Cracking in asphalt concrete

Fatigue resistance of asphalt concrete is usually evaluated by means of fatigue tests, like repeated load four point bending tests. These tests are rather time consuming and, therefore, not suited for performance based specifications. In this article a new, faster procedure to determine the fatigue characteristics is introduced. The procedure is based on a combination of theory and experiments. Both the theorectical background and the experiments that were used are discussed in this article. Furthermore, fatigue relations that were developed using the new procedure and relations developped using classical fatigue tests, are compared.Road & Railroad Research LaboratoryCivil Engineering and Geoscience

Design, calibration and validation of a wheel-rail contact force measurement system in V-Track

An innovative downscale test rig called V-Track has been constructed for wheel-rail contact experiments under impact loading conditions. In this paper, a force measurement system termed a dynamometer was developed in V-Track to measure the wheel-rail contact forces. The dynamometer consists of four 3-component piezoelectric force sensors and was mounted between the wheel assembly and the steel frame of V-Track, enabling it to measure the forces transmitted from the wheel-rail interface to the frame. Static tests were first carried out to calibrate the dynamometer in three directions. Then, several tests were performed in V-Track to examine the reliability and validity of the dynamometer for measuring the wheel-rail contact forces under running conditions. Experimental results show that the dynamometer is capable of reliably and accurately measuring these forces. Utilizing the measurement results from the dynamometer, the control of the wheel-rail contact forces in V-Track has also been achieved.Railway Engineerin

An experimental study on the effects of friction modifiers on wheel–rail dynamic interactions with various angles of attack

By modifying friction to the desired level, the application of friction modifiers (FMs) has been considered as a promising emerging tool in the railway engineering for increasing braking/traction force in poor adhesion conditions and mitigating wheel/rail interface deterioration, energy consumption, vibration and noise. Understanding the effectiveness of FMs in wheel–rail dynamic interactions is crucial to their proper applications in practice, which has, however, not been well explained. This study experimentally investigates the effects of two types of top-of-rail FM, i.e. FM-A and FM-B, and their application dosages on wheel–rail dynamic interactions with a range of angles of attack (AoAs) using an innovative well-controlled V-track test rig. The tested FMs have been used to provide intermediate friction for wear and noise reduction. The effectiveness of the FMs is assessed in terms of the wheel–rail adhesion characteristics and friction rolling induced axle box acceleration (ABA). This study provides the following new insights into the study of FM: the applications of the tested FMs can both reduce the wheel–rail adhesion level and change the negative friction characteristic to positive; stick–slip can be generated in the V-Track and eliminated by FM-A but intensified by FM-B, depending on the dosage of the FMs applied; the negative friction characteristic is not a must for stick–slip; the increase in ABA with AoA is insignificant until stick–slip occurs and the ABA can thus be influenced by the applications of FM.Railway Engineerin

A 3D finite element modeling of hammer test for track parameter identification

In railway tracks, short wave rail surface defects give rise to high wheel-rail dynamic forces and noise. Having a better insight into short wave defect occurrence could lead to development of adapted maintenance methods or track design to delay or avoid defect, so that the high short wave defect related maintenance costs may be reduced. As part of a health monitoring method under development, obtaining track dynamic characteristics is the first step in assessing track deterioration. In-situ hammer test measurements are widely used for track parameter derivation. In this paper, in order to reproduce hammer test measurements so that track parameters can be identified, a Finite Element model was developed where the rail and the sleeper were modeled with their real geometry. Although some discrepancy still exists between the first and second order pin-pin anti-resonances and the measured frequencies, the model showed the same main track characteristics in the receptance function as the measurements. The reacceptance function in the frequency range 1500-3000 Hz was also qualitatively determined. The model could not yet simulate the frequency response between 450-1000 Hz for tracks with monoblock and timber sleepers as modal analysis of the sleeper corroborated. Future work is focused on improving the model so that it is valid for the complete frequency range of 450-3000 Hz and tracks of all the sleepers type.Structural EngineeringCivil Engineering and Geoscience

A multiple spiking neural network architecture based on fuzzy intervals for anomaly detection: a case study of rail defects

In this paper, a fuzzy interval-based method is proposed for solving the problem of rail defect detection relying on an on-board measurement system and a multiple spiking neural network architecture. Instead of outputting binary values (defect or not defect), all data will belong to both classes with different spreads that are given by two fuzzy intervals. The multiple spiking neural networks are used to capture different sources of uncertainties. In this paper, we consider uncertainties in the parameters of spiking neural networks during the training phase. The proposed method comprises two steps. In the first step,multiple sets of the firing times for both classes are obtained from multiple spiking neural networks. In the second step, the obtained multiple sets of firing times are fuzzy numbers and they are used to construct fuzzy intervals. The proposed method is showcased with the problem of rail defect detection. Thenumerical analysis indicates that the fuzzy intervals are suitable to make use of the information provided by the multiple spike neural networks. Finally, with the proposed method, we improve the interpretability of the decision making regarding the detection of anomalies.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Railway Engineerin

