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This paper presents a unique structural reliability estimation method incorporating structural parameter identification results based on the seismic response measurement. In the shaking table test, a three-bent concrete bridge model was shaken to different damage levels by a sequence of earthquake motions with increasing intensities. Structural parameters, stiffness and damping values of the bridge were identified under damaging seismic events based on the seismic response measurement. A methodology was developed to understand the importance of structural parameter identification in the reliability estimation. Along this line, a set of structural parameters were generated based on the Monte Carlo simulation. Each of them was assigned to the base bridge model. Then, every bridge model was analyzed using nonlinear time history analyses to obtain damage level at the specific locations. Last, reliability estimation was performed for bridges modeled with two sets of structural parameters. The first one was obtained by the nonlinear time history analysis with the Monte Carlo simulated parameters which is called nonupdated structural parameters. The second one was obtained by updating the first set in Bayesian sense based on the vibration-based identification results which is called updated structural parameters. In the scope of this paper, it was shown that residual reliability of the system estimated using the updated structural parameters is lower than the one estimated using the nonupdated structural parameters. CE Database subject headings: Shake table tests; Vibration; Identification; Structural reliability; Parameters; Bayesian analysis; Monte Carlo method; Kalman filters. Author keywords: Shaking table test; Vibration-based; Identification; Reliability; Bayesian; Monte Carlo; Extended Kalman filter. DOI: 10.1061/(ASCE)EM.1943-7889.0000066

A stochastic model of traffic excitation on bridges is developed assuming that the arrival of vehicles traversing a bridge (modeled as an elastic beam) follows a Poisson process, and that the contact force of a vehicle on the bridge deck can be converted to equivalent dynamic loads at the nodes of the beam elements. The parameters in this model, such as the Poisson arrival rate and the stochastic distribution of vehicle speeds, are obtained by image processing of traffic video data. The model reveals that traffic excitations on bridges are spatially correlated. This important characteristic is usually incorrectly ignored in most output-only methods for the identification of bridge structural properties using traffic-induced vibration measurement data. In this study, the stochastic traffic excitation model with partial traffic information is incorporated in a Bayesian framework, to evaluate the structural properties and update their uncertainty for condition assessment of the bridge superstructure. The vehicle weights are also estimated simultaneously in this procedure. The proposed structural assessment methodology is validated on an instrumented highway bridge. DOI: 10.106l/(ASCE)0733-9399(2009) 135:8(747) CE Database subject headings: Monitoring; Assessment; Bridges, highway; Vibration; Excitation; Vehicles; Stochastic models; Bayesian analysis.

A new vision of structural health monitoring (SHM) is presented, in which the ultimate goal of SHM is not limited to damage identification, but to describe the structure by a probabilistic model, whose parameters and uncertainty are periodically updated using measured data in a recursive Bayesian filtering (RBF) approach. Such a model of a structure is essential in evaluating its current condition and predicting its future performance in a probabilistic context. RBF is conventionally implemented by the extended Kalman filter, which suffers from its intrinsic drawbacks. Recent progress on high-fidelity propagation of a probability distribution through nonlinear functions has revived RBF as a promising tool for SHM. The central difference filter, as an example of the new versions of RBF, is implemented in this study, with the adaptation of a convergence and consistency improvement technique. Two numerical examples are presented to demonstrate the superior capacity of RBF for a SHM purpose. The proposed method is also validated by large-scale shake table tests on a reinforced concrete two-span three-bent bridge specimen. CE Database subject headings: Bayesian analysis; Filters; Monitoring; Assessments; Vibration; Identification; Structural analysis.

Urban water delivery systems can be damaged by earthquakes or severely cold weather. In either case, the damage cannot easily be detected and located, especially immediately after the event. In recent years, real-time damage estimation and diagnosis of buried pipelines attracted much attention of researchers focusing on establishing the relationship between damage ratio (breaks per unit length of pipe) and ground motion, taking the soil condition into consideration. Due to the uncertainty and complexity of the parameters that affect the pipe damage mechanism, it is not easy to estimate the degree of physical damage only with a few numbers of parameters. As an alternative, this paper develops a methodology to detect and locate the damage in a water delivery system by monitoring water pressure on-line at some selected positions in the water delivery systems. For the purpose of on-line monitoring, emerging supervisory control and data acquisition technology can be well used. A neural network-based inverse analysis method is constructed for detecting the extent and location of damage based on the variation of water pressure. The neural network is trained by using analytically simulated data from the water delivery system with one location of damage, and validated by using a set of data that have never been used in the training. It is found that the method provides a quick, effective, and practical way in which the damage sustained by a water delivery system can be detected and located. CE Database subject headings: Damage; Water distribution: Water pipelines: Buried pipes: Urban areas: Data collection.

