Joseph Quartieri

Modeling environmental noise exceedances using non-homogeneous Poisson processes.

In this work a non-homogeneous Poisson model is considered to study noise exposure. The Poisson process, counting the number of times that a sound level surpasses a threshold, is used to estimate the probability that a population is exposed to high levels of noise a certain number of times in a given time interval. The rate function of the Poisson process is assumed to be of a Weibull type. The presented model is applied to community noise data from Messina, Sicily (Italy). Four sets of data are used to estimate the parameters involved in the model. After the estimation and tuning are made, a way of estimating the probability that an environmental noise threshold is exceeded a certain number of times in a given time interval is presented. This estimation can be very useful in the study of noise exposure of a population and also to predict, given the current behavior of the data, the probability of occurrence of high levels of noise in the near future. One of the most important features of the model is that it implicitly takes into account different noise sources, which need to be treated separately when using usual models.

Deterministic decomposition and seasonal ARIMA time series models applied to airport noise forecasting

One of the most hazardous physical polluting agents, considering their effects on human health, is acoustical noise. Airports are a strong source of acoustical noise, due to the airplanes turbines, to the aero-dynamical noise of transits, to the acceleration or the breaking during the take-off and landing phases of aircrafts, to the road traffic around the airport, etc.. The monitoring and the prediction of the acoustical level emitted by airports can be very useful to assess the impact on human health and activities. In the airports noise scenario, thanks to flights scheduling, the predominant sources may have a periodic behaviour. Thus, a Time Series Analysis approach can be adopted, considering that a general trend and a seasonal behaviour can be highlighted and used to build a predictive model. In this paper, two different approaches are adopted, thus two predictive models are constructed and tested. The first model is based on deterministic decomposition and is built composing the trend, that is the ...

Honking noise contribution to road traffic noise prediction

The implementation of Road Traffic Noise predictive Models (RTNMs) is crucial in order to be able to predict noise in urban areas strongly affected by vehicular traffic. These RTNMs can have in input a small or large number of inputs, according to the implemented function. Among these inputs, honking cannot be neglected in some specific areas in which drivers are used to horn in traffic jam or in proximity of intersections or other vehicles. In this paper, starting from a field measurement campaign in India, the authors highlight the shortcomings of standard RTNMs, that are not able to include random noisy events such as low or high pressure honking. Once the differences will be evaluated, the contribution of honking will be estimated and added to the predictions, to achieve a new model that is able to provide results in good agreement with field measurements.The implementation of Road Traffic Noise predictive Models (RTNMs) is crucial in order to be able to predict noise in urban areas strongly affected by vehicular traffic. These RTNMs can have in input a small or large number of inputs, according to the implemented function. Among these inputs, honking cannot be neglected in some specific areas in which drivers are used to horn in traffic jam or in proximity of intersections or other vehicles. In this paper, starting from a field measurement campaign in India, the authors highlight the shortcomings of standard RTNMs, that are not able to include random noisy events such as low or high pressure honking. Once the differences will be evaluated, the contribution of honking will be estimated and added to the predictions, to achieve a new model that is able to provide results in good agreement with field measurements.

Validation of Seasonal ARIMA Models on Road Traffic Noise Measurements

The Time Series Analysis (TSA) technique is largely used in economics and related field, to understand the slope of a given univariate dataset and to predict its future behaviour. The Seasonal AutoRegressive Integrated Moving Average (SARIMA) models are a class of TSA models that, based on the periodicity observed in the series, build a predictive function that can extend the forecast to a given number of future periods. In this paper, these techniques are applied to a dataset of equivalent sound levels, measured in an urban environment. The periodic pattern will evidence a strong influence of human activities (in particular road traffic) on the noise observed. All the three models will exploit the seasonality of the series and will be calibrated on a partial dataset of 800 data. Once the parameters of the models will be evaluated, all the forecasting functions will be tested and validated on a dataset not used before. The performances of all the models will be evaluated in terms of errors values and distributions, such as introducing some error indexes that explain the peculiar features of the models results.

