prediction of noise pollution from construction sites …...pollution although to varying levels...

Energy Education Science and Technology, Part A: Energy Science and Research

2012 Volume (issues) 29(2): 989-1002

Prediction of noise pollution from

construction sites at the planning

stage using simple prediction charts

Zaiton Haron1,*

, Khairulzan Yahya2, Zanariah Jahya

1

1Universiti Teknologi Malaysia, Faculty of Civil Engineering, Department of Structure and Materials, 81310

Skudai, Johor, Malaysia 2Universiti Teknologi Malaysia, Faculty of Civil Engineering, Construction Technology and Management

Centre, 81310 Skudai, Johor, Malaysia

Received: 10 August 2011; accepted: 13 October 2011

Abstract

Prediction of noise pollution from construction site plays an important role in planning and construction

management. However, engineers may have difficulty in making predictions at the planning stage because the

acoustic characteristics and location of the source are not precisely known, and many assumptions have to be

made. This study focuses on the development of chart predictions based on stochastic modelling, so that the data

available at the planning stage can be used to produce a set of noise levels along with standard deviations. The

study compares the noise predictions using the chart with the results of measurement, and simulation. Two

simple charts in the form of deviations from the mean noise level versus the ratio r/w, and standard deviation

versus the ratio r/w, were established based on analysis using stochastic models developed by considering

systematic changes in the site parameters. The charts were applied to predict construction noise in a physical

case of substructure work. The noise levels predicted using the design charts are slightly higher, by 3 dB(A) and

1 dB(A), than the results obtained using measurement and simulation, respectively. Based on these results, the

charts can be used to manually approximate construction noise at the planning stage with reasonable accuracy.

The advantages of charts are that the level of noise at various locations of the receiver can be determined

manually and quickly using various sound power levels of equipment that may actually be employed in the

construction process. Keywords: Noise pollution; Noise prediction; Stochastic modelling; Construction management;

Sustainable environment ©Sila Science. All rights reserved. 1. Introduction

In general, construction activities generate excessive noise pollution and can be very

disturbing when these activities are very close to sensitive areas. This is coupled with noise

fluctuations [1] due to changes in the modes of the machines’ working [2, 3] and high noise

emissions from the equipment used [2, 4, 5]. As a result, all stages of construction yield noise

___________ *Corresponding author. Tel.: +607-553-1581; fax: +607-556-6157.

E-mail address: [email protected] or [email protected] (Z. Haron).

990 Z. Haron / EEST Part A: Energy Science and Research 29 (2012) 989-1002

pollution although to varying levels [6], with the work on substructures (excavation) being the noisiest

stage, while others such as the framework and walls, brickwork, services and roof are in the range of

the same noise emission level [7]. Although the effect of construction noise on civilians is not as

serious as of the effect on the construction workers, such as hearing loss [8-10], physical and

psychological disorders still occur and have been reported elsewhere [11-15]. These adverse effects

may however be reduced by complying with the emission limits set by the local authority while still in

the planning stage through the prediction of noise resulting from the construction process. However,

engineers may have difficulty in making prediction at the planning stage because the source and

characteristics of sound, such as the frequency spectrum, duration, and movement of sound sources,

are not precisely known since only the architectural design (for buildings) or general layout (for open

sites, e.g. landfill) are available. In addition, some of the work will be subcontracted to a subcontractor

who is responsible for providing much of the plant, therefore the assumptions used to predict the noise

levels in general are based on the most hazardous activities [15]. Stochastic models have been introduced to include the effect of random positions of sources and

the random power of acoustic strength [16-19] in the modelling of construction noise. Historically,

stochastic models have been used in acoustics since the 1950's, starting with the determination of the

mean free path in a room [20-22], the propagation of sound as it propagates through a complex

environment [23-29], and the variability in the sound source from the traffic [30-33]. Due to the

existence of some similarities in the variability of the sound source of traffic and open site activities, a

number of stochastic models of noise from construction sites were studied [16-19, 34, 35]. Several

studies showed that stochastic models had the advantage of reducing the laboriousness of assessing the

various parameters and results in the statistical information output which had also been used in

contemporary work in environmental noise [36-41]. These included the levels that are exceeded 10%

of the time (L10), 50% of the time (L50), and 90% of the time (L90). Furthermore, Lmax and Lmin

stand for the maximum and minimum sound levels, respectively. Two stochastic models have been developed in previous research, namely the Monte Carlo

approach and the probability approach [16-19]. Although not yet verified by measurement, the models were found not only to be in good agreement with the deterministic approach in terms of

