in Weak Rocks

Agustawijaya¹

2018

Indonesian J. Geosci.

View full text Add to dashboard Cite

show abstract

“…The International Society for Rock Mechanics (ISRM) suggested methods (Ulusay & Hudson 2007, 2016 provides a comprehensive basis for such a standard. Consideration should be given to the physical means for how the data will be obtained, such as physical core log or Acoustic Televiewer (ATV).…”

Section: Data Collection Standardsmentioning

confidence: 99%

“…Unless samples that failed on structure and from unreliable axial splitting are removed, the data will not reflect the true UCS of the rock type. Furthermore, the testing methods employed to obtain the data need to be as per the ISRM suggested methods (Ulusay & Hudson 2007, 2016. Even though a recognised, rigorous routine is used to obtain the data, it does not mean that the data will reflect reality.…”

Section: Accuracymentioning

confidence: 99%

“…Cai et al 2004proposed a numerical methodology for the comparison of spacing and the joint condition rating. Once again this follows the principles of Duran (2016), where interval data can be referenced in the context for the graphical framework presented in Cai et al (2004). The example ( Figure 7) highlights one of the features of the QGIS software, in the presentation of a heat map as an underlay to the point data, to allow assessment of density or clustering of data points.…”

Section: Cai Gsi Graphmentioning

confidence: 99%

“…In a similar manner, as highlighted in Duran (2016), whereby the axes of the GSI graphs are related to numerical equivalents of block size and joint conditions, a Q-factor graph has been graphically referenced, with block size represented by the ratio of RQD/Jn on the y-axis and the ratio of Jr/Ja on the x-axis, allowing for analysis of the distribution of Q Prime (Q'). Figure 8 presents an example of this graph.…”

Section: Q'-factor Graphmentioning

confidence: 99%

See 2 more Smart Citations

Data management and geotechnical models

Hamman¹,

Plooy²,

Seery³

2017

Proceedings of the International Conference on Deep and High Stress Mining

View full text Add to dashboard Cite

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.

show abstract

Rock mass assessment — what goes wrong?

Abstract: The use of the Geological Strength Index (GSI) to assess rock mass quality in combination with the Hoek-Brown rock mass shear strength criteria has become widely used within the geotechnical fraternity to assess stability for mine pit slopes.

Cited by 2 publications

References 15 publications

Influence of Rock Properties in Estimating Rock Strength for Shallow Underground Structures in Weak Rocks

Influence of Rock Properties in Estimating Rock Strength for Shallow Underground Structures in Weak Rocks

Data management and geotechnical models

Contact Info

Product

Resources

About