In this paper, a field study is carried out to monitor the natural frequencies of Malahide viaduct bridge which is located in the north of Dublin. The bridge includes a series of simply supported spans, two of which collapsed in 2009 and were replaced. The replaced spans are stiffer than most of the others and these differences resulted in higher natural frequencies. An indirect bridge monitoring approach is employed in which acceleration responses from an instrumented train are used to estimate the natural frequencies of each span of the viaduct showing the locations of the two replaced spans with higher stiffness. For the indirect approach, an Ensemble Empirical Mode Decomposition (EEMD)-based Hilbert Huang Transform (HHT) technique is employed to identify the natural frequency of each span. This is carried out by analysing the Instantaneous Frequencies (IFs) from the calculated intrinsic mode functions. The average of the IFs calculated using 41 runs of the instrumented train (with varying carriage mass and speed for each run) are used to estimate the natural frequencies. To assess the feasibility of the indirect approach, a bespoke set of direct measurements was taken using accelerometers attached successively on each span of the viaduct. The free and forced vibrations from each span are used to estimate the first natural frequencies. The frequencies obtained from drive-by measurements are compared to those from direct measurements which confirms the effectiveness of indirect approaches. In addition, the instantaneous amplitudes of the drive-by signals are used to indicate the location of the stiffer spans. Finally, the accuracy and robustness of the indirect approaches for monitoring of multi span bridges are discussed.

The static and dynamic effects of a railway bridge subjected to train loads have been investigated and analyzed to assess its condition. This paper investigates the potential of using repeated dynamic measurements taken on a passing train to determine the condition of a bridge. The results indicated that instrumented trains could be successfully used for ongoing monitoring of condition and, by implication, for identification of the need for repair or rehabilitation. This full-scale approach expands the potential for applications of bridge–vehicle dynamic interaction responses, along with their ability to be demonstrators of successful implementations of decisions on public infrastructure. The work also provided insights into the impact of a stiffer foundation at one pier and greater stiffness in two spans of a viaduct, for which experimental evidence is rarely available. The work also identified some of the challenges of such detection, such as accurate positioning and variability in the speeds of the passing trains.

AbstractScour in railway bridges is often an important problem, especially for old bridges. In this regard, the assessment of full-scale bridges to establish scour repair adequacy or efficiency is an important question. Typically, scour weakens the bridge structure by modifying its boundary conditions. Such changes, when significant enough, can lead to changes in modal properties and in vibrations measurements with respect to the ideal baseline. On the other hand, a repair attempts to restore the release in boundary conditions. Consequently, a repair also changes the modal properties and the dynamic responses. This indicates that significant and consistent changes of bridges before and after repair can indicate the adequacy and efficiency of scour repair. This paper addresses this idea by carrying out a full-scale test on scour repair of a train bridge in Ireland. The bridge is monitored on two abutments with several wireless accelerometers while the bridge is operational. Both ambient and train passage events are considered in terms of collection of vibration data. While it is not possible to discern the difference between repaired and scour-damaged conditions, it is observed that time domain markers may be more consistent in indicating repair efficiency as compared to its frequency domain counterpart. This full-scale demonstration is expected to act as a full-scale example for vibration-based repair monitoring and performance criteria for future detection algorithms. The approach does not require the bridge to be closed down and can be relevant for other sensors as well.KeywordsBridgeScourInstrumentationMonitoringDamageRailwayTrainVibrationMode shapeFrequency-DomainTime-DomainMahalanobis distance

AbstractA well-designed data acquisition (DAQ) system is essential to collect accurate (bridge and track) data over a long-term period. Direct instrumentation of a single component of the network (e.g. sensors installed on a bridge) is costly and offers minimal data. An instrumented train that operates for a long period of time over the entire network can collect a large amount of data at less expense and maintenance of the system. A measurement system installed on an in-service train for monitoring the Irish Rail Network for a duration of twelve months is proposed here to monitor the track and bridge condition. Uni-axial and tri-axial accelerometers are proposed to instrument sprung and un-sprung masses to measure vibration in the vertical direction and along the three axes. The vibration data are transmitted to and stored in the data logger. A high accuracy GPS system is used for referencing the data to geographical coordinates. An existing power supply on the train powers the data acquisition system. Such instrumentation is fundamental to obtaining information around risk assessment and performance evaluation of critical infrastructures, especially due to their inherent uncertainties. With a system like that described in this paper, investigation into these uncertain systems can be carried out in a systematics manner and without having to close the operations of the structure.KeywordsAccelerometerBogieBridgeDAQDesignInstrumentationMonitoringNetworkRailwayTrackTrainVibration