LAeq, but also have the noise content for a working period. For this reason, noise that can annoy

people can be determined, for example the probability that the noise level exceeded World Health Organisation (WHO) limits [42] or the Department of the Environment (DOE) [43] limits. These

findings were in agreement with suggestions from previous research that the use of a single LAeq

prediction alone is not sufficient for identifying the particular sound that could affect the public [44, 45].

In this study, simple prediction charts were developed using a stochastic model to estimate

the level of noise arising from construction noise. The charts provide engineers with a

predictive tool to estimate the noise level from a construction site while still in the planning

stage, for the preparation of an environmental impact assessment for the purpose of obtaining

approval from local authorities. The present study is also important for verifying previous

studies on the simulation of cumulative noise level distribution with those obtained from real

measurements.

2. Methodology

The work presented in this study starts with the development of simple prediction charts,

the measurement of noise levels at selected sites and the simulation of noise levels using the

Z. Haron / EEST Part A: Energy Science and Research 29 (2012) 989-1002 991 Monte Carlo approach. Comparison between the result of the charts with measurement at the

physical site and from the simulation was carried out.

2. 1. Development of simple prediction charts

Prediction charts were developed using the Monte Carlo model developed by previous

researchers [16, 18, 19] with some modifications as follows: (1) the fluctuation in noise level

generated at the receiver during a real construction process is only due to the random position

of an item of equipment, (2) an item of equipment has the strength of 1 watt, (3) there is no

screening between the receiver and source. The mean noise level for a site is calculated by averaging several noise levels obtained

when a machine work in random positions in a site. The site is assumed to be a well-defined

working rectangular area with width w and depth d, and a receiver is located at a distance r

from the site centre at an angle of o (Fig. 1a). The noise level at the receiver is obtained by

assuming hemispherical radiation over a hard surface and is given by:

)102

(log.10 12

210),(

ij

aji

R

WL

(1)

where aW is the acoustic power of the source which equals 1 watt, and ijR is the distance

from the source position (xi,yj,zs) to the receiver (xr,yr,zr) given by:

ijR = ( (xi – xr)2 + (yj – yr)

2 + (zs – zr)

2 )0.5

(2)

with:

sinrxr and cosryr

The probability of a machine working at a random point or position is defined by using two

random numbers Ni and Nj, with coordinates xi and yj as follows:

xi = w(Ni – 0.5) (3)

yj = d(Nj – 0.5) (4)

A machine working at one location has the same probability as it working at all other

points, and the source and receiver are considered to have the same height. By repeating the random position of the machine several samples of the noise level are

obtained, and a statistical analysis is conducted to determine the probability of the noise level

distribution as shown in Fig. 1b. From this figure, the mean noise level (Lp) and standard

deviation () are determined, and these are the important parameters in establishing the

charts. The derivation of the charts consists of three steps: identification, estimation, and

diagnostic checking. The identification stage involves determining the general characteristics of the Lp and of

a square site with a change in receiver distance (r) using the above equation and procedure. It

was found that a square site with a ratio r: w: d results in the same distribution of noise levels

(Fig. 1b) and thus has the same value of . The distribution of noise levels and

systematically decreases with the same distance to the receivers (Fig. 1c). It was found that

992 Z. Haron / EEST Part A: Energy Science and Research 29 (2012) 989-1002 the Lp of square sites changes systematically compared to the sound pressure level if the

source is placed at the centre of the site, (Lc) (Fig. 1d). Both and the mean level deviation,

L=Lp-Lc produce a systematic variation when plotted against r / w (Fig. 1e and Fig. 1f).

