Figure 1 - uploaded by Zaiton Haron
Content may be subject to copyright.
Source publication
The large number of operations involving noisy machinery associated with construction site activities result in considerable variation in the noise levels experienced at receiver locations. This paper suggests an approach to predict noise levels generated from a site by using a Monte Carlo approach. This approach enables the determination of detail...
Context in source publication
Context 1
... random number is normally a pseudo-random number uniformly distributed over the specified 0 to 1 interval. Figure 1 shows the relationship between the random number generated by the computer and the stochastic variable, g, assumed to have a normal distribution. When a random number, N, is selected from a uniform distribution, P(N), the value of N points to a value in the cumulative distribution probability, F(N). ...
Similar publications
In this work we want to clarify, via a Monte Carlo experiment, if (and when) for an integer-valued time series it is really recommended to adopt the coherent forecasting methods from INAR models or if equivalently good predictions can be obtained from the simpler AR models. Results show that INAR models should be preferred.
The use of the Monte Carlo technique in a reliable and inexpensive way without the need for a standard radioactive source in determining the detector efficiency is becoming widespread every passing day. It is important to model the detector with the real dimensions for an accurate and precise results for the method. Another parameter as important a...
Citations
... MCS has been used extensively for addressing probabilistic uncertainty in range estimation for construction projects [114]. This probabilistic method is a suitable tool to consider the embedded uncertainties in predicting noise emission from construction activities [21,34,44,73]. In addition to the uncer- Acoustic analysis High accuracy in low frequencies [64] Field measurement Inside of machinery operation cabin tainties that should be considered in construction noise prediction, the operational sequence is an important factor, which affects the noise emission from construction sites. ...
... However, if the noise emission from the noise source is not available, then we need historical data from past projects to predict noise level at a specific location. The most frequently used method for this purpose is MCS [21,34,44,73]. However, the best tool for noise prediction using historical data can vary depending on the available data and the purpose of the prediction. ...
Construction noise is one of the most important and prevalent occupational hazards in the construction industry. The negative effects of construction noise can be mitigated by implementing an efficient noise management process during pre-construction and construction stages. Current research themes in the area of construction noise management are fragmented and there is a need for a comprehensive review that directly explores the noise management process and its application in the construction industry in order to identify research themes and their major achievements, reveal limitations and gaps in the literature , and recommend avenues for future research. This study proposes a new construction noise management framework and categorizes the previous studies conducted in the area of construction noise management. The framework introduces four main steps for construction noise management including noise assessment, noise prediction, noise control and noise monitoring. Then, content analysis is conducted for the previous studies under the four identified noise management steps. The major contribution of this review lies on providing researchers and practitioners with a holistic understanding of the construction noise management process to improve workplace health and safety in construction sites and surrounding areas.
... The stochastic modelling generated 100, 000 data with different mean level deviation and standard deviation due to the randomized parameters during the simulation. A study proved that it is necessary to have large samples of up to 20, 000 to generate a smooth probability distribution curve [6]. As a result, the number of iteration for both the nested loops were determined as 20, 000 steps. ...
... Based on the data from Table 2, the average predicted standard deviations from both SPC and DL had an insignificant difference of 0.2 dBA as well. The disparities resulted from the inclusion of duty cycles in DL whilst the SPC considered the machine operates at all times [6], [7]. However, overall DL outperformed the SPC technique with the introduction of different duty cycles in the stochastic model. ...
Construction noise monitoring is crucial to assess the impactsof construction noise onthe workers and surroundings. However, the existing noise prediction methods are time-consuming in which required laborious work for the computation of the noise levels. This study aims to assess the accuracy andreliability of the deep learning model (DL) that adopted the stochastic modelling and artificial neural network (ANN) in construction noise prediction. The artificial neural network was trained with the output of stochastic modelling. The outcome of noiselevel prediction using simple prediction chart (SPC) and DLmodel wasdiscussed and compared to 3 case studies. The case studies were conducted at construction sites located in Semenyih,Selangor,Malaysia. The results of DL model showed high accuracy of predicted noise levels along with an absolute difference of less than 2.3 dBA. Besides, the predicted noise levels are reliable as the R-squared value was high. On that account, DL model is proved to be reliable and accurate in noise level prediction and it has the potential to be utilized as a managerial tool to monitor construction noise more effectively.