Rail Condition Monitoring using Axle Box Acceleration Measurements: Defect detection in the Netherlands and Romania

In this paper we discuss rail condition monitoring based on axle box acceleration (ABA) measurements. We present three case studies. The first one in The Netherlands, the detection of local defects with different severity levels (squat A, squat B and squat C) is analysed. The second case from the Faurei testing ring in Romania, the detection of rail defects over the whole testing ring is presented and examples of responses at a local defect (wheel-burn) is discussed with measurements at 80km/h (conventional speed measurement) and 200km/h (high speed measurement). In the third case, ABA measurements were obtained during operation in a train with passengers in the railway line near Brasov, Bartolomeu-Zărneşti. Examples of the defects and validations are discussed.Railway Engineerin

A condition-based maintenance methodology for rails in regional railway networks using evolutionary multiobjective optimization: Case study line Braşov to Zărneşti in Romania

In this paper, we propose a methodology based on signal processing and evolutionary multiobjective optimization to facilitate the maintenance decision making of infra-managers in regional railways. Using a train in operation (with passengers onboard), we capture the condition of the rails using Axle Box Acceleration measurements. Then, using Hilbert-Huang Transform, the locations where the major risks are detected and ssessed with a degradation model. Finally,evolutionary multiobjective optimization is employed to solve the maintenance decision problem, and to facilitate the visualization of the trade-offs between number of interventions and performance. Real-life measurements from the track from Braşov to Zărneşti in Romania are included to show the methodology.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Railway Engineerin

Condition monitoring of railway transition zones using acceleration measurements on multiple axle boxes: Case studies in the Netherlands, Sweden, and Norway

Railway transition zones connecting conventional embankments and rigid struc-tures, such as bridges and tunnels, usually degrade much faster than other railway sections. Efficient health condition monitoring of transition zones is important for preventative track maintenance. In this paper, a methodology for monitoring rail-way transition zones using acceleration measurements on multiple axle boxes (multi-ABA) of a passing train is presented. To showcase its capability, the measurements in the Netherlands, Sweden, and Norway are analyzed and dis-cussed. It is found that different bridges and transition zones exhibit unique char-acteristics including dominant wavelengths and energy distribution. Based on these unique characteristics, the geometry and support conditions at different lo-cations of a transition zone can be evaluated. Higher train speed makes the char-acteristics more pronounced. The results demonstrate that the multi-ABA meas-urement has the potential to evaluate and thus monitor the health conditions of various transition zones.Railway Engineerin

Detection of Rail Surface Defects based on Axle Box Acceleration Measurements: A Measurement Campaign in Sweden

This work presents the results of a measurement campaign to demonstrate the effectiveness of the axle box acceleration (ABA) technology for detecting rail defects. The measurements were conducted along the Iron Ore line between Sweden and Norway for the IN2TRACK3 project. This line is mostly single-track with passenger-freight mixed traffic and heavy axle load. Historical data and track information data were not considered in this study. By analyzing data acquired from the accelerometers in vertical and longitudinal directions, rail defects were detected in near real-time using big-data analytics. For our validated sections, 100% of rail defects (including squats) were detected using time-frequency analysis and an outlier detection approach. The methodology also allows for identifying priority locations, e.g., defective welds, joints, transition zones, etc., and its use for prescriptive maintenance recommendations is being explored in the framework of the IAM4RAIL project.Railway Engineerin

Smart technology solutions for the NeTIRail-INFRA case study lines: Axle box acceleration and ultra-low cost smartphones

In this paper, we present preliminary results on the development of smart technology solutions for lower density railway lines. The goal is to reach a cost effective inspection and asset management to minimize maintenanceinterventions time/cost without dedicated inspection vehicles. The proposed methods include axle box acceleration measurements and ultra-low cost smartphones. The collected data will be further used to increase knowledge of the condition of the railway track and to estimate the comfort of passengers. In order to make use of the data, the data is interpreted and converted from monitoring information into management information. Feasibility and preliminary studies were conducted in the railway lines of Romania. The results presented in this paper were obtained in the framework of the H2020 project NeTIRail-INFRA, Work Package 4: Monitoring and Smart Technology.Railway Engineerin