Now showing 1 - 8 of 8
  • Publication
    Profile Calculation and Bridge Damage Detection Using Vehicle-based Inertial Readings and the Fleet Monitoring Concept
    (University College Dublin. School of Civil Engineering, 2022) ;
    The aim of this research is to use inertial vehicle sensor data to determine road and rail profiles and to monitor bridge condition. A novel fleet monitoring concept is developed to determine profiles and detect bridge damage using a fleet of instrumented vehicles. To improve the robustness of the calculation, a Bayesian updating method is used. To calculate the profile from vehicle response, a novel Inverse Newmark-Beta method is developed. Newmark-Beta allows vehicle acceleration to be calculated in response to an excitation such as a surface profile. Inverse Newmark-Beta finds the excitation corresponding to a known acceleration. For a single vehicle, the profile can be found if the vehicle properties are known. However, for a single vehicle, acceleration by itself is not enough to determine both profile and vehicle properties. Fortunately, a fleet of vehicles provides additional information that can be used to address this problem. To solve the fleet monitoring problem, the Inverse Newmark-Beta method is combined with the Cross Entropy (CE) optimisation method. Here, the road profile is calculated using accelerations from multiple vehicles, without prior knowledge of the vehicle properties. Sprung mass and half-car models are used to represent the vehicle and test this method separately. Numerical results show that the calculated profiles are the same as the ‘true’ profiles. The absolute values of the vehicle properties are not obtained but this algorithm can determine the relative values. Noise added to the accelerations has an influence on the calculated results. The fleet monitoring concept is used again to determine a flexible railway profile. The ‘apparent profile’(AP) of the railway track is defined as the true surface profile plus components of track deflection. Again, the Inverse Newmark-Beta method and CE optimisation are used together to solve this problem. Here, the train is simulated as a 4-axle carriage model and the railway track is represented by a beam supported on spaced sprung masses. The calculated AP of railway track is found to be very close to the true one. Since the previous method is sensitive to noise, the fleet monitoring concept is also solved using a Bayesian Updating method. The road profile is again determined using vehicle measurements. The calculated road profile is close to the true profile and is insensitive to noise in the simulated measurements. In addition, it can determine the relative vehicle properties at the same time. A 3-D ‘carpet’ road profile is also tested and shows good results. This thesis goes on to use similar principles of fleet monitoring to assess bridge condition. Firstly, a novel method is proposed to calculate the moving reference influence line (MR-IL), i.e., the deflection due to a moving (static) unit load at the (moving) location of that load. The results show that the MR-IL can indicate the condition of a bridge. The AP of a railway bridge is used to calculate the MR-IL. This numerical approach is assessed using a blind test operated by an independent research group. In the blind test, a frame structure is used to model the railway bridge and different levels of global damage are simulated. Using a 4-axle train carriage model, the damage levels of the bridge are inferred accurately. When a half car model is used to represent the train bogie, damage levels can be found again with less accuracy. The bridge damage is then detected using the Bayesian Updating method, with drive-by data. For local damage, the second moments of area of each segment of the bridge is updated as data becomes available. It is shown in simulations that estimates of the bridge second moments of area can be found even with local damage. The vehicle mass can be calculated. Bridge bearing damage is also simulated in this section. Using the Bayesian method, the value of bearing rotational spring stiffness, bridge second moments of area and vehicle masses can be calculated.
  • Publication
    Indirect Monitoring of Railway Bridges by Direct Integration
    Railway bridges are of importance as critical elements in transportation networks. Unfortunately, many railway bridges are old and these structures are subject to degradation over time. To monitor bridge structures, many methods are introduced. In recent years, indirect bridge monitoring methods have become more popular. These methods use passing vehicles to measure dynamic responses such as accelerations. In this paper, a new direct integration approach is introduced to directly calculate the apparent railway track profile (AP) that is consistent with the measured accelerations. An adaptation of the Newmark Beta numerical method is used for this purpose. Using AP, bridge displacement profile difference (BDPD) is calculated to monitor bridges. The BDPD is the difference between the baseline (healthy) profile and the apparent profile after damage and environmental effects. BDPD is sensitive to temperature change and bridge damage. It has its own frequency which is close to the bridge frequency.