... By itself such an investigation is not new (see e.g. [1], [2]), but the general formulation and the use of directional behavior seems unexplored. This paper presents the theory and calculation results of a series of theoretical experiments of the summed sound field of sets of random constellations of sound sources. ...
It is not uncommon in acoustics practice that one has to predict the effect of clusters of sound sources, even if position, aiming, exact power or directionality is not known for each instance at any moment. Some examples are road traffic (line), crowd noise or construction site noise (surface), stage monitor loudspeakers or instruments (volume). More abstract examples are diffuse (random) reflections off boundaries. In a general formulation clusters of emitters that do not interfere with each other can have either random or definite (A) power, (B) directionality, (C) aiming and (D) position along a (1) trajectory (e.g. road), (2) a surface (rectangular or spherical), or (3) within volume (a rectangular or spherical). The paper presents results of a series of theoretical experiments to find out if there are any characteristic properties of such constellations in the near or far fields.
... For typical acoustic simulations using either the Finite Element Method or the Boundary Element Method, the exact locations of the noise sources and their respective noise profiles would be required. Researchers often need to resort to statistical or operational research methods such as the random walk approach, 5 the Monte Carlo method, 6 and the discrete event simulation method. 7 A review of the analysis methods and mitigation measures can be found in the work by Wu. 8 More accurate and precise description of noise profiles of construction equipment and processes will greatly improve the fidelity of these prediction models. ...
Noise pollution from construction sites has become a major problem for major cities with the continued rapid development as well as redevelopment of cities. These construction sites, in particular for new subway systems, are often near to residential and commercial buildings. A better understanding and characterization of noise profiles will be required for project management and planning as well as environmental impact assessment. In this study, instead of using the typical type 1 sound level meters for the measurement of noise profiles emitted from construction equipment and processes commonly done in construction industry, we attempt to characterize the noise profiles of common construction equipment at their respective noise source using an Acoustic Array or Acoustic Camera. The study also highlighted the significant presence of low-frequency noise at construction sites for some construction equipment and processes. This may have some implications for the design of noise barriers at construction sites.
... Researchers worldwide have used two methods for studies of construction noise emission estimation: the first examines noise data from equipment [3,4,[12][13][14][15] and the second from construction sites [16]. Taking advantage of such estimation methods, individual exposure levels can be plausibly measured during the planning phase. ...
Noise produced by construction activities has become the second most serious acoustic polluting element in China. To provide industry practitioners with a better understanding of the health risks of construction noise and to aid in creating environmentally friendly construction plans during early construction stages, we developed a quantitative model to assess the health impairment risks (HIA) associated with construction noise for individuals living adjacent to construction sites. This model classifies noise-induced health impairments into four categories: cardiovascular disease, cognitive impairment, sleep disturbance, and annoyance, and uses disability-adjusted life years (DALYs) as an indicator of damage. Furthermore, the value of a statistical life (VSL) is used to transform DALYs into a monetary value based on the affected demographic characteristics, thereby offering policy makers a reliable theoretical foundation for establishing reasonable standards to compensate residents suffering from construction noise. A practical earthwork project in Beijing is used as a case study to demonstrate the applicability of the proposed model. The results indicate that construction noise could bring significant health risks to the neighboring resident community, with an estimated 34.51 DALYs of health damage and 20.47 million yuan in social costs. In particular, people aged 45-54 are most vulnerable to construction noise, with the greatest health risks being caused by sleep disturbance.
... Hamoda [5] used the Back-Propagation Neural Network (BPNN) to predict the construction noise by modeling the nonlinear and imprecise relationship between inputs and outputs. Monte Carlo technique had been adopted to predict the construction noise by reflecting the uncertainties [15][16][17]. However, these studies did not consider the equivalent continuous noise (L eq-T ) over a period of time, let alone the uncertainties, dynamics and interactions that are particularly sensitive to the L eq-T . ...
