Vibration of a new concrete bridge was monitored and change in the bridge structural stiffness was identified accordingly over a 5-year period. This threespan 111-m long bridge is instrumented with 13 acceleration sensors at both the superstructure and the columns. The sensor data are transmitted to a server computer wirelessly. Modal parameters of the bridge, that is, the frequencies and the modal shapes were identified by processing 1,707 vibration data sets collected under traffic excitations, based on which the bridge structural parameters, stiffness and mass, and the soil spring values were identified by employing the neural network technique. The identified superstructure stiffness at the beginning of the monitoring was 97% of the stiffness value based on the design drawings. In the identified modal frequencies, a variation from −10% to +10% was observed over the monitoring period. In the identified stiffness values of the bridge superstructure, a variation from −3% to +3% was observed over the monitoring period. Based on the statistical analysis of the collected data for each year, 5% decrease in the first modal frequency and 2% decrease in the superstructure stiffness were observed over the 5-year monitoring period. Probability density functions were obtained for stiffness values each year. Stiffness threshold values for the collapse of the bridge under the operational loading can be determined. Then the number of years can be assessed for which the area under the proposed probability density functions is greater than the threshold value. So the information obtained in this study is valuable for studying aging and long-term performance assessment of similar bridges.

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Enabling an automated, remote and rapid detection of structural damage, sensor-based structural health monitoring is becoming a powerful tool for maintenance of civil engineering structures. In this study, a baseline-free, time-domain damage detection method was developed for concrete structures, which is based on analysis of nonlinear damping from measured structural vibration responses. The efficacy of the proposed method was demonstrated through a large-scale concrete bridge model subjected to different levels of seismic damage caused by shaking table tests. By applying the random decrement signature technique, the proposed method successfully identified, from its ambient vibration responses, nonlinear damping of the bridge associated with the seismic damage. The amount of the nonlinear damping increases as the seismic damage becomes more severe. This paper also compares the damage detection results with those obtained by stiffness-based methods, demonstrating a strong correlation between the increase in nonlinear damping and the decrease in structural stiffness associated with the increase in damage severity. valuable for post-seismic damage detection by improving the current visual inspection, which is time consuming, subjective, and potentially dangerous for inspectors.Research on vibration-based damage detection began in late 1970s in aerospace structures [1][2][3]. Then in the early 1980s, the research emerged to the civil engineering structures, especially for bridges and monumental structures. Many system identification techniques and damage detection methods have been developed over the past 30 years. Some full-scale tests were conducted, but researchers had difficulties to completely validate the efficacy of the proposed methods, mainly due to the unavailability of undamaged structures as a baseline. Of considerable interest was the research on large civil engineering structures developed since 1995 at the Los Alamos National Laboratory, as it allowed researchers to compare the dynamic response of a structure before and after the introduction of different levels of damage [4,5]. Most of these studies were based on changes in the dynamic characteristics of structures, since changes in physical properties cause detectable changes in modal parameters [6,7]. Usually these techniques are based on the measurement of changes in dynamic parameters including natural frequencies, mode shapes, and damping ratios. They are based on linear analysis, and their reliability and application range are widely known [8].The main issue that must be addressed, when the frequency-domain modal identification techniques are applied for seismic damage detection, is the presence of nonlinearity in the structural response. Most of the methods developed so far are based on frequency-domain analysis, which assumes linear structural responses. In case of civil engineering structures, this assumption is difficult to accept. As suggested by some studies [9,10], a nonlinear analysis is necessary during extreme events, because most civ...

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...

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This paper proposes a reliability estimation methodology which utilizes system identification results obtained from vibration measurements. A series of earthquake and white noise excitations are imposed to a three-bent reinforced concrete bridge by three-shaking tables, simultaneously. Progressive structural damage is measured and observed, in accordance with increasing intensities of damaging events. Response measurements are obtained by accelerometers located on the deck and the columns of the bridge. Finite element models for non-updated and updated cases were obtained with and without considering acceleration measurements, respectively. Afterwards, damage detection and reliability estimation were carried out for these two cases using fragility curves. Consequently, it is shown that fragility curves of updated models significantly differ from fragility curves of non-updated models. The distinction stems from the difference between stiffness and especially damping parameters of updated and non-updated models. Such difference becomes more prominent at the extreme levels of damage.

SUMMARYDetermination of response characteristics using probabilistic approaches is essential to deal with high level of load and resistance uncertainties in civil engineering structures. Multi-span bridges present a significant problem regarding the varying nature of seismic loads on different bridge piers. With an emphasis on comparison of multi-support excitation with the conventional uniform excitation, this paper aims at providing a framework to evaluate probabilistic seismic response and estimate reliability of bridges under multi-support excitations simulated by multivariate stochastic processes. Moreover, the framework integrates the experimental data of a multi-support seismic shaking table test of a multi-span bridge structure as well as structural health monitoring (SHM) findings based on vibration measurements. The results demonstrate the importance of the multivariate stochastic processes, therefore, multi-support excitation, on estimating seismic behavior of multi-span bridges. Furthermore, this study proves the significance of structural parameter updating with respect to the evaluation of structural reliability. It is observed that the difference between the SHM-integrated and the conventional reliability estimation results vary according to the change in bedrock conditions.

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