Now showing 1 - 5 of 5
  • Publication
    A New Damage Indicator for Drive-by Monitoring using Instantaneous Curvature
    Drive-by monitoring has enhanced the possibilities for bridge damage detection, with the potential to deliver a bridge rating in the time it takes an instrumented vehicle to pass overhead. This paper outlines the importance of Instantaneous Curvature (IC) as an indicator of local damage. For the IC calculation, bridge deflections are measured from the vehicle before and after the occurrence of damage, so that a comparison between the two situations can be made. Differences in curvature are clearly visible in numerical simulations, especially at the damage location. A Finite Element model of a simply supported bridge subject to a crossing vehicle is modelled dynamically. In this paper, the Curvature Ratio (CR) is proposed as the damage indicator, defined as the ratio of IC in the current bridge to IC in the corresponding healthy bridge. Road profile and random noise in the simulated measurements are considered to represent realistic conditions. Simulations in MATLAB demonstrate that CR is an effective indicator in most of the analysis cases.
      288
  • Publication
    The feasibility of using Laser Doppler Vibrometer measurements from a passing vehicle for bridge damage detection
    Bridges play an important role in transport infrastructure and it is necessary to frequently monitor them. Current vibration-based bridge monitoring methods in which bridges are instrumented using several sensors are sometimes not sensitive enough. For this reason, an assessment of sensitivity of sensors to damage is necessary. In this paper a sensitivity analysis to bridge flexural stiffness (EI) is performed. A discussion between the use of strain or deflections is provided. A relation between deflection and stiffness can be set by theorem of virtual work, expressing the problem as a matrix product. Sensitivity is obtained by deriving the deflection respect to the reciprocal of the stiffness at every analysed location of the bridge. It is found that a good match between the deflection and the bridge stiffness profile can be obtained using noise-free measurements. The accuracy of sensors is evaluated numerically in presence of damage and measurement noise. Field measurements in the United States are also described to identify the potential issues in real conditions.
    Scopus© Citations 26  311
  • Publication
    Damage detection using curvatures obtained from vehicle measurements
    This paper describes a new procedure for bridge damage identification through drive-by monitoring. Instantaneous curvature (IC) is presented as a means to determine a local loss of stiffness in a bridge through measurements collected from a passing instrumented vehicle. Moving reference curvature (MRC) is compared with IC as a damage detection tool. It is assumed that absolute displacements on the bridge can be measured by the vehicle. The bridge is represented by a finite element (FE) model. A Half-car model is used to represent the passing vehicle. Damage is represented as a local loss of stiffness in different parts of the bridge. 1% random noise and no noise environments are considered to evaluate the effectiveness of the method. A generic road surface profile is also assumed. Numerical simulations show that the local damage can be detected using IC if the deflection responses can be measured with sufficient accuracy. Damage quantification can be obtained from MRC.
    Scopus© Citations 21  640
  • Publication
    TRUSS Training in Reducing Uncertainty in Structural Safety: D5.2 Final Report: WP5 - Rail and Road Infrastructure
    This deliverable reports on the outputs of eight Early Stage Researchers (ESR7-ESR14) in work package, WP5 (Rail and Road Infrastructure), under the supervision of academic and industrial experts during the three years of their projects within the EU TRUSS (Training in Reducing Uncertainty in Structural Safety, 2015-2018) Innovative Training Network (ITN) programme (http://trussitn.eu/). Two types of infrastructure are analysed in WP5: bridges (ESR7-ESR12) and pavements (ESR13-ESR14). The first six projects aim to reduce uncertainty in bridge safety. They address areas of work such as bridge condition assessment (ESR7), probabilistic modelling of bridge damage using damage indicators (ESR8), railway bridge condition monitoring and fault diagnostics (ESR9), condition assessment based on measured vibration level (ESR10), the use of optical fibre distributed sensing for monitoring (ESR11), and the use of displacement and velocity measurements for damage localisation (ESR12). The last two projects are on uncertainty in pavement safety, where ESR13 considers the use of truck sensors for road pavement performance and asset management and ESR14 investigates the possibility of using unmanned aerial vehicles and photogrammetry method for road and bridge inspections. Generally, the areas of work developed in this work package are vehicle-infrastructure interaction, traffic load modelling, road materials, uncertainty modelling, reliability analysis, field measurement and Structural Health Monitoring (SHM) of bridges.
      863
  • Publication
    Structural Health Monitoring Developments in TRUSS Marie Sklodowska-Curie Innovative Training Network
    This paper reports on recent contributions by the Marie Sklodowska-Curie Innovative Training Network titled TRUSS (Training in Reducing Uncertainty of Structural Safety) to the field of structural safety in rail and road bridges (http://trussitn.eu). In TRUSS, uncertainty in bridge safety is addressed via cost efficient structural performance monitoring and fault diagnostics methods including: (1) the use of the rotation response due to the traffic traversing a bridge and weigh-in-motion concepts as damage indicator, (2) the combination of design parameters in probabilistic context for geometrical and material properties, traffic data and assumption on level of deterioration to evaluate bridge safety (via Bayesian updating and a damage indicator based on real time measurement), (3) the application of a fuzzy classification technique via feature selection extracted using empirical mode decomposition to detect failure, and (4) the testing of alternative vibration based damage sensitive features other than modal parameters. Progress has also been made in improving modern technologies based on optical fiber distributed sensing, and sensors mounted on instrumented terrestrial and on aerial vehicles, in order to gather more accurate and efficient info about the structure. More specifically, the following aspects have been covered: (a) the spatial resolution and strain accuracy obtained with optical distributed fiber when applied to concrete elements as well as the ideal adhesive, and the potential for detecting crack or abnormal deflections without failure or debonding, (b) the possibility of using the high-resolution measurement capabilities of the Traffic Speed Deflectometer for bridge monitoring purposes and, (c) the acquisition of bridge details and defects via unmanned aerial vehicles.
      287