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...