Prof G.K.Pradhan successfully demonstrted the use of empty plastic bottles in explosives column during blasting. Resulted is savings in blasting cost and immense benefits attached environmental problems.

1. Productivity Improvement in Aggregate Quarry vis-a-vis Fragmentation
by Blasting
Ramesh MurldiharBhatawdekar
Centre of Tropical Geoengineering (GEOTROPIK), UniversitiTeknologi Malaysia, 81310, Johor, Malaysia
EdyTonnizam Mohamad
Centre of Tropical Geoengineering (GEOTROPIK), UniversitiTeknologi Malaysia, 81310, Johor, Malaysia
DanialJahedArmaghani
Department of Civil and Environmental Engineering, Amirkabir University of Technology, 15914, Tehran,
Iran.
SaksaridChangthan
Siam City Concrete Co, Bangkok 10110, Thailand
Gyandera Kumar Pradhan
AKS University, Madhya Pradesh, India
ABSTRACT: Blasting for primary fragmentation, needs of an aggregate quarry has a unique role as compared
with normal blasting in mining. The percentage recovery of aggregates, dictate the overall mine economics.
Blasting design program has been a complex in mine feasibility stage as well as throughout the life of the
mine. Selection of drilling, explosives, initiation system over the years has attracted a lot of scientific inputs.
While geology and GSI have an overriding effect, inputs like mean block size, RQD%, powder factor, MC,
ratios of hole diameter, spacing, stemming with burden, hold key to the success for determining mean
fragment size. R2
value with ANN is 0.845 as compared to 0.674 with MVRA.Excavator output improved
from 200 TPH to 245 TPH with reduction in mean fragment size from 0.27 m to 0.13 m. Productivity
improvement without compromising on HSE norms and regulatory framework in Thailand Quarry are
presented.
Keywords: Artificial neural network (ANN), Geotechnical strength Index (GSI), RQD%, Powder factor,
Maximum charge per delay (MC)
1 INTRODUCTION
In Thailand, world class infrastructure is
developed in and around Bangkok. Number of
construction aggregate quarries and limestone
quarries are developed as shown in Figure 1.
Construction aggregates consists of Permian
Ordovician limestone, Cretaceous granite and
Tertiary basalt. Typical large quarries utilize 200
mm diameter drill hole which carries powder factor
from 0.4 to 0.66 kg/ cu m (Tangchawal, 2017).
Maximum charge per delay varies from 64.31 to
160.85 kg per delay for a distance of 150 m and
534.91 to 643.40 kg per delay for a distance of
300m. For smaller quarries burden to spacing pattern
of 1.5m X 1.8 m to 3.5 m X 3.5 m patterns are used.
For larger quarries burden to spacing pattern from 5
m x 6 m to 6 m X 8 m are used.
The aggregate quarry under this study has annual
production capacity of 2.5 MTPA and has expanded
its capacity to 5 MTPA. The existing quarry use 76
mm diameter DTH drills, 2.2 Cum back hoe
excavators and 25 T dump trucks. Feed size to
primary crusher is 800 mm. The objective of this
study is listed as follows: a) to classify the rock mass
based on Geological Strength Index (GSI) b) to
investigate key parameters influencing blast
fragmentation through multi variate regression
analysis (MVRA) and artificial neural network
(ANN) c) to evaluate the efficiency of excavator
with fragmentation.
Analysis by previous researchers (Ash, 1968;
Hustrulid, 1999; Jimeno et al., 1995) shows that
fragmentation depends upon rock mass properties
and geological discontinuities, properties of
explosives, blast hole diameter, burden, spacing,
bench height, stemming length, blast hole alignment
and deviation, type of blast hole pattern rectangular
or staggered, blast hole sub drilling, Prediction of
blast fragmentation depends upon three main factors
– rock mass properties, instant release of explosive
energy and blast design.

2. Figure 1: Small and large aggregate quarries around Bangkok,
(Tangchawal, 2017)
In situ block size and distribution with
discontinuities play an important role in blastability
of rock mass (Wang et al.,1991. 1992; Lu and
Latham 1996; Lu 1997). As per blast fragmentation
study by Kulatilake et al. (2010, 2012) and
Mehrdanesh et al. (2017), mean in situ block size
plays crucial role in predicting mean blast fragment
size. RQD is a measure of unweathered drill core
longer than 10 cms (Deere, 1966). RQD being easy,
quick and hence used to indicate joint density for
core hole logs. For blasting face, to establish mean
fragment size, RQD of blasting face is considered
one of important parameter (Chakraborty et al.,
2004; Saliu and Akande (2007); Mehrdanesh et al.,
2017).