(a) Site configuration with receiver positioned at

an angle to the site

(b) Noise level distribution of sites with the

same ratio of r: w: d

50 60 70 80 900

0.2

0.4

0.6

0.8

1

Level, dB

Pro

bability d

istr

ibution

r=29m r=33m

r=41m

r=57m

r=89m

r=153m

r=281m

(c) Effect of distance on noise level distributions

for a square site (___ = 50 m × 50 m, - - - - =

100 m × 100 m)

(d) Mean noise level variation (Lp)

0 10 20 30 40 500

1

2

3

4

5

r/w

Sta

ndard

devia

tion

(e) Standard deviation () variation versus r/w

100

101

-0.4

-0.3

-0.2

-0.1

0

0.1

r/w

Mean level devia

tion

(f) Deviation of mean noise level (L)

versus r/w

Fig. 1. Effect of distance for square sites (50 m × 50 m and 100m × 100 m).

Z. Haron / EEST Part A: Energy Science and Research 29 (2012) 989-1002 993

In the estimation stage, the characteristics of versus r/w and L versus r/w were further

investigated for rectangular sites with aspect ratios of 1:8, 1:4, 1:2, 2:1, 4:1, and 8:1. Sites

with approximate fixed dimensions 18 m × 141 m, 25 m ×100 m, 35 m × 70 m, 100 m × 25

m, and 141 m × 18 m were selected. Each site has a receiver which is presumed to move

along the radius of a circle at an angle of 0° from the site centre. plotted against r/w is more

systematic, as shown Fig. 2. The for sites with the greatest w is less than the for sites with

a greater d, due to the more symmetrical noise level distribution of sites with a greater w. It

has a narrow range between the maximum and minimum levels and therefore a smaller

standard deviation. Sites with the greater depths, in turn, have a larger range between the

maximum and minimum levels due to the effect of the inverse square law. The highest sound

pressure levels are given by points closest to the receiver, and the lowest are given by points

at greater distances.

10-2

10-1

100

101

102

0

2

4

6

8

r/w

Sta

ndard

devia

tion

1:8

1:4

1:2

1:1 2:1

4:1

8:1

Fig. 2. Variation in standard deviation () for a receiver at 0° for site aspect ratios between

Fig. 3 shows the variation in for each aspect ratio as the receiver moves from 0 to 45°

for aspect ratios 1:2 to 2:1. The variation in for aspect ratios 1:8, 1:4, 4:1 and 8:1 are shown

in Appendix A. Statistical analysis was performed and the results showed that there are

insignificant differences in the changes in value as the receiver moved from 0 to 15 for all

aspect ratios, while there are significant changes in when the receiver moves from 0 to 30

and 0 to 45 for all site aspect ratios except 1:1.

The L variation for the receiver located at 0° fall systematically with r/w as shown in Fig.

4. It was found that sites with an aspect ratio of 1:1 have the smallest L (i.e. less than 0.5 dB)

for receivers placed in all corners. Sites with aspect ratios of 1:2, 2:1, 4:1, and 8:1 showed

increases or decreases in L of as much as 2 dB(A), while the largest L, of about 10 dB, was

for the site with an aspect ratio of 8:1. Fig. 5 shows the L for each aspect ratio as the receiver moves from 0 to 45° for aspect

ratios 1:2 to 2:1. Aspect ratios 1:8, 1:4, 4:1 and 8:1 are shown in Appendix B. Simple

relationships between L, Lp, the sound power level (Lw), and receiver’s distance (r) are

established. It was determined that the relationship can be expressed as follows:

LrLogLL wp 8)(1020 (5)

For the site aspect ratio w:d and r/w, Eq. 1 calculates L while the respective is obtained

directly from Fig. 3. Finally a diagnostic check of the chart was performed to reveal its possible adequacy and

to test the consistency of the charts in Fig. 3 and Fig. 5 through computations of the another

group of sites with a fixed depth of 50 m, with dimensions of 6m × 50 m, 14 m × 50 m, 25 m

× 50 m, 100 m × 50 m, 200 m × 50 m, and 400 m × 50 m. It was observed that all sites with

the same aspect ratio have the same curve of variation and L when plotted against r/w.


101

102

0

2

4

6

8

(r/w)

Sta

ndar

d de

viat

ion 0

15

30

45

o

o

o

o

(a) Aspect ratio 1:2.

101

102

0

0.5

1

1.5

2

2.5

3

r/w

Sta

ndar

d de

viat

ion

30.45

0,15

o

o

(b) Aspect ratio 1:1.