This paper presents a technology for detecting invisible damage inside concrete, which is based on reconstruction of dielectric profile (image) of the concrete illuminated with microwaves sent from and received by antenna arrays controlled by specialized software. The imaging system developed in this study consists of an 8x8 transmitting and an 8x8 receiving arrays, an innovative numerical bifocusing operator for improving image resolution, and imaging software for reconstructing a two-dimensional image from the scattered field. The effectiveness of the developed technology in detecting steel and voids inside concrete has been demonstrated through numerical simulation and experiments. CE Database subject headings: Microwaves; Imaging techniques; Antennas; Concrete structures; Damage assessment; Nondestructive tests.

A baseline model is essential for long-term structural performance monitoring and evaluation. This study represents the first effort in applying a neural network-based system identification technique to establish and update a baseline finite element model of an instrumented highway bridge based on the measurement of its traffic-induced vibrations. The neural network approach is particularly effective in dealing with measurement of a large-scale structure by a limited number of sensors. In this study, sensor systems were installed on two highway bridges and extensive vibration data were collected, based on which modal parameters including natural frequencies and mode shapes of the bridges were extracted using the frequency domain decomposition method as well as the conventional peak picking method. Then an innovative neural network is designed with the input being the modal parameters and the output being the structural parameters of a three-dimensional finite element model of the bridge such as the mass and stiffness elements. After extensively training and testing through finite element analysis, the neural network became capable to identify, with a high level of accuracy, the structural parameter values based on the measured modal parameters, and thus the finite element model of the bridge was successfully updated to a baseline. The neural network developed in this study can be used for future baseline updates as the bridge being monitored periodically over its lifetime. CE Database subject headings: Instrumentation; Vibration measurement; Neural networks; Bridges; Performance evaluation; Monitoring; Signal processing.

Jacketing technology using fiber reinforced polymer (FRP) composites is being applied for seismic retrofit and rehabilitation of reinforced concrete (RC) columns designed and constructed under older specifications. In this study, the authors develop an electromagnetic (EM) imaging technology for detecting such damage as voids and debonding between the jacket and the column, which may significantly weaken the structural performance of the column otherwise attainable by jacketing. This technology is based on the reflection analysis of a continuous EM wave sent toward and reflected from layered FRP--adhesive-concrete medium: Voids and debonding areas will generate air gaps which produce additional reflections of the EM wave. In this study, dielectric properties of various materials involved in the FRP-jacketed RC column were first measured using a plane-wave reflectometer. The measured properties were then used for a computer simulation of the proposed EM imaging technology. The simulation demonstrated the difficulty in detecting damage by using plane waves, as the reflection contribution from the voids and debonding is very small compared to that from the jacketed column. In order to alleviate this difficulty, dielectric lenses were designed and fabricated, focusing the EM wave on the bonding interface. Finally, three concrete columns were constructed and wrapped with glass-FRP jackets with various voids and debonding conditions artificially introduced in the bonding interface. Using the proposed EM imaging technology involving the especially designed and properly installed lenses, these voids and debonding areas were successfully detected. This technology can be used to assess the jacket bonding quality during the initial jacket installation stage and to detect debonding between the column and the jacket caused by earthquake and other destructive loads. DOI: 10.1061/(ASCE)0733-9399(2002)128:2(172) CE Database keywords: Microwaves; Damage; Concrete structures; Fiber reinforced materials; Polymer; Imaging techniques.

This study examines the fragility curves of a bridge by two different analytical approaches; one utilizes the time-history analysis and the other uses the capacity spectrum method. The latter approach is one of the simplified nonlinear static procedures recently developed for buildings. In this respect, a sample of 10 nominally identical but statistically different bridges and 80 ground-motion time histories are considered to account for the uncertainties related to the structural capacity and ground motion, respectively. The comparison of fragility curves by the nonlinear static procedure with those by time-history analysis indicates that the agreement is excellent for the state of at least minor damage, but not as good for the state of major damage where nonlinear effects clearly play a crucial role. Overall, however, the agreement is adequate even in the state of major damage considering the large number of typical assumptions under which the analyses of fragility characteristics are performed.

