Statistical classification of road pavements using near field vehicle rolling noise measurements

Low noise surfaces have been increasingly considered as a viable and cost-effective alternative to acoustical barriers. However, road planners and administrators frequently lack information on the correlation between the type of road surface and the resulting noise emission profile. To address this problem, a method to identify and classify different types of road pavements was developed, whereby near field road noise is analyzed using statistical learning methods. The vehicle rolling sound signal near the tires and close to the road surface was acquired by two microphones in a special arrangement which implements the Close-Proximity method. A set of features, characterizing the properties of the road pavement, was extracted from the corresponding sound profiles. A feature selection method was used to automatically select those that are most relevant in predicting the type of pavement, while reducing the computational cost. A set of different types of road pavement segments were tested and the performance of the classifier was evaluated. Results of pavement classification performed during a road journey are presented on a map, together with geographical data. This procedure leads to a considerable improvement in the quality of road pavement noise data, thereby increasing the accuracy of road traffic noise prediction models.

Truck noise IV-G. Field test results on a heavy duty, diesel truck having reduced noise emissions. Seventh report.

Transportation Department, Office of Noise Abatement, Washington, D.C.

Flammability characteristics of vehicle interior materials. Final technical report.

National Highway Safety Bureau, Washington, D.C.

Truck noise XI. Evaluation and reduction of heavy-duty truck noise. Final report.

Transportation Department, Office of Noise Abatement, Washington, D.C.

Assessment of vehicle interior dimensions and lap/shoulder belt fit. Final report.

Mode of access: Internet.

Prediction of light aircraft interior noise /

Bibliography: leaf 18.

Identification of damping in a bridge using a moving instrumented vehicle

In recent years, there has been a significant increase in the number of bridges which are being instrumented and monitored on an ongoing basis. This is in part due to the introduction of bridge management systems designed to provide a high level of protection to the public and early warning if the bridge becomes unsafe. This paper investigates a novel alternative; a low-cost method consisting of the use of a vehicle fitted with accelerometers on its axles to monitor the dynamic behaviour of bridges. A simplified half-car vehicle-bridge interaction model is used in theoretical simulations to test the effectiveness of the approach in identifying the damping ratio of the bridge. The method is tested for a range of bridge spans and vehicle velocities using theoretical simulations and the influences of road roughness, initial vibratory condition of the vehicle, signal noise, modelling errors and frequency matching on the accuracy of the results are investigated.

Associations of short-term particle and noise exposures with markers of cardiovascular and respiratory health among highway maintenance workers

BACKGROUND: Highway maintenance workers are constantly and simultaneously exposed to traffic-related particle and noise emissions, and both have been linked to increased cardiovascular morbidity and mortality in population-based epidemiology studies. OBJECTIVES: We aimed to investigate short-term health effects related to particle and noise exposure. METHODS: We monitored 18 maintenance workers, during as many as five 24-hour periods from a total of 50 observation days. We measured their exposure to fine particulate matter (PM2.5), ultrafine particles, noise, and the cardiopulmonary health endpoints: blood pressure, pro-inflammatory and pro-thrombotic markers in the blood, lung function and fractional exhaled nitric oxide (FeNO) measured approximately 15 hours post-work. Heart rate variability was assessed during a sleep period approximately 10 hours post-work. RESULTS: PM2.5 exposure was significantly associated with C-reactive protein and serum amyloid A, and negatively associated with tumor necrosis factor α. None of the particle metrics were significantly associated with von Willebrand factor or tissue factor expression. PM2.5 and work noise were associated with markers of increased heart rate variability, and with increased HF and LF power. Systolic and diastolic blood pressure on the following morning were significantly associated with noise exposure after work, and non-significantly associated with PM2.5. We observed no significant associations between any of the exposures and lung function or FeNO. CONCLUSIONS: Our findings suggest that exposure to particles and noise during highway maintenance work might pose a cardiovascular health risk. Actions to reduce these exposures could lead to better health for this population of workers.

Trade-off aproaches for the vibroacoustic analysis of trains

Passengers’ comfort in terms of acoustic noise levels is a key design driver for train design. The problem is especially relevant for high speed trains, where the aerodynamic induced noise is dominant, but it is also important for medium speed trains where the mechanical sources of noise may have more influence. The numerical interior noise prediction inside the train is a very comp lex problem, involving many different parameters: complex geometries and materials, different noise sources, com- plex interactions among those sources, broad range of frequencies where the phenomenon is important, etc. In this paper, the main findings of this work developed at IDR/UPM (Instituto de Microgravedad “Ignacio Da Riva”, Universidad Politécnica de Madrid) are presented, concentrat ing on the different modelling methodologies used for the different frequency ranges of interest, from FEM-BEM models, hybrid FEM-SEA to pure SEA models. The advantages and disadvantages of the different approaches are summarized. Different modelling techniques have also been evaluated and compared, taking into account the various and specific geometrical configurations typical in this type of structures, and the material properties used in the models. The critical configuration of the train inside a tunnel is studied in order to evaluate the external loads due to noise sources of the train. In this work, a SEA-model composed by periodic characteristic sections of a high spee d train is analysed inside a tunnel.

Preliminary human factors guidelines for automated highway system designers. Volume I: guidelines for AHS designers.

Federal Highway Administration, Washington, D.C.

Instrument and control location, accessibility and identification. Draft final report.

National Highway Safety Bureau, Washington, D.C.