PROCEEDINGS of the23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Predicting the noise level during sawing of carbonate rocks fromthe P-wave velocity
Sair KAHRAMAN1; M. Suat DELIBALTA2; Ramazan COMAKLI2
12

Hacettepe University, Turkey

Nigde Omer Halisdemir University, Turkey

ABSTRACT
Noise affects humans both physically and psychologically, whose impacts vary from person toperson. Block cutting machine is one of the most important noise sources in the stone processing plants.
This study investigates the predictability of the noise level generated during sawing of carbonate rocks
from the P-wave velocity. First, the P-wave velocities of rock samples were measured in the laboratory.
Then, the core samples were cut by an automatic cutting machine w ith diamond saw in the laboratory
and noise levels were measured. A strong linear correlation was found between the noise levels and theP-wave velocity. After including the densities of the samples to the analysis, the correlation coefficient
was increased significantly. It is concluded that the noise level of carbonate rocks can be estimatedusing the derived equations from the P-wave velocity. The laboratory noise level can be converted tothe site noise level using a conversion factor.
Keywords: Rock sawing, Noise, P-wave velocity

1. INTRODUCTION
About 20 % of European labors are exposed to noise so loud that they would have to raise their
voice to talk to other people. Exposure to noise is especially common in the construction andmanufacturing sectors (1). There are both physical and psychological effect of noise on humans.
Exposure to excessive noise for a short time can cause temporary hearing loss, while long -term
exposure to loud noise, or short exposures to very loud noises, can cause perma nent hearing loss. In
addition to hearing loss, exposure to noise in the workplace can cause a variety of other problems,including chronic health problems (2):••••

Exposure to noise over a long period of time decreases coordination and concentration. This
increases the chance of accidents happening.
Noise increases stress, which can lead to a number of health problems, including heart, stomachand nervous disorders. Noise is suspected of being one of the causes of heart disease andstomach ulcers.
Workers exposed to noise may complain of nervousness, sleeping problems and fatigue (feeling
tired all the time).
Excessive exposure to noise can also reduce job performance and may cause high rates ofabsenteeism.

There are limited studies in the literature on the relation between the rock properties and the soundlevel during cutting rock by diamond saw. Karakurt et al. (3) carried out a study on the noise level
generated during the sawing of granites using circular diamond sawblades. They showed that
increasing of peripheral speed, traverse speed and cutting depth increased noise levels. They derived
relations having moderate correlation coefficients between the noise level and both compressive
strength and density. They also developed multiple regression models for the prediction of noise level1

sairkahraman@yahoo.com
rcomakli@ohu.edu.tr
3
msdelibalta@ohu.edu.tr
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by including the operating variables and rock properties. We (4) studied the assessability of the noisefrom sawing rock by diamond saw using the P-wave velocity. The noise level measurement andP-wave velocity tests were performed on 36 different rock types. The evaluation of the results showedthat there was a strong power relation between the noise level and the P -wave velocity. It wasconcluded that the noise level from the diamond sawing could be assessed using th e P-wave velocity.
We (5) also investigated the predictability of the Los Angeles abrasion loss from the noise level incutting rocks by diamond saw and developed a prediction equation. In th is study, the Los Angelesabrasion, noise level measurement, density, and porosity tests were carried out on 27 different rocktypes such as igneous, metamorphic, and sedimentary. A good relation between the Los Angeles
abrasion loss and noise level was found. The multiple regression analysis was also performed by also
including density and porosity values in order to check the possibility of obtaining the more significantrelations. However, it was seen that the correlation coefficients of the multiple regression equations
were slightly higher than that of the simple regression equation. In another study (6), we evaluated thepredictability of the physico-mechanical properties of rocks from the noise level in cutting rock bydiamond saw in the laboratory and derived some correlations. The noise measurement, uniaxial
compressive strength, Brazilian tensile strength, point load strength, density, and porosity test werecarried out on 54 different rock types in the study. Significant correlations were found between thenoise level and the rock properties. In our recent study (7), we studied the predictability of the noisefrom the block cutting machines using the physico-mechanical and mineralogical properties of rocks
during sawing igneous rocks. The strong correlations were derived between the noise level and therock properties using the physico-mechanical and mineralogical properties.
The level of noise allowed by most countries' noise standards is generally 85 -90 dB over aneight-hour workday (2). Block cutting machines have been extensively used for the slab production
and are the most important noise sources in the stone processing plants. The noise levels of thesemachines during cutting the rock are generally higher than the allowable level of noise. Predicting thenoise level of the block cutting machine during the sawing of a new rock type is useful for the noisemanagement. In this study, the predictability of the noise from the block cutting machines wasinvestigated using the P-wave velocity for carbonate rocks.