A condition of blasted block size distribution
from a condition of in situ block size distribution is
through blasting process (Latham and Lu, 1999).
Explosive energy which results in breakage of rock
during blasting can be considered in two ways. Over
all rock mass to explosives consumed which is
represented by powder factor. Instantly gas pressure
is created to break rock after hole is fired. Maximum
charge per delay in each blast can be correlated with
maximum release of energy instantaneously.
Various studies carried out to predict mean blast
fragment size, powder factor is considered as crucial
parameter (Chakraborty et al., 2004; Morin and
Ficarazzo, 2006; Saliu and Akande, 2007; Gheibie et
al., 2009; Kulatilake et al., 2010, 2012; Singh et al.,
2016; Sharma and Rai 2017; Mehrdanesh et al.,
2017). Maximum charge per delay to predict blast
fragmentation is demonstrated by Monjezi et al.
(2009) and Faramarzi et al. (2013).
Studies by NIRM and CIMFR in India
recommended that minimum hole diameter should
be one hundredth of the bench height.On the other
hand, Adhikari (1999) proposed the maximum hole
diameter to be 0.01666 of the bench height.
Bhandari (1997) proposed burden to be 15 to 40
times of hole diameter for effective blasting in
opencast mines. For good blast fragmentation,
stiffness ratio should be between 2 to 4 as per Konya
and Walter (1990). Research studies at limestone
quarries show that with increase in stemming length
higher boulders are generated mainly from
stemming portion (Venkatesh et al., 1999 and
Cevizci and Ozkahraman, 2012). Thus, from various
studies it is observed that instead of single parameter
of hole diameter, burden, spacing and bench height,
various ratios of these parameters contribute to blast
performance. To predict mean blast fragmentation
size, various ratios of burden to blast hole diameter,
bench height to burden, spacing to burden and
stemming length to burden play an important role
(Chakraborty et al., 2004; Kulatilake et al., 2010,
2012; Faramarzi et al., 2013; Singh et al., 2016;
Sharma and Rai 2017; Mehrdanesh et al., 2017).
Figure 2: Limestone quarry at Thailand

3. The selected limestone quarry at Thailand is well
developed with number of benches as shown in
Figure 2. There is a variation in block size,
orientation of joints and spacing of joints.
2 METHODS
Knowledge of rock mass classification based on
GSI is useful for blast design. Every blasting face
was classified based on four types of rocks identified
based on GSI for jointed rock mass (Marinos and
Hoek, 2000). For this study, rock mass is classified
as blocy, very blocky, blocky/seamy and
disintegrated.
GSI depends upon fundamental geological
processes consisting of blockiness of rockmass and
condition of joints (Marinos et al., 2007). From
visual examination of each blasting face, GSI is
assessed based on lithology, structure, joint
condition. Average in-situ block size and RQD%
were measured at each blasting face and GSI
recorded for each blast.
Blast design parameters consisting of hole
diameter, burden, spacing, bench height, stemming
height were recorded from individual blast design to
determine various ratios. Maximum charge per
delay, powder factor were also recorded. For
individual blast, photographs were taken by placing
ball at blasted muck pile and further image analysis
by software.
Following input parameters are considered to
predict mean fragment size: Mean block size (XB),
RQD%, powder factor, maximum charge per delay,
(BD) burden to hole diameter ratio, (SB) spacing to
burden ratio, stiffness ratio (HB) consisting of ratio
of bench height to burden, (TB) stemming height to
burden ratio.
Total 78 blasting data set were collected for
analysis. Methodology of multivariate regression
analysis (MRA) and Artificial Regression Analysis
(ANN) was adopted to predict mean blast fragment
size (XB). Data on excavator output in Tonnes per
hour (TPH) for each blast was collected. Excavator
output was compared with fragment size greater than
800 mm (Boulder) and mean fragment size (X50).
Drilling output was recorded based drilling output in
meters per meter for each size of drill.
