Now showing 1 - 10 of 10
  • 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
    TRUSS, a European Innovative Training Network Dealing with the Challenges of an Aging Infrastructure Network
    Inspections and maintenance of infrastructure are expensive. In some cases, overdue or insufficient maintenance/monitoring can lead to an unacceptable risk of collapse and to a tragic failure as the Morandi bridge in Genoa, Italy, on 14th August 2018. An accurate assessment of the safety of a structure is a difficult task due to uncertainties associated with the aging and response of the structure, with the operational and environmental loads, and with their interaction. During the period from 2015 to 2019, the project TRUSS (Training in Reducing Uncertainty in Structural Safety) ITN (Innovative Training Network), funded by the EU H2020 Marie Curie-Skłodowska Action (MSCA) programme, has worked towards improving the structural assessment of buildings, energy, marine, and transport infrastructure. Fourteen Early Stage Researchers (ESRs) have been recruited to carry out related research on new materials, testing methods, improved and more efficient modelling methods and management strategies, and sensor and algorithm development for Structural Health Monitoring (SHM) purposes. This research has been enhanced by an advanced program of scientific and professional training delivered via a collaboration between 6 Universities, 1 research institute and 11 companies from 5 European countries. The high proportion of companies participating in TRUSS ITN has ensured significant industry expertise and has introduced a diverse range of perspectives to the consortium on the activities necessary to do business in the structural safety sector.
      219
  • Publication
    Damage Detection by Drive-by Monitoring Using the Vertical Displacements of a Bridge
    (CRC Press (Taylor & Francis), 2016-09-07) ; ;
    Drive-by monitoring has received increasing attention in recent years, as it has great potential useful for Structural Health Monitoring (SHM) applications. Although direct instrumentation of civil infrastructures has been demonstrated to be a way of detecting damage, it is also a very expensive method as it requires data acquisition, storage and transmission facilities on each bridge. Drive-by constitutes an alternative that allows the monitoring of a bridge without the necessity of installing sensors on it. In this numerical study, the vertical displacements of the bridge are used for damage detection purposes. The goal of this paper is to describe a model that can reproduce the vertical displacements of the bridge when a simulated vehicle is driving through and show how these displacements change with damage. Vertical displacements are calculated before and after damage, so that the sensitivity of the data to bridge damage can be determined. A finite element (FE) model of a simply supported beam interacting with a moving half car is used in this study. Damage is represented as a loss of stiffness in several parts of the bridge. Vertical displacements are generated at a moving reference for healthy and damaged states, corresponding to vehicle location on the bridge. Two options are explored, the first axle and the second one, as the locations to fix the simulated sensor on the vehicle.
      506Scopus© Citations 5
  • Publication
    Instantaneous Curvature in Bridge Damage Detection
    Among all the Structural Health Monitoring (SHM) recent methods found in literature, drive-by monitoring has demonstrated to be promising for damage detection purposes, particularly in bridges. As curvatures can be derived from displacement measurements taken by this method, they can also be used for damage detection, which has already been successfully demonstrated. This paper describes the use of Instantaneous Curvature (IC) for that purpose. Once the absolute displacements of the bridge are measured, damage location and quantification can be obtained through IC when having a moving reference over a bridge. In this paper, a bridge is represented by a finite element model of a Euler-Bernoulli beam. A Half-Car model of a vehicle is used to represent a Traffic Speed Deflectometer (TSD), a drive-by monitoring vehicle. Damage is represented as a loss of stiffness in different parts of the bridge and 1 % measurement noise is added. A generic road profile is also considered. Healthy and damaged states of the bridge are compared in order to validate the method.
      235
  • 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
  • Publication
    Drive-by Bridge Damage Detection Using Curvatures in Uncertain Environments
    (Civil Engineering Research Association of Ireland, 2016-08-30) ; ;
    Considerable effort has been dedicated in recent years to the development of bridge damage detection techniques. Recently, drive-by monitoring has become popular as it allows the bridge to be monitored without installing sensors on it. In this work, the Traffic Speed Deflectometer (TSD), which incorporates a set of laser Doppler sensors on a straight beam to obtain the relative velocity between the vehicle and the pavement surface, is modelled to obtain deflections on the bridge as the vehicle drives. From these deflections it is possible to obtain the curvature of the bridge, from which inferences on damage can be made. However, most of the time, the measurements taken by drive-by sensors are subject to a set of uncertainties or noise that can lead the damage detection procedure to either give false positives or to miss damage. For that reason, an analysis is needed in order to determine if these methods can work properly in uncertain or noisy environments. Moreover, as the road surface roughness affects the dynamic interaction between the vehicle and the bridge, this may also have an effect on the damage predictions. Hence, the goal of this paper is to study the sensitivity of curvature measurements to both the presence of environmental noise and the effect of the road surface roughness.
      300
  • Publication
    TRUSS, a European innovative training network dealing with the challenges of an aging infrastructure network
    Inspections and maintenance of infrastructure are expensive. In some cases, overdue or insufficient maintenance/monitoring can lead to an unacceptable risk of collapse and to a tragic failure as the Morandi bridge in Genoa, Italy, on 14th August 2018. An accurate assessment of the safety of a structure is a difficult task due to uncertainties associated with the aging and response of the structure, with the operational and environmental loads, and with their interaction. During the period from 2015 to 2019, the project TRUSS (Training in Reducing Uncertainty in Structural Safety) ITN (Innovative Training Network), funded by the EU H2020 Marie Curie-Skłodowska Action (MSCA) programme, has worked towards improving the structural assessment of buildings, energy, marine, and transport infrastructure. Fourteen Early Stage Researchers (ESRs) have been recruited to carry out related research on new materials, testing methods, improved and more efficient modelling methods and management strategies, and sensor and algorithm development for Structural Health Monitoring (SHM) purposes. This research has been enhanced by an advanced program of scientific and professional training delivered via a collaboration between 6 Universities, 1 research institute and 11 companies from 5 European countries. The high proportion of companies participating in TRUSS ITN has ensured significant industry expertise and has introduced a diverse range of perspectives to the consortium on the activities necessary to do business in the structural safety sector.
      243