Instantaneous damage detection of bridge structures and experimental verification

Abstract

An extended Kalman filtering (EKF) method was developed and applied to instantaneously identify elemental stiffness values of a structure during damaging seismic events based on vibration measurement. This method is capable of dealing with nonlinear as well as linear structural responses. Identification of the structural elemental stiffness enables location as well as quantification of structural damage. The instantaneous stiffness values during an event can provide highly useful information for post-event capacity estimation. In this study, a large-scale shaking table test of a three-bent concrete bridge model was performed in order to verify the proposed damage detection method. The bridge model was shaken to different damage levels by a sequence of earthquake motions with increasing intensities. The elemental stiffness values of the structure were instantaneously identified in real time during the damaging earthquake excitations using the EKF method. The identified stiffness degradations and their locations agreed well with the structural damage observed by visual inspection and strain measurements. More importantly, the seismic response accelerations analytically simulated using the instantaneous stiffness values thus identified agreed well with the measured accelerations, demonstrating the accuracy of the identified stiffness. This study presents an experimental verification of a structural damage detection method using a realistic bridge model subjected to realistic seismic damage. cause safety hazards to the traveling public, halt mobility of the transportation network, and disrupt emergency response. The current practice relies on visual inspection for damage detection, which is time consuming, insufficient, subjective, and requires physical presence of the crew on the structure that is potentially hazardous after major natural or man-made events such as earthquakes and accidents.Structural condition assessment of highway bridges has long relied on visual inspection, which involves subjective judgment of inspectors and detects only local and visible flaws. The frequency of visual inspection and the qualification of the inspectors were regulated by the National Bridge Inspection Standards [1]. The Federal Highway Administration (FHWA) Recoding and Coding Guide [2] also provides guidance in terms of the condition ratings and the documentation in current practice. Even with these provisions, a recent investigation initiated by FHWA to examine the reliability of visual inspections reveals significant variability in the structural condition assignments by inspectors [3]. Moreover, visual inspection cannot quantitatively evaluate the strength and/or deformation capacity reservation of a bridge. The Long-term Bridge Performance Program (LTBP) was recently initiated by FHWA, exploring sensor-based continuous monitoring of bridges under traffic conditions as well as during extreme events [4].Sensor-based structural health monitoring (SHM) can revolutionize the way of inspecting structures, particularly for post-ev...

Simultaneous identification of bridge structural parameters and vehicle loads

“…From Eq. (22), provided that the system parameters in H can be identified (as introduced in Section 3.1), then the unknown external force vector F could be determined from the measured Y.…”

“…When applying the Bayesian inference approach to solve the unknown forces, the unknown vector F in Eq. (22) can be encapsulated in the posterior probability density function (PDF) p F; r 2 ; s Y …”

“…In summary, existing extensive vibration-based bridge system identification methods can be categorized as follows: (1) studies focusing on identification of bridge parameters based on dynamic measurements due to ambient excitations [16][17][18], earthquake excitations [19][20][21][22] or traffic-induced bridge responses with known traffic loads [23][24][25][26]; (2) the identification of dynamic vehicle axle loads or moving forces by assuming known bridge parameters; and (3) identification of both bridge and vehicle parameters with a full sensor placement or using measurements from both the bridge and vehicle subsystems.…”

Damage detection of framed structures subjected to earthquake excitation using discrete wavelet analysis

“…The damaged region can be determined by the spatial distribution pattern of the observed spikes. Soyoz and Feng (2007) applied wavelet analysis for damage detection in bridge structures both numerically and experimentally. Noh et al (2011Noh et al ( , 2012, introduced three wavelet-based damagesensitive features (DSFs) which are defined as functions of wavelet energies at particular frequencies and specific times.…”

Structural damage detection using extended Kalman filter combined with statistical process control

“…Furthermore, it is feasible to build continuous and automatic structural damage detection system for in-service structures. Many successful applications of EKF-based structural damage detection method have been reported in the literature [2]- [5]. Yang et al developed an adaptive extended Kalman filter(AEKF) method to track changes of structural parameters, and tested in numerical simulations for both linear and nonlinear structures [2].…”