Table 1 : Summary of data collection:
Parameter Source of data/
methodology
GSI Visual examination of face
RQD% Actual field measurement
Insitu block size
Burden, spacing
Blast design of individual blast
Hole diameter
Bench height
Stemming height
Powder factor
Maximum charge per
delay
Powder factor
Mean fragment size Photograph of blasted face &
analysis with software
Excavator efficiency Blast wise loading rate by
excavator in TPH based on a)
Boulders >800mm per 100 T b)
mean fragment size X50
Drill efficiency Drill hole diameter, drilling output
in meters per hour
2.1 Multivariate Regression Analysis
The objective of multivariate regression analysis
(MRA) is to resolve equivalent factors of a function
to contribute best fit based on a set of data
observations. This technique results with a function
which is linear (straight-line) equation. In
circumstances where more than one independent
variable occurs, MRA is utilized to form the best-fit
equation. A least square fit is performed to resolve
engineering problems through MRA. While
applying this technique, some coefficients are
proposed by mode of backlash operator. The MRA
equation is given as follows (Tonnizam Mohamad et
al., 2017):
z = x+y1m1+ y2m2+ y3m3+….+ynmn (1)
where m1, m2, m3 ….mn are independent
variables, y1, y2, y3, …. yn are coefficients of
independent variables, x is constant andz is the
output of the system.
In order to develop a MRA equation for
prediction of fragment size, using the mentioned
system inputs, Excel software was selected and
utilized. All 78 datasets were divided to 2 sections;
training (57 datasets) and testing (21 datasets) and
then, considering only training datasets, a MRA
equation was constructed as follows:
BS is burden to spacing ratio. RQD is expressed
in %. MC is maximum charge per delay in Kg. PF is
powder factor in Kg per cum of blasted material. BD
is burden to hole diameter ratio. HB is bench height
to burden ratio. TB is stemming height to burden
ratio.
After developing the above equation, there is a
need to evaluate it through 21 testing datasets.
Results of training and testing datasets showed that
an acceptable performance prediction can be
achieved by the developed MRA equation. Several

4. performance indices consisting of coefficient of
determination (R2
), root mean square error (RMSE)
and variance account for (VAF) were used to
evaluated predictive models in this study where their
equations can be found in other studies (Armaghani
et al., 2017). More discussion regarding the
developed MRA will be given later.
2.2 Artificial Neural Network
By studying the the human-brain information
process motivated to develop ANN as soft
computation technique. Network architecture,
learning rule, and transfer function are three major
factors of ANN. Recurrent and feed-forward are two
major groups of ANNs. The study by Shahin et al.
(2002) proposes that if there is no time-vulnerable
factor in the ANN, the feed-forward ANN (FF-
ANN) can be carried out. The multi-layer perceptron
(MLP) neural network is one of well-known FF-
ANNs (Haykin 1999). This type of ANN is linked to
each other by means of various weights to a number
of neurons in different layers (input, hidden and
output layers). Kalinli et al. (2011) expressed the
great performance of MLP-ANNs in approximating
different functions in high-dimensional spaces. After
providing the data into ANNs and before finding the
solution, still, the ANN requires training. Dreyfus
(2005) reported that the back-propagation (BP)
algorithm is the most popular algorithms among
various category of learning algorithms for training
the MLP feed-forward neural networks.
Kuo et al. (2010) interpretedthat in a classical
BP-ANN, the imported data in the input layer kicks
off to generate to hidden nodes through connection
weights. In the BP-ANN, the flexible connection or
weight, Wij is multiplied by the input from each
neuron in the preceding layer, li.The sum of the
weighted input signals is calculated at each node and
subsequently, this value is combined to obtain a
threshold value recognized as the bias value, Bij,
(see Eq. 2). The output of node is produced (see
Eq.3) by combining input, Ji, and then passing
through a non-linear transfer function f (Jj), for
instance a sigmoidal function.The output of each
neuron, generally, produces the input to the next
layer neuron.This procedure is pursued until the
output is obtained.Yet, to pick up the error,
subsequently the generated output is analysed with
the expected output.The major reason for BP
training is to substitute the weights between the
neurons in iterative manner that reduces the mean
square error (MSE) to least of the system.In classic
artificial intelligence book by Fausett (1994), the BP
algorithm is illustrated.
