Characterising Rock Mass Properties for Fragmentation

Modelling

Andrew Scott and Italo Onederra Fragblast 11 - 25th August, 2015

What is it all about? • Current basis of blast design? • Current blast fragmentation models • The rock mass data on which these depend • Sources of data

Exploration Operating mines Other disciplines Emerging technologies

• Dealing with variability • Extending models for specific breakage

characteristics.

Basic blast design • Most blast design rules target “satisfactory”

blast performance!

• There are myriad qualitative blast design rules that influence the geometry of a design

• The “rock mass” has little prominence in most of these design rules – the ranges in suggested factors range from “Very Hard” to “Low Strength” rock.

Diameter Burden

Explosive Density

Rock Factor

B B

S

Free Face

Free Face

Stemming

Sub-drill

Burden Hole Length

Bench Height

Spacing (S) Large diameter ANFO S = (24 to 33) D (Very hard to low strength rocks) S/B = (1.14 to 1.18 ) B Large diameter Emulsion S = (34 to 45) D (Very hard to low strength rocks) S/B = (1.13 to 1.18 ) B Small diameter B = (38 to 51) D (Very hard to low strength rocks) S/B = (1.15 to 1.31 ) B Staggered pattern S = 1.15B Common S = (1 to 2) B S/B = 1.15 recommended for broad shallow blasts where the free face is the surface S/B > 1.15 is preferred where a free face can be utilised for relief

How to predict blast fragmentation • We need a model!

How to predict blast fragmentation

• A model is a quantitative framework that presents a simplified “version of reality” that allows the relationships between “cause” and “effect” to be understood

• Two development paths – An empirical or engineering approach – A mechanistic or fundamental approach

• We need a model!

Mechanistic approaches

• Fracture mechanics, hydro-dynamics, physics, chemistry, …..sophistry…..

• Data describing dynamic rock behavior requires sophisticated tests

• Computing requirements are intensive and it is not yet possible to routinely model practical field problems using these tools.

Onederra et al (2010)

• Attempt to simulate the dynamic fracture processes

Mechanistic model parameters

Development of empirical fragmentation models

The Kuz-Ram development path

Some observations • The data provided for modelling must suit the

assumptions relied upon in the model • Avoid using nominal values for rock mass

properties to represent large volumes of rock • Acknowledge the variability found in rock mass

properties • Ensure an adequate description of rock mass

structure – ensure that the structures that control breakage have been included.

• Draw on data from other disciplines – geology, metallurgy, geotech

Exploration • Take advantage of guidance

from available surface geological mapping, aerial geophysics etc.

• Vast majority of useful data will come from drill core supplemented by chip drilling

• Drilling is controlled by resource geologists who also allow metallurgists and geotechnical engineers some access to the core – the blasting engineer is well down the pecking order!

Rock Strength • Dynamic properties are really needed but are not easy to

measure and we don’t have the design formulae to use them! • Static strength parameters are normally used in empirical

blasting models Unconfined Compressive Strength

– Often biased to high values – Limited statistical representation

Point Load Strength – Easy to measure but greater variability – Relates well to energy - breakage data

Field indices – Can vary in interpretation from person to person – Often represented as measured data in data bases

• Need to seek relationships between strength data and other rock mass properties (lithology, alteration, geophysical properties, etc) to extrapolate data beyond sample locations.

Structure • Ideally we want to know the “in-situ size

distribution” of the intact rock mass

• This is very complex!

• What does it mean to blast these materials?

• What is their RQD?

• What is their Fracture Frequency?

• What is their in-situ size distribution?

Presentation of data and statistics

Strength - Augite Basalt

0%

10%

20%

30%

40%

50%

< 25 25 - 49 50-74 75-99 100-149 >150

UCS MPa

Freq

uenc

y

The presentation of data needs to ensure that it will be interpreted correctly.

An average rock strength of 75 MPa will actually have a distribution of strengths.

The actual strength will vary with location (lithology, alteration etc) and may need to be represented on a spatial basis.

A range of strengths will result in a range of blasting results: • Most rock will blast like 75 MPa rock • 12% will blast like 125 MPa rock • 10% will blast like 20 MPa rock

Interpreting in-situ structure

0%

5%

10%

15%

20%

25%

30%

35%

40%

2 4 6 8 10 12 14 16 18 20 >20

Fracture Frequency

HMD HBM PH FR ABOVE

0%

10%

20%

30%

40%

50%

60%

70%

2 4 6 8 10 12 14 16 18 20 >20

Fracture Frequency

FT HMD HBM PO FR BELOW

0

5

10

15

20

25

0 50 100 150 200 250 300 350 400 450

Frac

ture

s pe

r met

re

Depth - m

135XC08 - Fracture Frequency

Data Acquisition • Data acquisition techniques are required that are:

Less dependent on personal effort Remote Capture variability as well as spot values

• Examples of emerging technologies for this purpose include: Blast hole drill monitoring Photogrammetry and laser based survey and mapping techniques. Down-hole and surface geophysics

Ramos, Hatherly and Montiero, ACFR, 2009

SMCS

Strata Characterisation While Drilling

Has been technically feasible for many years, but only now being used routinely in a few operations.

Remote Structural Mapping Systems work, but current developments focus on making the process less technically demanding in both the field and office.

Appropriate analytical tools are needed to interpret the data and present it in a suitable form for blast design.

Fine Breakage Behaviour • Rock breaks in distinctive patterns when the fragments are unaffected by

macro-structures • Michaux demonstrated self-similar behaviour between crushing, small

scale blasting and production blasting for the same rock

• Michaux argues that results from fine crushing tests can be used to extend fragmentation curves below 1 mm in size.

Michaux developed a crushing test procedure to characterise this fine breakage behaviour

Model extended to cover dust…

Fines from inherent clays

• Cerro Colorado mine in Chile had excessive fines in their copper heap leach

• Clay minerals were bound within the rock matrix and liberated with rock breakage

• “Dean David Index” (DDI) developed from a specific crushing test to quantify the clay generated during blasting

• DDI was incorporated into the fragmentation and crushing models to guide changes to practice to limit fines generation.

Conclusions

• Best fragmentation model is one formulated to specifically address the problem being studied

• Essential that the rock mass properties used are consistent with the underlying assumptions relied on by the model

• Rock mass data needs to be shared between disciplines • Important to adequately account for variability • All models need to be calibrated and checked for reality • Established models can be extended to account for properties or

mechanisms not usually addressed.