Étude théorique et expérimentale de la propagation des vibrations émises par un trafic ferroviaire se déplaçant à vitesse constante

Dans cet article, nous présentons un modèle numérique spécialement conçu pour l'étude des vibrations basses fréquences couplant un sol stratifié à une voie ferrée supportant un train se déplaçant à vitesse constante. Les résultats ainsi obtenus sont en accord avec les observations expérimentales suivantes : pour une augmentation des vitesses, on note une majoration des déplacements à la surface du sol et dans la voie. Dans le but de la validation qualitative du modèle numérique, des mesures sur sites dont le sol est particulièrement “mou” ont également été réalisées. Une chaîne de mesures composée d'une caméra vidéo numérique rapide, d'un vélocimètre laser et de plusieurs accéléromètres est disposée aux abords d'une voie ferrée dans la région d'Amiens. Des tests géomécaniques et sismiques préalables sont mis en œuvre pour évaluer les paramètres mécaniques du sol. A l'issue de ces mesures, la vitesse des ondes de Rayleigh est calculée (proche de 100 m/s). Les résultats issus des mesures de vidéo numérique rapide sont comparés à ceux du modèle. Les déplacements maximum ainsi que les vitesses particulaires à la surface du sol peuvent alors être retrouvés bien que la totalité du signal n'ait pas été prise en compte dans le modèle

Finite element model for crack growth process in concrete bituminous

International audienc

Tire–pavement tractive rolling contact under turning conditions: towards pavement top-down cracking

International audienc

Retrospective Analysis of a Listeria monocytogenes Contamination Episode in Raw Milk Goat Cheese Using Quantitative Microbial Risk Assessment tools

In 2005, the Belgian authorities reported a Listeria monocytogenes contamination episode in cheese made from raw goat's milk. The presence of an asymptomatic shedder goat in the herd caused this contamination. On the basis of data collected at the time of the episode, a retrospective study was performed using an exposure assessment model covering the production chain from the milking of goats up to delivery of cheese to the market. Predictive microbiology models were used to simulate the growth of L. monocytogenes during the cheese process in relation with temperature, pH, and water activity. The model showed significant growth of L. monocytogenes during chilling and storage of the milk collected the day before the cheese production (median increase of 2.2 log CFU/ml) and during the addition of starter and rennet to milk (median increase of 1.2 log CFU/ml). The L. monocytogenes concentration in the fresh unripened cheese was estimated to be 3.8 log CFU/g (median). This result is consistent with the number of L. monocytogenes in the fresh cheese (3.6 log CFU/g) reported during the cheese contamination episode. A variance-based method sensitivity analysis identified the most important factors impacting the cheese contamination, and a scenario analysis then evaluated several options for risk mitigation. Thus, by using quantitative microbial risk assessment tools, this study provides reliable information to identify and control critical steps in a local production chain of cheese made from raw goat's milk

Identification of effective properties of the railway substructure in the low-frequency range using a heavy oscillating unit on the track

As the demand for predictions of train-induced vibrations is increasing, it is essential that adequate parameters of the railway structure are given as input in the predictions. Gathering this information can be quite time-consuming and costly, especially when predictions are required for the low-frequency emission. This article presents a procedure for deriving the effective properties of the foundation under the sleepers of a railway track from measurements taken with a heavy oscillating unit on the track. The unit consists of two masses inside a modified freight car that exert a dynamic force in the range 3–30 Hz on one of the two axles. The ratio of force applied on the axle over the resulting response measured with an accelerometer is studied. The foundation of the sleepers is modelled using a frequency-dependent complex-valued dynamic stiffness.Design and ConstructionCivil Engineering and Geoscience

Cuckoo Search Based Backcalculation Algorithm for Estimating Layer Properties of Full-Depth Flexible Pavements

This study introduces a backcalculation algorithm to estimate the material properties of the full-depth asphalt pavements. The proposed algorithm, namely CS-ANN, uses an Artificial Neural Network (ANN) based forward response engine, which is developed from the solutions of nonlinear finite element analysis to calculate the deflections mathematically. In the backward phase of the method, Cuckoo Search (CS), is utilized to search for the layer moduli values. The performance of the proposed method is investigated by analyzing the synthetically calculated deflections by a finite element based software and deflection data obtained from the field. In addition, to evaluate the searching capability of CS, optimization algorithms widely used in pavement backcalculation; Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA), are employed for comparison purposes. Obtained results indicate that the proposed backcalculation approach is able to determine stiffness-related layer properties in an accurate and rapid manner. In addition, CS presents a promising performance in reaching the optimum solutions that are better than GA, PSO, and GSA