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Descripción
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| Noise mapping is a complex process requiring a large amount of data from different sources, which are not always available. In the process, there are many factors involving simplifications, approaches and deviations that contribute to the final uncertainty of the result. An error in the final result of the noise map causes an incorrect amount of exposed population, as well as the design and implementation of inadequate or wrong noise action plans. The uncertainty analysis in the creation of noise maps is therefore a key to use its results as a tool to design noise action plans. However, up to now, there are only guides giving an approximate range of possible contribution to the uncertainty depending on the quality of input data. The present doctoral thesis analyses the contributions to the total uncertainty of a noise map, proposing a methodology to quantify it. Firstly, a review of the state of the art is performed, studying different aspects contributing to the system uncertainty: calculation standard, acoustic calculation engine, situational acoustic model and acoustic measurements. After analyzing the sources that contribute to the overall system uncertainty, a method to quantify the expanded uncertainty properly is proposed, from an analytical calculation and experimental determination. Thus, a specific value of the uncertainty of a map can be calculated avoiding approximations and range values. In order to apply the proposed method, three different noise mapping cases are discussed and their implemented methodologies are studied. In the first case, the qualities of the input data to create the acoustic model and the model validation by experimental measurements are discussed. Then, a calculation of the expanded uncertainty of the whole mapping is performed, comparing the obtained result with the approximated range values specified so far. Thus, the affect of default input values to the model and the application of European calculation methods in non-European countries are checked. The second case, a mixed methodology through measurements and simulation is discussed. Additionally, rail noise prediction method recommended by the European Union is evaluated. The third study case, describes the entire process of data collection and using default data that leads to a map with a high deviation, therefore, it is not a validated map. Consequently, the quality of the input data improvement process is described, quantifying the uncertainty reduction compared to the initial data. That analysis is done before and after the calibration process. KEYWORDS: NOISE MAPPING; UNCERTAINTY; VALIDATION; CALIBRATION; SIMULATION; MEASUREMENTS. | |
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Internacional
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Si |
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ISBN
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Tipo de Tesis
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Doctoral |
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Calificación
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Sobresaliente cum laude |
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Fecha
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10/12/2009 |