(2)
(3)
Khamesi et al. (2015) illustrated that as a part of
ANN modelling procedure, the prepared database
was normalized during first stage in order to
simplify the design procedure and given below:
Znorm = (Z – Zmin) / (Zmax- Zmin) (4)
where Z and Znorm are the measured and
normalized values, respectively. Zmax and Zmin are
the maximum and minimum values of the Z.
Researchers, in the past, have suggested different
percentages for the testing datasets. Amounts of
20%, 25% and a range of 20%-30% of total datasets
were recommended for testing dataset in the
previous studies (Swingler, 1996). Hence, as
mentioned earlier, in the present study, 80% and
20% of total datasets were practiced for model
development and confirming the developed models,
respectively. In ANN modelling, choosing of the
ANN training algorithm and also decision of
selecting the network architecture are the most
challenging task. Levenberg-Marquardt (LM) was
chosen among all ANN training algorithms and
utilised to train the ANN systems. On the contrary,
as stated by many scholars (e.g. Singh et al. 2006),
an ANN network with only one hidden layer can
evaluate almost all engineering problems, therefore,
an ANN network with one hidden layer was applied.
Another important issue in designing an ANN model
is number of hidden nodes where the maximum
number of hidden nodes were reported by Hecht-
Nielsen (1987) as 2Ni + 1. In this study, with Ni =
8, the maximum number of hidden node can be
equal or less than 17. Hence, many ANN models
were built based on the different number of hidden
nodes and finally, according to obtained
performance indices, a model with 11 hidden nodes
outperforms the other models. The results
demonstrated that by developing new ANN model,
accuracy of the developed model was increased
significantly compared to created model by MRA.
Evaluation of the selected ANN model is given later.
3 RESULTS AND DISCUSSION
3.1 GSI
Geological study was undertaken, and rock mass
was classified based on geological strength index.
Rock mass at different locations was classified into
four categories (a) Blocky limestone (b) Very blocky
limestone (c) Blocky/ seamy limestone and (d) Dis-
integrated limestone as shown in Figure 3.

5. From 78 blasting data sets, it is observed that GSI
based rock mass classification, mean in-situ block
size range for blocky limestone is 0.8 to 1.2 m, very
blocky limestone is 0.5 to 0.8 m, blocky / seamy
limestone is 0.3 to 0.6 m and disintegrated limestone
is 0.1 to 0.3 m.
Figure 3: Rock mass classification based on GSI (Bhatawdekar et al., 2016)
Figure 4: Photographs of blasted limestone at Thailand Quarry for image analysis
Figure 4 shows photographs of blasted muck pile.
Based on blast fragmentation as per Figure 4,
blast fragmentation analysis is classified based on
rock mass classification based on blocky, very
blocky, blocky/ seamy and disintegrated.
Figure 5 shows image analysis done with
software and graphs are classified based on
limestone as per GSI stating in situ mean block size
(X) for a given blast. Four graphs consist image
analysis weight % finer Vs mean fragment size.
Figures 5(a), (b), (c) and (d) consist of image
analysis for blocky limestone, very blocky
limestone, blocky/ seamy limestone and
disintegrated limestone respectively. From blast
fragmentation distribution curves in Figure 5, it is
observed that disintegrated limestone has 40%
below 0.1 m and thus maximum fine generation.
0
10
20
30
40
50
60
70
80
90
100
0.1 1 10
Weight%Finer
m (a)
0
10
20
30
40
50
60
70
80
90
100
0.1 1 10
Weight%Finer
m (b)

6. Figure 5: Blast fragmentation distribution (a) Blocky limestone (b) Very blocky limestone (c) Blocky/ Seamy limestone (d) Dis-
integrated limestone
3.2 MVRA and ANN
As stated before, R2
, RMSE and VAF were used
to evaluate the obtained results. These performance
indices were calculated for both MRA and ANN
models as shown in Table 5. R2
were achieved as
0.678 and 0.764 for training and testing of MRA,
respectively, while these results were obtained as
0.825 and 0.845 for ANN model. Apart from R2
,
considering RMSE and VAF results, it was found
that ANN is capable to increase accuracy prediction
level of fragment size. By focusing on VAF results,
it can be observed that by developing an ANN
model VAF values were deeply improved from
about 65% to more than 80%. This shows ability of
ANN model in solving problem of predicting
fragment size. Generally, it can be concluded that
ANN model compared to MRA is performed better
and can predict fragment size with higher accuracy
level. The predicted fragment size values by MRA
and ANN models together with their measured
values are shown in Figures 6 and 7, respectively.