An Irish Rail intercity train was instrumented for a period of one month with inertial sensors. In this paper, a novel calibration algorithm is proposed to determine, with reasonable accuracy, vehicle model parameters from the measured vehicle response data. Frequency domain decomposition (FDD) is used to find the dominant frequencies in the captured data. Randomly chosen 2 km data segments are chosen from a number of datasets, thereby averaging out the effects of variations in track longitudinal profile, track stiffness, signal noise and other unknowns. The remaining dominant peaks are taken to be vehicle frequencies. An optimization technique known as Cross Entropy is used to find vehicle mass and stiffness properties that best match modal vehicle eigenfrequencies identified in the frequency analysis. Finally, the calibrated vehicle is run over a measured track profile and the resulting model output is compared to measured data to validate the results.

Mode shapes are sensitive to the structural condition of bridges but a reasonable estimate of such changes require several accelerometers, which can be resource intensive. This paper obviates this problem through a novel structure health monitoring (SHM) approach for estimating modal parameters of bridges, including damage-induced changes of boundary conditions by using progressively re-deploying sensors along a monitored bridge. This concept of re-deployable sensors and subsequent use of a series of measurements allow extracting data from different bridge segments and also to get an indication of the condition of the bridge through frequency domain decomposition. Data from different segments are combined to estimate the global mode shape of the bridge and its gradient is observed to be indicative of support stiffness change. The concept is successfully tested through a full-scale field trial on a railway bridge in the Republic of Ireland, before and after the rehabilitation of its supports. The results are expected to guide future on-site measurement of damages due to flooding, scour, and other natural hazards, along with the effectiveness of intervention actions like repair and rehabilitation, providing a clear evidence base for practical value of SHM.

This paper describes the results of an exploratory study on the analysis of on-train-data-recorder (OTDR) data. The results are discussed in terms of their applicability in competence management and human factors research. Data were downloaded for 20 journeys at the same time of day, over the same route during 1 month. The data were matched to the drivers rostered to work that route to compare differences within and between drivers. Clear differences were found between drivers in terms of their use of the power and brake levers, but unsurprisingly the network was also found to exert an influence on their use. The station dwell times and overall journey time were found to routinely exceed timetabled allowances, and red signals were frequently encountered across the 20 journeys. The data provide for possibilities to support driver competence management through the identification of desirable driving behaviours, but as yet such indicators have not been validated. The data also provide a wealth of information for human factors research that has applications in better understanding the train driver task and supporting it through improving rules and interface designs.

This paper investigates the use of drive-by train measurements for railway track monitoring. An in-service Irish Rail train was instrumented while using accelerometers and a global positioning system. The measurements were taken over two months and the train bogie accelerations from 60 passes on the Dublin-Belfast line were used for this study. A 6 km section of the line is the particular focus, where the maintenance measurements from a Track Recording Vehicle (TRV) were available. The Hilbert transform is used to obtain the instantaneous amplitudes of the acceleration signals. A new representation of the signal is proposed to show the signal energy level as a function of train location. It is shown that the forward speed of the train has a significant influence on the energy level of the signals. Therefore, a two-step speed correction is applied to the data. First, data from passes with forward speed below a certain limit are removed from the data set. Subsequently, a scaling factor is defined for the remaining signals and the energy levels of those signals are scaled while using online speed measurements. The scaled amplitudes are compared with the TRV data. It is shown that the energy levels of the signals match the TRV measurements very well.

The emergence of systematic condition monitoring of railway infrastructure has the potential to reduce the cost of providing a safe network. The traditional ‘inspect and rectify’ style of maintenance planning is being increasingly complemented by a ‘monitor, predict and prevent’ approach. In order to facilitate this, the frequency of track measurement must be increased from the current periodic measurements using specialised instrumented vehicles. In recent years there has been an increased interest in the challenge of finding railway track longitudinal profile using the response of passing instrumented vehicles as a by-product of regular service. A method is presented where the inertial response of a train bogie is used as input to an optimisation technique that infers the track longitudinal profile. The method finds the track profile that generates a numerical output from a vehicle-track interaction model that best fits a measured response. Experimental data is used to validate the longitudinal profile estimation algorithm. An Irish Rail InterCity train was instrumented to capture in-service vehicle responses. During the testing period, the longitudinal profile of a section of this line featuring a known settlement issue was surveyed by traditional means, for reference. A calibrated vehicle is used in the optimisation algorithm to find the longitudinal profile that generates a numerical vehicle response best fitting the measured data. The known track settlement is found quite well using the calibrated vehicle, thereby validating the method. The reproducibility of the method is assessed. While improvements in accuracy and reproducibility are required to bring the method up to best practice standards, the information provided demonstrates the ability to find localised changes in track profile.