2. SAMPLING
Rock blocks were collected from the stone and marble quarries, and stone processing plants inNigde, Kayseri, Konya, Antalya and Afyon areas of Turkey for the laboratory testing. In order toprovide the test specimens free from fractures, partings or alteration zones, block samples wereinspected for macroscopic defects. A total of 17 different rock types were sampled, 5 of which werelimestone, 7 of which were travertine, and 5 of which were marble. The locations and the types of therocks sampled are given in Table 1.
Table 1 – The rock types and the locations of the tested rocks.
Rock code
LocationRock type (Trade mark)1
Sogutalan/Bursa
Limestone (Bursa beige)
Korkuteli/Antalya
Limestone(Korkuteli beige)
2
Limestone(Aegean brown)
3
Fethiye/Mugla
Limestone (Bunyan rosa)
4
Bunyan/Kayseri
5
Dolomitic Limestone
Yahyali/Kayseri
6
Travertine
Mut/Icel
7
Travertine (Limra)
Finike/Antalya
8
Travertine
Karaman9
Travertine
Godene/Konya
10
Travertine(Demre stone)
Demre/Antalya
11
Bucak/Burdur
Travertine (Limra)
12
Sivas/Yildizeli
Travertine
13
Uckapili/Nigde
Marble14
Marble (Tiger skin)
Iscehisar/Afyon
15
MarbleKemalpasa/Bursa
16
Marble (Afyon sugar)
Iscehisar/Afyon
Gumusler/Nigde
Marble177603

3. LABORATORY STUDIES
3.1

Ultrasonic velocity test
The P-wave velocities were measured on the samples having a diameter of 54.7 mm and a lengthof 110 mm. End surfaces of the core samples were polished sufficiently smooth plane to provide goodcoupling. In the tests, PUNDIT 6 instrument and two transducers (a transmitter and a receiver) having
a frequency of 1 MHz were used. A good acoustic coupling between the transducer face and the rocksurface is necessary for the accuracy of transit time measurement. Stiffer grease was used as a coupling
agent in this study. Transducers were pressed to either end of the sample and the pulse transit time wasrecorded (Figure 1). The tests were repeated three times for each rock type and average value wastaken as the P-wave velocity value (Table 2).

Figure 1 – P-wave velocity measurement.
3.2 Noise level measurement tests
The core samples with a diameter of 54.7 mm were cut using an automatic cutting machine with adiamond saw of 300 mm (Figure 2). The rotational speed of the saw was 2750 rpm and the advancing
rate of the saw was 10 mm/min. The noise level was continuously measured by a sound level meter
during cutting of the core sample. The measurements were generally repeated three times and theresults were averaged for each rock type (Table 2).

Figure 2 – The automatic cutting machine used in the noise level measurement tests.

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3.3 Density test
The smooth-cut core samples were used in the determination of the dry density. The specimen
volume was calculated from the average of several calliper readings. The mass of the specimen wasdetermined by a balance, capable of weighing to an accuracy of 0.01 of the sample weights. The
density values were obtained from the ratio of the specimen mass to the specimen volume. The density
tests were repeated three times for each rock type and the average value was recorded as the density
value (Table 2).
Rock code
12345678910111213
1415
1617

Table 2 – Average test results.
Noise level (dB)P-wave velocity (km/s)
86.6
6.1
87.9
6.2
83.6
6.1
83.4
6.0
84.1
6.1
80.4
4.0
81.5
4.3
83.1
5.4
81.7
5.4
83.1
5.5
78.8
3.7
84.1
5.4
85.2
5.7
82.6
4.1
83.6
4.6
84.0
5.1
83.7
5.3

