Drive-by damage detection in bridges using the apparent profile

O’Brien, Eugene J.; Keenahan, Jennifer

doi:10.1002/stc.1721

Cited by 72 publications

(48 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These model‐based techniques differ from purely data‐based approaches that researchers are working to develop that can detect, localize, and quantify evolving damage in structural systems. These techniques measure and monitor vibration profiles, mode shapes, or other data about the structure to either identify damage without having a baseline reading or in a continual monitoring fashion, where algorithms detect change in these vibration profiles corresponding to damage . An advantage of this class of approaches is that an accurate computer model of the structure is not required for comparison.…”

Section: Introductionmentioning

confidence: 99%

“…These techniques measure and monitor vibration profiles, mode shapes, or other data about the structure to either identify damage without having a baseline reading 15,16 or in a continual monitoring fashion, where algorithms detect change in these vibration profiles corresponding to damage. [17][18][19] An advantage of this class of approaches is that an accurate computer model of the structure is not required for comparison. Despite some of the inherent difficulties in creating an accurate model of a complex structural system, however, model-based techniques can, once model error is reduced or compensated for, provide a clear and intuitive indication of the presence and severity of structural damage through the inverse analysis process.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Extended constitutive relation error‐based approach: The role of mass in damage detection

Chodora

Prabhu³

et al. 2019

Struct Control Health Monit

View full text Add to dashboard Cite

Summary This paper presents an extended constitutive relation error (ECRE)‐based method for detecting damage in structural systems. Although most ECRE‐based methods for damage detection have relied on an iterative model calibration process, the approach advocated in this study uses residual energy to identify structural damage. Residual energy represents the spatial distribution of discrepancies between model predictions of the undamaged system and experimental measurements obtained from the damaged system. The current study calculates this residual energy by tightly linking experimentally measured vibration data to an associated numerical model. The approach implemented in this paper, referred to as the M‐K ECRE‐based approach, differs from previous ECRE‐based damage detection methods in that it incorporates both unbalanced elastic and unbalanced inertial forces in the calculation of residual energy. The rationale for including unbalanced inertial forces is that damage can also alter the mass distribution of a structure in addition to its influence on the distribution of structural stiffness. By considering unbalanced inertial forces, the proposed approach improves the calculation of residual energy, which in turn leads to an improvement in the method's ability to identify damage. At the end of this study, the efficiency of the M‐K ECRE‐based approach to identify damage is demonstrated on a scaled two‐story steel frame that is subjected to varying types and severities of structural damage.

show abstract

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Extended constitutive relation error‐based approach: The role of mass in damage detection

Chodora

Prabhu³

et al. 2019

Struct Control Health Monit

View full text Add to dashboard Cite

show abstract

“…It is particularly promising for short and medium span bridges. Several methods have been proposed in recent years using indirect measurements for damage detection purposes [23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

“…It uses a set of Doppler laser vibrometers to measure the vertical velocity between vehicle and road profile under the rearright tyre [47]. OBrien & Keenahan [25] first propose the use of a TSD for drive-by bridge monitoring, but do not address the issue of finding absolute deflection measurements from the measured relative velocities.…”

Section: Introductionmentioning

confidence: 99%

Damage detection using curvatures obtained from vehicle measurements

O’Brien

Martínez

Malekjafarian

et al. 2017

J Civil Struct Health Monit

View full text Add to dashboard Cite

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.

show abstract

“…3,4 As new mobile, [5][6][7][8] heterogeneous, [9][10][11][12] smart, [13][14][15][16] wireless, and distributed [17][18][19][20][21][22] sensing technologies emerge, SHM systems have become more practical, cost-effective, and sustainable not only for laboratory but also for field applications.…”

Section: Introductionmentioning

confidence: 99%

Biomechanically influenced mobile and participatory pedestrian data for bridge monitoring

Feng

2017

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Future structural health monitoring systems are evolving toward crowdsourced, autonomous, sustainable forms based on which damage-indicative structural features can be identified. Unlike conventional sensor systems, they serve as nonstationary, mobile, and distributed sensor network components. For example, smartphone sensors carried by pedestrians decouple from the structure of interest, making it difficult to measure structural vibration. Taking bridges as instances, smartphone sensor data contain not only the bridge vibration but also the pedestrians' biomechanical features. In this article, pedestrians' smartphone data are used to conduct force estimation and modal identification for structural health monitoring purposes. Two major pedestrian activities, walking and standing, are adopted to estimate walk-induced forces on structures and identify modal parameters, respectively. First, vibration time history of a walking pedestrian combined with pedestrian weight is a measure of dynamic forces imposed on the structure. Second, standing pedestrian's smartphone sensors provide spectral peaks which are mixtures of structural and biomechanical vibrations. Eliminating biomechanical content reveals structural modal properties which are sensitive to structural integrity. This study presents the first structural health monitoring application recruiting pedestrians in a testbed bridge monitoring example. Orchestrating pervasive and participatory pedestrian data might bring new frontiers to structural health monitoring through a smart, mobile, and urban sensing framework.

show abstract

Drive-by damage detection in bridges using the apparent profile

Cited by 72 publications

References 33 publications

Extended constitutive relation error‐based approach: The role of mass in damage detection

Extended constitutive relation error‐based approach: The role of mass in damage detection

Damage detection using curvatures obtained from vehicle measurements

Biomechanically influenced mobile and participatory pedestrian data for bridge monitoring

Contact Info

Product

Resources

About