Advanced composite materials are being used for seismic retrofit of reinforced concrete (RC) columns designed and constructed by old specifications. The writers are currently developing an electromagnetic (EM) imaging technology for assessing damage of the columns covered by such jackets, specifically debonding at the adhesive bonding layer between the jacket and the column. This technology takes advantage of the difference in electrical properties (including conductivity and dielectric constant) between the adhesive epoxy (under a good bonding condition) and air (in case of debonding). In this paper, an inversion method is presented that can efficiently and accurately reconstruct electrical property images (profiles) from continuous sinusoidal EM waves of different frequencies launched toward and reflected from the layered medium, such as the composite-adhesive-concrete medium. This inversion method consists of a forward model and an iterative optimization process. By comparing the wave signals sent to and reflected from the medium, the reflection coefficient of each layer of the medium can be measured and the electrical property profile (EM image) can be inverted. Using the inverted electrical property, the forward model computes the reflection coefficient. The Jacobian matrix method is applied to modify the inverted profile interactively to minimize the error between the measured and calculated reflection coefficient. A constrained optimization technique is introduced in order to achieve maximum stability of the inverted profile against noise. At the present stage, simulation study is carried out in which the electric property image for the bonding layer is satisfactorily reconstructed and debonding successfully detected using the proposed inversion method.

This paper introduces an optically powered electrical accelerometer recently developed by the author for measuring dynamic response of civil structures and describes a field test using its prototypes installed on a 310-m tall concrete TV tower, together with the results of system identification analysis based on the field measurement. This sensor employs an electric circuit, LC oscillator, in the sensor head. The resonant frequency of the LC oscillator is modulated by the movement of a pendulum installed inside the sensor head, indicating the acceleration of the pendulum support. The oscillatory electric voltage is transformed to an optical signal and transmitted through an optical fiber. The LC circuit is optically powered as well. Therefore, there are no electrical cables involved in this sensor, which brings many advantages to the sensor, such as immunity to an electromagnetic field and significantly less troublesome cabling problems. Prototype accelerometers were fabricated and installed on the TV tower to measure its ambient vibration. based on the measurements, dynamic characteristics of this tower, including the natural frequencies, damping ratios, and mode shapes, were successfully identified. They are essential for the assessment of the dynamic response of the tower under wind and earthquake loads. This field test has not only provided valuable information of the tower's dynamic characteristics, but also demonstrated the excellent performance and great potential of this optically powered sensor for civil structure applications.

A stochastic model of traffic excitation on bridges is developed assuming that the arrival of vehicles traversing a bridge (modeled as an elastic beam) follows a Poisson process, and that the contact force of a vehicle on the bridge deck can be converted to equivalent dynamic loads at the nodes of the beam elements. The parameters in this model, such as the Poisson arrival rate and the stochastic distribution of vehicle speeds, are obtained by image processing of traffic video data. The model reveals that traffic excitations on bridges are spatially correlated. This important characteristic is usually incorrectly ignored in most output-only methods for the identification of bridge structural properties using traffic-induced vibration measurement data. In this study, the stochastic traffic excitation model with partial traffic information is incorporated in a Bayesian framework, to evaluate the structural properties and update their uncertainty for condition assessment of the bridge superstructure. The vehicle weights are also estimated simultaneously in this procedure. The proposed structural assessment methodology is validated on an instrumented highway bridge. [ABSTRACT FROM AUTHOR]

This paper presents a technology for detecting invisible damage inside concrete, which is based on reconstruction of dielectric profile (image) of the concrete illuminated with microwaves sent from and received by antenna arrays controlled by specialized software. The imaging system developed in this study consists of an 8×8 transmitting and an 8×8 receiving arrays, an innovative numerical bifocusing operator for improving image resolution, and imaging software for reconstructing a two-dimensional image from the scattered field. The effectiveness of the developed technology in detecting steel and voids inside concrete has been demonstrated through numerical simulation and experiments. [ABSTRACT FROM AUTHOR]