100

101

0

1

2

3

4

5

(r/w)

Sta

ndard

devi

ation

15

30

45

o

o

o

0 o

(c) Aspect ratio 2:1.

Fig. 3. Standard deviation () variation for receiver at 0°, 15°, 30° and 45° for site aspect ratios

between 1:2 to 2:1.

10-1

100

101

-12

-10

-8

-6

-4

-2

0

2

4

r/w

Mea

n le

vel d

evia

tio

n

1:8 1:4

1:2 1:1

2:1

4:1

8:1

Fig. 4. Variation in mean level deviation (L) for the receiver at 0° for site aspect ratios


100

101

-0.2

0

0.2

0.4

0.6

0.8

1

r/w

Mea

n le

vel d

evia

tion 0

o

15 o

30 o

45 o

(a) Aspect ratio 1:2

100

101

-0.4

-0.2

0

0.2

0.4

r/w

Mea

n le

vel d

evia

tion

45

15

15

0

o

o

o

o

(b) Aspect ratio 1:1

100

101

-3

-2.5

-2

-1.5

-1

-0.5

0

r/w

Mea

n le

vel d

evia

tion

0

o

15

o

30

o

45

o

(c) Aspect ratio 2:1

Fig. 5. Mean level deviations (L) for receivers positioned at different angles for aspect ratios 1:2 to 2:1. 2. 2. Application of charts The important factor when using the charts lies in the identification of the overall site and

its division into several sub-sites, which define the activity of the machine and its working

area. Dependent activities carried out by two or more machines can also be grouped in one

sub-site. The prediction of noise at a receiver located at a distance r from each sub-site can be

carried out by using the following six steps: (1) decide on the w and d of the sub-area where

the equipment will be working; (2) determine the angle of the receiver position to the site

centre, ; (3) determine the distance from the site centre to the receiver, r and the ratio r/w; (4)

determine by referring to Fig. 3; (5) determine L using Fig. 5; (6) calculate Lp using Eq. 5.

Each of the mean noise levels from each sub-site are combined using Eq. 6 to obtain the

equivalent noise level, LAeqn and is obtained through the sum of variances using Eq. 7.

)10..1010(log.10 10/10/210/110

LpnLpLpAeqnL

(6)


222

21 ... n

(7)

Where Lp1, Lp2,..Lpn is the mean noise level from each equipment and 1 , 2 , .. n is the

standard deviation of the mean noise level for each item of equipment. 2. 3. Comparison with measurements

Measurements of noise levels at construction sites have been conducted at the physical site

shown in Fig. 6 in order to verify the accuracy of the predictions using the chart. The site was

selected based on the activities of the machines employed, and because there were no obstructions

that could absorb sound between the receiver and the machines. In this case study, the site is

divided into four sub-sites that are dependent on the activities and work of the machines, as listed

in Table 1. Noise data were measured using a type two sound level meter, and was calibrated

using data logging calibration performed before and after measurements. A sound calibrator is

used to check the sound level meter with a reference sound (94 dB at 1 kHz), and will receive a

small error of less than 0.5 dB(A). All factors and weather conditions that may affect sampling

have been recorded in the notes. This is important when considering the factors that influence and

affect the quality of the data collected.

Three categories of observations were made: (1) sound power level of each machine,

Lw;(2) noise level at the receiver during the working day; and (3) period of time of the machining

working in a specific mode. The sound power levels of each piece of equipment were acquired by

using the measurements carried out at four points 1 m from each item of equipment. Each

measurement period for each point was 30 seconds, and each point was measured in duplicate.

Measurements were carried out with the equipment operating at full power and in idle conditions.

The four measurements were averaged to determine a sound level LAeq30.

The noise levels at the receiver were continuously observed from 08:40 to 15:00, which

represented a period of one working day. Background noise is also measured. Measurement

results are presented in the form of a cumulative frequency distribution and frequency

distribution, from which the index values such as LAeq10 and LAeq90 are obtained. The length of

time that each item of equipment spent in idle, full-power, or off mode were calculated to obtain

the probabilities of the working condition (Pw) that it might be completely off for P of the working

day, on idle for Q of the working day, and operating at full power for S of the working day, or

Pw(P,Q,S).