  • Publication
    A Two-Stage Direct Integration Approach to Find The Railway Track Profile Using In-Service Trains
    (Civil Engineering Research Association of Ireland, 2020-08-28) ; ;
    The railway track is an important element in transportation networks. In recent years, drive-by monitoring of railways has become more popular. Using data measured from in-service trains, the railway profile can be found. In previous research, a complex optimiziton method is used to calculate the railway profile. This paper introduces a new two-stage direct integration approach to find the same track profile much more efficiently. The calculated track profile is similar to a ‘true’ profile and can be used to monitor the condition of the track.
  • Publication
    Drive-by damage monitoring of transport infrastructure using direct calculation of the profile
    Roads and railway tracks are a major focus of interest in transport infrastructure monitoring. Settlement in a road or railway track profile changes the dynamic excitation applied to passing vehicles. This, in turn, results in a changed dynamic response in the original source of loading, such as a passing vehicle. These changes in dynamic excitation make it possible to detect damage in transport infrastructure from the vehicle response. In this paper, the profile is calculated using accelerations in a passing vehicle and used to monitor transport infrastructure.
      384Scopus© Citations 1
  • Publication
    Determination of road profile using multiple passing vehicle measurements
    (Taylor & Francis, 2019-12-19) ; ;
    This paper describes a novel method to determine a road profile through the analysis of accelerations in a passing vehicle. A direct integration algorithm is proposed to determine the profile from the measured vehicle acceleration response. A sprung mass model and a half-car model are used to represent the vehicles in separate analyses. Combining the direct integration algorithm with the Cross Entropy (CE) optimisation method, a vehicle fleet monitoring concept is proposed for the monitoring of roads and/or bridges. In this approach, the profile can be calculated using accelerations from multiple vehicles without prior knowledge of the vehicle properties. Numerical results show that calculated profiles are the same as the ‘true’ profiles which were used to generate the ‘simulated measured’ accelerations.
      520Scopus© Citations 22
  • Publication
    Fleet Monitoring - Using Sensors in a Fleet of Passing Vehicles to Monitor the Health of Bridges
    This paper proposes the use of a fleet of instrumented vehicles to monitor the condition of infrastructure and bridges. It is anticipated that data from privately owned vehicles with low-cost accelerometer and GPS data, will be available for this purpose in the future. An inverse version of the well known Newmark-Beta method is proposed to determine road/rail surface profile from measured accelerations. Some results are reported from an instrumented train that made repeat runs on railway track over a period of a month. For bridge health monitoring, the concept of a moving reference influence line is proposed as a damage indicator. It is shown in simulation to give good indications of bearing damage in a simply supported bridge.
  • Publication
    Railway Bridge Condition Monitoring Using Numerically Calculated Responses from Batches of Trains
    This study introduces a novel method to determine apparent profile of the track and detect railway bridge condition using sensors on in-service trains. The concept uses a type of Inverse Newmark-β integration scheme on data from a batch of trains. In a self-calibration process, an optimization algorithm is used to find vehicle dynamic properties and speed. For bridge health monitoring, the apparent profile of the bridge is first determined, i.e., the true profile plus components of ballast and bridge deflection under the moving train. The apparent profile is used, in turn, to calculate the moving reference deflection influence line, i.e., the deflection due to a moving (static) unit load. The moving reference influence line is shown to be a good indicator of bridge stiffness. This numerical approach is assessed using an elaborate finite element model operated by an independent research group. The results show that the moving reference influence line can be found accurately and that it constitutes an effective indicator of the condition of a bridge.
      141Scopus© Citations 6
  • Publication
    A Direct Integration Approach to Drive-by Damage Monitoring of Railway Tracks
    Railway tracks can be monitored by visual inspection or, more recently, indirectly using inertial sensors installed in a passing vehicle. Defects in the track such as depressions in the profile or points of low stiffness (e.g. hanging sleepers) interact dynamically with passing vehicles and can be detected with accelerometers and gyrometers. This can be achieved using special purpose track recording vehicles or through instrumentation of regular trains in service. It has been shown in previous research that an optimisation procedure can be applied to back-calculate track profiles from vehicle-mounted sensor data. This involves finding the profile that gives a best fit to the measured data. In this paper, a new direct integration approach is introduced to find the same track profile in a fraction of the computing time. The Newmark-Beta method is used. Compared with the profile calculated using the optimisation algorithm, the results are similar. However, direct integration is much more efficient than optimisation and allows the calculation to be completed in a fraction of the time. The calculated track profile can be used to estimate points of low stiffness.