... Though the general methods including the Monte Carlo methods [15,16] could address uncertainties or randomness, but they could not model the dynamics and complex interactions over time. The discrete-event simulation (DES) method has the capabilities for modeling uncertainties, dynamics and interactions [28], so the DES method is selected to estimate the construction noise in terms of L eq-T by implementing the noise-calculating models from Equations (2)-(10) as well as accounting for the uncertainties, dynamics and complex interactions. ...
Quantitative prediction of construction noise is crucial to evaluate construction plans to help make decisions to address noise levels. Considering limitations of existing methods for measuring or predicting the construction noise and particularly the equivalent continuous noise level over a period of time, this paper presents a discrete-event simulation method for predicting the construction noise in terms of equivalent continuous level. The noise-calculating models regarding synchronization, propagation and equivalent continuous level are presented. The simulation framework for modeling the noise-affected factors and calculating the equivalent continuous noise by incorporating the noise-calculating models into simulation strategy is proposed. An application study is presented to demonstrate and justify the proposed simulation method in predicting the equivalent continuous noise during construction. The study contributes to provision of a simulation methodology to quantitatively predict the equivalent continuous noise of construction by considering the relevant uncertainties, dynamics and interactions.
... The available literature on the quantification of noise in construction sites focuses primarily on developing deterministic models to predict sound levels produced by the construction equipment, with some studies offering slight insight into the optimisation of sound attenuation barriers [13,[49][50][51][52]. The general format of the noise level equation employed in this study (Eq. ...
... These two factors were argued to cause disturbance to the accuracy of noise levels predicted at the receiver whenever the distance of the source from the receiver exceeded 100 m. Zaiton Haron and Khairulzan Yahya [24] used Monte Carlo methods in order to predict the noise levels from equipment on a construction site. Simulation was run to collect data and the probability density functions (PDF) and cumulative distribution functions (CDF) of noise levels based on the samples obtained were plotted. ...
Activities undertaken on a construction site are often accompanied with high levels of noise. Addressing the issue of noise pollution in construction is gaining significance with the growing awareness about the social and environmental components of sustainable construction and the increasing numbers of projects being undertaken in congested urban areas. The documented methods for reducing noise pollution in construction include controlling (1) the noise produced at the source; (2) noise levels reaching a receptor; (3) noise propagated along the transmission path. Methods addressing the latter points use the fact that attenuation of noise increases as the transmission path gets longer. Thus the efficiency of such methods can be improved considerably through optimising the arrangement of temporary facilities on construction sites, with respect to a receptor, making use of noise attenuation due to distancing noisy facilities away from noise-sensitive receivers. The building under construction can also be used as a barrier to the noise transmission path, where obstruction of particular facilities from a given receiver can help in producing lower levels of sound as measured at the receptor. The available literature on site layout planning is extensive but limited to only achieving traditional construction project objectives (travel and material handling cost, safety, etc.). This paper presents a mixed integer non-linear programming (MINLP) model that optimises the location of temporary facilities on site in order to minimise the sound levels measured at a pre-defined receptor. The present model is expressed in three stages: (1) defining the noise objective function; (2) implementing model constraints; and (3) application of COUENNE to solve the MINLP for a case study.
... Stochastic models have been introduced to include the effect of random positions of sources and the random power of acoustic strength [16][17][18][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][21][22], the propagation of sound as it propagates through a complex environment [23][24][25][26][27][28][29], and the variability in the sound source from the traffic [30][31][32][33]. ...
... Two stochastic models have been developed in previous research, namely the Monte Carlo approach and the probability approach [16][17][18][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 L Aeq , but also have the noise content for a working period. ...
... 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. ...
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.
... SOUND PROP AGA nON MODEL Literature describes many methods and models to estimate noise reduction in industrial rooms [18][19][20][21][22][23][24][25]. Some models are intended to predict the sound power of each one of the sources that are operating simultaneously in a closed room [26][27][28][29]. ...
This paper presents an approach to teach the propagation of machine generated acoustic noise in a closed industrial room. The proposed educational approach is based on a model that facilitates the simulation of noise propagation. The physics and numerical elements of the model are described. The model and its simulation have been used for teaching an undergraduate class on Industrial Noise Control. The simulation results are compared to data obtained using a set of microphones located in a machine room.