Fig. 6. Construction site layout.


Table 1. Division of sub-sites

Sub-site Activities in sub-sites

A An excavator (EX1) was digging out spoil and a portable compactor (PC) was used to

compact the spoil

B An excavator (EX2) excavated the soil in sub-site B for filling sub-site A

C Two dump trucks (DT1 and DT2) carried soil from sub-site B to sub-site A

D A mobile crane (MB) lifted a steel bar and moved it within sub-site D

2. 4. Comparison with simulation

A stochastic model in the form of the Monte Carlo approach was used to determine the mean

noise level and standard deviation from a series of noise level samples through the random

positioning of the machine. The basic model was proposed elsewhere [16, 17]. Each sub-site is

represented by one local model to take into account the activities in each sub-site, and all together

there are four local models. Theses local models are then combined into a global model to represent

all construction activities carried out in one day (Fig. 7). In this simulation, each sub-site was

assumed to be a well-defined working rectangular area with a width w and depth d, and a receiver

located at a distance r from the sub-site’s centre (Fig. 6).

In contrast with the prediction using charts, the effects of the random acoustic power of the

machine to the intensity of sound is taken into consideration in Pw(P,Q,S) by introducing another

random number, Nk. For example, the probability that it might be completely off for P% of the

working day, on idle for Q% of the working day, and operating at full power for S% of the working

day is:

100/)(

100/100/

100/

QPN

QNP

PN

k

k

k

(8)

The noise level at the receiver from a particular source location works with random power is

obtained using Eq. 1. This procedure is repeated for other machines in the same sub-site, and also

for the machines in other sub-sites of B, C, and D. The combined noise levels arising from the

contributions of all sources is calculated using the logarithmic rule. A statistical analysis is then

carried out to determine the frequency and cumulative distribution function of the noise level and

the mean noise level, standard deviation or other Lindex were calculated.

Fig. 7. Principles of Monte Carlo model for obtaining cumulative distribution level


3. Results and discussion

3. 1. Result from measurement and simulation

Application of simple prediction charts were carried out for a physical site, as

shown in Fig. 1, with data obtained from site measurement, including r, , Lw and Pw

as presented in Table 2. The LAeq distribution for measurements over 6 hrs 20 minutes

was 59 dBA with of 4.2 dB(A) while the background noise was 32 dB(A). The

design charts produced Lp and that have a disparity of 3 dB(A) and 2 dB(A)

respectively when compared to the actual measurement results, due to the following

three reasons: (i) rounding-off of w/d and r/w to the nearest value available in the

charts has yielded a smaller and higher L; (ii) the charts do not account for the duty

cycle of the machine, Pw or the percentage of time during which the equipment is

working on site, thus producing a higher L for each item of equipment; (iii) the charts

assumed that all items of equipment work over their entire respective areas, while in

real construction some of the work may not reach all points in each sub-site.

Table 2. Results of the application of the design charts

Sub-area Equipment Pw Lw r , , w/d, r/w (Fig.

3)

L

(Fig.

5)

Lp6hrs

(dB(A))

Combined

Lp6hrs

(dB(A))

A EX1 0.36:0.02:0.62 103

r = 81.49 m,

= 0o

w/d = 1,

r/w = 2

1.25

0

57

62 dB(A)

std = 2.4

PC 0.37:0:0.63 105 59

B EX2 0.36:0.01:0.63 103

r = 141.55

m,

= 42.1o

w/d = 1.1,

r/w = .5

0.75 0 52

C

DT1 0.35:0.16:0.49 93 r = 101.24

m,

= 30o

w/d = 0.2,

r/w = 5.1

1.75 0.12

45

DT2 0.35:0.17:0.48 93 45

D MB 0.4:0.02:0.58 93

r = 125.6 m,

= 3o

w/d = 0.4,

r/w = 6.3

0.8 0.12 43

Application of the measured data into Eqs. 1 to 5 and 9 yielded the noise level

distribution shown in Fig. 8. In comparison with the results of the charts, the

simulation has a slightly lower Lp of 61 dB (A) and a of 5.2 dB(A). It was

previously mentioned that the charts and simulation used the same assumption that all

equipment was working over the entire sub-site, therefore the 1 dB(A) difference in

Lp from that of the charts’ result was due to the first two reasons concerning the

disparity of Lp between the measurements and chart results. There were disparities in

Lp values obtained from the simulation and measurement results (as much as 2

dB(A)) and in the probability of noise level distributions as shown in Fig. 8. These

were due to the assumption in the model that all point in the sub-site were considered

to have been reached by the equipment.

