The gradual deterioration of train wheels can increase the risk of failure and lead to a higher rate of track deterioration, resulting in less reliable railway systems with higher maintenance costs. Early detection of potential wheel damages allows railway infrastructure managers to control railway operators, leading to lower infrastructure maintenance costs. This study focuses on identifying the type of sensors that can be adopted in a wayside monitoring system for wheel flat detection, as well as their optimal position. The study relies on a 3D numerical simulation of the train-track dynamic response to the presence of wheel flats. The shear and acceleration measurement points were defined in order to examine the sensitivity of the layout schemes not only to the type of sensors (strain gauge and accelerometer) but also to the position where they are installed. By considering the shear and accelerations evaluated in 19 positions of the track as inputs, the wheel flat was identified by the envelope spectrum approach using spectral kurtosis analysis. The influence of the type of sensors and their location on the accuracy of the wheel flat detection system is analyzed. Two types of trains were considered, namely the Alfa Pendular passenger vehicle and a freight wagon.

Due to the increasing demand for safer and faster rail transport, train wheelsets operating under high axle loads require more careful and reliable inspections and maintenance. During service, the train wheelsets are constantly operating under harsh conditions such as fatigue, thermal variation and impact. The wheel defects can induce damage to railway tracks or even derailments, increasing costs for both railway administrations and rolling stock operators. Therefore, early detection of wheel defects may prevent significant damages that could lead to service interruptions or derailments. The purpose of this research is to present an approach to detect the presence of wheel flats using an envelope spectrum analysis, as well as to test, discuss and analyse the sensitivity of the proposed approach to the unevenness of the track, the random position of the wheel flat impact occurrence and the severity of the flat. Subsequently, the sensitivity of the proposed method is tested to accurately detect a wheel flat when the signal is perturbed by different noise intensities. A wide range of 3D simulations based on a train–track interaction model has been performed for different train speeds and different types of flat geometries. From the obtained results, it is evident that envelope spectrum analysis is a capable tool and a cost-effective method that can be used to detect wheel flats along with the flat impact frequency for different train speeds in real-world conditions.

Wheel defects can induce damage to the railway tracks, increasing considerable maintenance costs for both railway administrations and rolling stock operators. This paper aims to develop an unsupervised early damage detection methodology, capable of automatically distinguishing a defective wheel from a healthy one, with respect to the small flat size. The proposed methodology is based on the acceleration and shear time histories evaluated on the rails for the passage of traffic loads, and involves the following steps: (i) data acquisition from sensors; (ii) feature extraction from acquired responses with continuous wavelet transform (CWT) model; (iii) feature normalisation to suppress environmental and operational variations; (iv) data fusion to merge the features from each sensor and enhance sensitivity to detect wheel defects; and (v) feature classification to classify the extracted features into two categories: a healthy wheel or a defective one. The shear and acceleration measurement points are strategically defined in order to examine the sensitivity of the proposed methodology, not only to the type of sensors, but also to the position where they are installed. It has been demonstrated that one sensor can detect a defective wheel automatically, allowing the development of an easy-to-implement low-cost monitoring system.

This paper focuses on the evaluation of the seismic vulnerability of pre-1990 highway concrete bridges, based on the development of analytical fragility curves. The parameters considered are the span length, column height, lap splices, reinforcement yield strength, and concrete compressive strength. Nonlinear response history analyses using 3D models were conducted for each set of bridge samples subjected to earthquake ground motions with different intensities. Moreover, this study analyzes the influence of the earthquake fault type on the seismic vulnerability of the bridges, using as a performance parameter the displacement ductility demand of the piers. Based on this parameter, to support future seismic-risk mitigation efforts fragility curves are utilized to assesses the seismic vulnerability of typical concrete bridge classes in Iran. The selected suite of seismic records originates in strike-slip and reverse fault seismic sources. The bridges subjected to reverse fault records present pier demands and seismic vulnerability larger than the bridges subjected to strike-slip accelerograms. The study shows that the span length, lap splice, column height, and seismic fault type have significant effects on the seismic vulnerability of bridge piers. Additionally, the results are useful to identify and prioritize retrofit actions of the most seismically vulnerable bridges.

The present paper focuses on the numerical modeling of a weigh-in-motion system developed with the purpose of assessing the static loads imposed by the train onto the track infrastructure. Weigh-in-motion systems would be useful in overcoming the disadvantages typical of the conventional static weighing such as costs and traffic management. However, contrary to the conventional static weighing, weigh-in-motion systems do not allow a direct measurement of the static load since the train–track dynamic interaction gives rise to dynamic loads that are added to the static ones. This study investigates how train speed and track unevenness affect the loads assessed by the weigh-in-motion system. In order to achieve that goal, a comprehensive statistical study was performed based on an extensive amount of calculations. Finally, based on the conclusions and trend identified through the comprehensive parametric study, an approach is proposed to correct the direct result given by the weigh-in-motion system in order to obtain an estimation of the static load.