Statistical and Semi-Dynamical Road Traffic Noise Models Comparison with Field Measurements

The need for road traffic noise monitoring is growing in urban areas due to the growth of vehicles number and to the consequent increase of risk for human health. Noise measurements cannot be performed everywhere, or even in a large number of sites, because of high costs and time consumption. For this reasons, Road Traffic Noise predictive Models (RTNMs) can be implemented to estimate the noise levels at any distance, knowing certain parameters needed as input of the RTNM. In this paper, the main statistical RTNMs are presented, together with the implementation of two innovative and advanced models: the EU suggested model (CNOSSOS-EU) and a research model presented by Quartieri et al. (2010). These models will be compared with noise measurements performed in different sites and with different traffic conditions, in order to avoid bias from geometry or other features of the area under study. The main conclusion is that the application of innovative models and the inclusion of dynamical information about traffic flow, will lead to better results with respect to statistical models.

Time series analysis techniques applied to transportation noise

Acoustic noise assessment is a crucial problem in areas in which transportation means, such as motorway, railway, airport, etc., are present. Dwelled areas, in fact, represent a sensible point, that is affected by several externalities, among which, acoustic noise is very important. In this paper, the techniques known as Time Series Analysis (TSA), are used to analyze datasets of noise level produced by transport systems. This approach is based on the analysis of trend and seasonality of the series, and on the implementation of a function of the time that can provide predictions for future time periods. According to the choice and to the input of each model, the forecast horizon can vary from few days further to any time period in the future. Two techniques will be presented: one is based on a Deterministic Decomposition (DD-TSA), able to predict at any future time period; the second is based on a stochastic approach, and adopt the so called SARIMA (Seasonal AutoRegressive Integrated Moving Average) models, to provide prediction on a short time range. Both techniques will be applied to a road traffic noise dataset and to an airport noise levels time series. Results will show that the typology of transportation system does not affect the prediction performances of both the DD-TSA and the SARIMA techniques, even though the time basis of the data is different, being daily for traffic noise and hourly for airport.

A hybrid predictive model for acoustic noise in urban areas based on time series analysis and artificial neural network

The dangerous effect of noise on human health is well known. Both the auditory and non-auditory effects are largely documented in literature, and represent an important hazard in human activities. Particular care is devoted to road traffic noise, since it is growing according to the growth of residential, industrial and commercial areas. For these reasons, it is important to develop effective models able to predict the noise in a certain area. In this paper, a hybrid predictive model is presented. The model is based on the mixing of two different approach: the Time Series Analysis (TSA) and the Artificial Neural Network (ANN). The TSA model is based on the evaluation of trend and seasonality in the data, while the ANN model is based on the capacity of the network to “learn” the behavior of the data. The mixed approach will consist in the evaluation of noise levels by means of TSA and, once the differences (residuals) between TSA estimations and observed data have been calculated, in the training of a ANN ...

Prediction of Airport Acoustical Noise by Deterministic Decomposition and Seasonal ARIMA Techniques

In this paper a time series of hourly equivalent noise levels acquired near the international airport of Nice, France, is analysed. Two different techniques are proposed to model and forecast the time series: deterministic decomposition and seasonal autoregressive moving average. The two models are defined and fitted on the calibration dataset. Subsequently, the developed models are tested comparing their forecasts with 25 noise level data not used in the calibration phase. A detailed error analysis, by means of statistics and metrics, will be presented to test the models performances.

An Ultrasound Technique for the Characterization of the Acoustic Emission of Reinforced Concrete Beam

Among the various non-destructive techniques for health monitoring in structures, the Acoustic Emission (AE) is well known in scientific literature. Ultrasonic waves emitted by the creation and propagation of cracks in concrete or Reinforced Concrete specimens are usually collected by means of ultrasonic sensors. The signals must be treated in front-end readout process with preamplifiers and filters, to be able to set a proper trigger level and to cut the background noise (belonging to different frequency ranges). In addition, the post processing of the data is important to “clean up” the dataset, removing fake events, and to extract the proper information, useful for structure damage assessment. In this paper, the authors present the experimental set up and the transducers used to acquire the AE signals recorded during a four-point bending test on a RC beam. The ad hoc realized amplifier and filtering circuit used in the test are also described. Then, an example of an AE signal is also reported, in terms of frequency spectrum analysis and noise filtering.