From the above comparison, the prediction method using the chart produced Lp

that were slightly higher than the measurement, and simulation results. The factors


that influenced the prediction results are the accuracy of estimated sub-site dimensions, w and

d, r, Lw and . The accurate Lw and the rounding off of values of r/w and w/d that best fit the

charts would produce a reasonable Lp. Although the charts were developed without screening

in the pathway between the source and receiver, the Lp obtained from a sub-site can be

reduced by screening attenuation provided that the screen covers the entire pathway between

the receiver and all point in the sub-site. Furthermore, the chart can also be used for any

values of acoustic power, even though it was built using a source power of 1kw, since the

charts are dimensionless. For this reason, although the details of the machine are not known,

engineers can use the previous data of Lw to produce a set of Lp and both manually and

quickly, without the necessity of a sophisticated computer.

Fig. 8. Noise level distribution from simulation versus measurement.

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

40 50 60 70

Pro

bab

ility

dis

trib

uti

on

Noise level, dB(A)

Noise leveldistribution(measurement)

Cumulativedistribution level(Measurement)

Noise leveldistribution(simulation)

4. Conclusion

This study has shown the development of a simple predictions chart based on stochastic

modelling, so that the data available at the planning stage can be used to produce a set of

noise levels along with their standard deviation. The study compares the predictions of noise

from using the chart with the results of measurement, simulation, and calculation of standard

methods. Two simple charts in the form of deviations from the mean noise level versus the

ratio r/w and standard deviation versus the ratio r/w were established based on analysis using

stochastic models developed by systematic changes in the site parameters. The charts were

applied to predict the construction noise at a physical case of substructure work. The noise

levels predicted using the design charts are slightly higher, by 3 dB(A) and 1 dB(A), than the

results obtained using measurement and simulation, respectively. Based on these results, the

charts can be used to manually approximate construction noise at the planning stage with

reasonable accuracy. The advantages of the charts are in determining the level of noise at

various locations of the receiver both manually and quickly, using the various sound powers

of equipment that may be employed in a real construction process.

Acknowledgements

The authors would like to express their sincere thanks to Ministry of Science, Technology

and Innovation Malaysia (MOSTI) and Universiti Teknologi Malaysia (UTM) for their

sponsorship of the research.


Appendices

Appendix A : Standard deviation () variation for receiver at 0°, 15°, 30° and 45° for site aspect ratios

1:8, 1:4,4:1 and 8:1

101

102

0

2

4

6

(r/w)

Stan

dard

dev

iatio

n

0

15

30

45

o

o

o

o


100

101

102

0

1

2

3

4

5

6

(r/w)

Stan

dard

dev

iatio

n

0

15

30

45

o

o

o

o


100

101

0

1

2

3

4

5

(r/w)

Sta

ndar

d de

viat

ion

15

30

45

o

o

o

0 o

(e) Aspect ratio 8:1

10-1

100

101

0

1

2

3

4

5

(r/w)

Stan

dard

dev

iatio

n

0

15

30 45 o

o

o

o

(f) Aspect ratio 4:1

Appendix B : Mean level deviations (L) for receivers positioned at different angles for aspect ratios

1:8, 1:4,4:1 and 8:1

101

102

0

0.5

1

1.5

2

2.5

r/w

Mea

n le

vel d

evia

tion 0

o

15 o

30 o

45 o


101

-0.5

0

0.5

1

1.5

2

r/w

Mea

n le

vel d

evia

tion

0 o

15 o

30 o

45 o


100

-6

-5

-4

-3

-2

-1

0

r/w

Mea

n le

vel d

evia

tion

0

o

15

o

30 o

45

o


10-1

100

-10

-8

-6

-4

-2

0

r/w

Mea

n le

vel d

evia

tion

0 o

15 o

30 o

45 o


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prediction of noise pollution from construction sites …...pollution although to varying levels...

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