These figures proved that ANN predictive model can
provide a higher performance capacity in predicting
fragment size.
Table 2: The obtained results of developed models
Model
Performance Index
R2
RMSE VAF (%)
Train Test Train Test Train Test
MRA 0.678 0.764 0.026 0.038 66.684 64.768
ANN 0.825 0.845 0.182 0.214 82.001 80.286
Figure 6: The predicted fragment size values by MRA model together with their measured
0
10
20
30
40
50
60
70
80
90
100
0.1 1 10
Weight%Finer
m (c)
0
10
20
30
40
50
60
70
80
90
100
0.1 1 10
Weight%Finer
m (d)

7. Figure 7 The predicted fragment size values by ANN model together with their measured values
3.3 Excavator Performance
Table 3 shows excavator output compared with
fragment size greater than 800 mm (boulder) per 100
Tof blasted material. Table 4 shows excavator
output based on mean blast fragment size in ‘m’.
Table 3: Excavator output in TPH compared with fragment > 0.8 m (Boulder) per 100 T
Fragment >
0.8m
per 100 T
Excavator output TPH
200-
205
206-
210
211-
215
216-
220
221-
225
226-
230
231-
235
236-
240
241-
245
246-
250
4
5
6
7
8
9
10
11
12
Table 4: Excavator output in TPH compared with mean fragment size (X50) in m
Mean
fragmentation
m
Excavator output TPH
From To
200-
205
206-
210
211-
215
216-
220
221-
225
226-
230
231-
235
236-
240
241-
245
246-
250
0.131 0.140
0.141 0.150
0.151 0.160
0.161 0.170
0.171 0.180
0.181 0.190
0.191 0.200
0.201 0.210
0.211 0.220
0.221 0.230
0.231 0.240
0.241 0.250
0.251 0.260
0.261 0.270
Output of excavator varies from 200 to 250 TPH.
For increase in fragment size greater than 0.8 m
(boulder) from 4 to 12 per 100 T of blasted
limestone, excavator output drops from 245 to 200
TPH. For increase in mean fragment size from 0.13
to 0.27 m, excavator output drops from 245 to 200
TPH. Based on 8 variable inputs, it is possible to
predict mean fragment size with ANN model and
improve productivity of excavator.
3.4 Drill performance:
Comparison is made whether to have additional
DTH(Down The Hole) drills or new Top Hammer

8. (TH) drills. New 102 mm diameter TH drills are
procured to meet enhanced drilling requirement.
New TH drill output is 30 m/hour as compared to
old DTH drills output of 12 m/hour.Top hammer
drill has improved productivity from 12m/hour for
DTH drill to 30 m/ hour for Top hammer drill. Hole
diameter increased marginally from 76 mm to 102
mm without having adverse environment impact on
fly rock, ground vibration and air over pressure for
meeting local regulation. Spacing and burden was
proportionately increased. Thus, drilling
productivity has improved from 270 T/ hour to 1300
T/hour of drilling.
4 CONCLUSIONS
• Rock mass classification is an effective tool
for blast design. This has to be done on the
basis of study of geology of the area and
field observations of the blasting face. of
limestone into four types as blocky, very
blocky, blocky/ seamy and disintegrated
limestone supports for blast design based on
observation of blasting face.
• Eighnput Input parameters consisting of
mean block size (XB), RQD%, powder
factor, maximum charge per delay (MC),
(BD) burden to hole diameter ratio, (SB)
spacing to burden ratio, stiffness ratio (HB)
consisting of ratio of bench height to burden,
(TB) stemming height to burden ratio play
significant role are suitable for predicting
mean fragment size are suitable for
predicting mean fragment size (X50).
• R2
achieved as 0.678 and 0.764 for training
and testing of MVRA, respectively.
• R2
achieved as 0.825 and 0.845 for training
and testing of ANN, respectively.
• Excavator output improves from 200 TPH to
245 TPH with reduction in mean fragment
size from 0.27 m to 0.13 m and bigger
fragment size greater than 0.8 m from 12% to
4%
• Introduction of top hammer drill from DTH
drill has improved output 270 to 1300 T/
hour
Thus objectives of improving overall productivity
of aggregate quarry through drilling and blasting
were achieved.
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