Density (g/cm3)
2.61
2.61
2.57
2.62
2.63
1.97
2.35
2.33
2.36
2.39
2.17
2.43
2.69
2.59
2.64
2.62
2.68

4. EVALUATION OF THE RESULTS
The test results given in Table 2 were evaluated using the regression analysis. The noise levels werecorrelated to the P-wave velocity values in order to develop an estimation equation. A linear relation
with a good correlation coefficient (r) of 0.78 was found between the noise level and the P-wave
velocity (Figure 3). Increasing P-wave velocity increases the noise level. The equation of the line is:ܰ‫ ܮ‬ൌ ʹǤͲͶܸ௣ ൅ ͹ʹǤ͸ͺ(1)where, NL is the noise level (dB) and V P is the P-wave velocity (km/s).

Figure 3 – The correlation between the noise level and the P-wave velocity.

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The P-wave velocity values were also correlated to the density values. As shown in Fig. 4, there isa linear relation between the P-wave velocity and the density values. Increasing density increases theP-wave velocity values. The relation has a good correlation coefficient (0.74). The equation of the lineis:ܰ‫ ܮ‬ൌ ͹Ǥ͹ͷߛ ൅ ͸ͶǤͲͻ(2)where, NL is the noise level (dB) and γ is the density (g/cm3 ).

Figure 4 – The correlation between the noise level and the density.
The multiple linear regression analysis was performed by including both the P-wave velocity andthe density values to the analysis for the expectation of obtaining stronger model than Equation 1. The
correlation coefficient of the derived model is 0.85, higher than that of Equation 1. The derived
multiple regression model is as follows:ܰ‫ ܮ‬ൌ ͳǤͶܸ௣ ൅ ͶǤͶߛ ൅ ͸ͷǤʹʹ

(2)where, NL is the noise level (dB), VP is the P-wave velocity (km/s), and γ is the density (g/cm3 ).
The measured and estimated noise levels in this study are the laboratory noise levels. These noiselevels should be converted to the field noise levels for the practical use. It was shown (7) that thelaboratory noise levels can be converted to the field noise level by multiplying a conversion factor of
1.16 for the block cutting machines. Thus, the conversion factor can be used for converting theestimated noise levels using the equations derived in this study to the field noise level.

5. CONCLUSIONSThe noise level measurements were performed in the laboratory during cutting seventeen carbonate
rocks such as limestone, travertine and marbles in order to evaluate the noise from the block cutting
machines in the stone processing factories using the P-wave velocity of rocks. The statistical analysisof the results showed that there was a strong linear correlation between the noise levels and the P-wave
velocity. Multiple regression analysis was also performed by including both the P -wave velocity andthe densities of the samples to the analysis and a stronger model was derived than that of the simple
regression analysis.
Concluding remark is that the laboratory noise level for a new rock type to be cut can be estimatedusing the derived relations, and then, the it can be converted to the site noise level using the conversion
factor.

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ACKNOWLEDGEMENTS
This study has been supported by the Scientific Research Project Unit of Nigde Omer Halisdemir
University, under the project number FEB 2009/03. The automatic cutting machine used in the study
was financed by Alexander von Humboldt Foundation.

REFERENCES
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by circular diamond sawblades. Sadhana 2013;38(3): 491–511.
4. Kahraman S, Delibalta MS, Comakli R. The assessment of noise from block cutting machines using theP-wave velocity. Proc Fifth Int. Conf. Sustainable Development in the Minerals Industry (SDIMI 2011);
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5. Kahraman S, Delibalta MS, Comakli R. Noise level measurement test to predict the abrasion resistanceof rock aggregates. Fluct Noise Lett. 2013;12: Paper No. 1350021; DOI: 10.1142/S0219477513500211.
6. Delibalta MS, Kahraman S, Comakli R. The usability of noise level from rock cutting for the predictionof physico-mechanical properties of rocks. Fluct Noise Lett. 2015;14: Paper No. 1550006; DOI:
10.1142/S02194775155000 66.
7. Kahraman S, Delibalta MS, Comakli R, Fener M. Predicting the noise level in rock sawing from thephysico-mechanical and mineralogical properties of rocks. Applied Acoustics 2016; 114:244 – 251.

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