Search

In contrast to the traditional uncertainty quantification of the modal parameters identified from the output‐only measurement data, this study develops an uncertainty propagation and quantification method for structural flexibility identified from input–output measurement data and then uses the derived flexibility matrix for structural reliability analysis. First, a novel procedure for variance estimations of the complete system matrix of the state‐space model is derived based on first‐order perturbation theory. Then, the mode scaling factors and integrated modal flexibility are subsequently derived and quantified. Finally, the predicted displacements and their uncertainty intervals are used to implement the reliability analysis in terms of the structural displacement serviceability limit state (SLS). The accuracies of the derived expressions are verified by a numerical example through the comparison of variance values estimated from Monte Carlo simulations. The simulation example also indicates that we can expect to identify structural parameters with higher reliability if the input force to the structure is measured, and the identified modal scaling and corresponding flexibility matrix undergo more uncertainty than those of the modal parameters. Finally, we apply the proposed uncertainty quantification method to a scaled three‐story frame structure to demonstrate its practicability. The proposed method bridges a gap between the identified structural flexibility and structural reliability analysis directly by the probability category. [ABSTRACT FROM AUTHOR]

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.

Methods that identify structural component stiffness degradation by pre- and postevent low amplitude vibration measurements, based on a linear time-invariant (LTI) system model, are conceptually justified by examining the hysteresis loops the structural components experience in such vibrations. Two large-scale shake table experiments, one on a two-column reinforced concrete (RC) bridge bent specimen, and the other on a two-span three-bent RC bridge specimen were performed, in which specimens were subjected to earthquake ground motions with increasing amplitude and progressively damaged. In each of the damaged stages between two strong motions, low amplitude vibrations of the specimens were aroused, and the postevent component stiffness coefficients were identified by optimizing the parameters in a LTI model. The stiffness degradation identified is consistent with the experimental hysteresis, and could be quantitatively related to the capacity residual of the components. CE Database subject headings: Shake table tests; Verification; Bridges, concrete; Concrete, reinforced; Assessment; Vibration; Stiffness.

This paper presents a comprehensive study involving triaxial earthquake simulator testing on seismic isolation of electric power transformers. In this study, two isolation systems were developed: one using sliding bearings combined with rubber bearings and the other segmented high-damping rubber bearings. Triaxial earthquake simulator testing was performed using a large-scale transformer model equipped with a real bushing. The effectiveness of the base isolation system in reducing the response acceleration of power transformer systems was demonstrated. Important observations were made on the seismic responses of the transformer and bushing. In particular, the vertical component of the ground motion induced a high-frequency response of the bushing when the transformer was isolated with the sliding isolation system. This is because the vertical motion changes the friction forces in the sliding bearings that excite high modes in the transformer-bushing system. Furthermore, the effect of the interaction with the bushing connecting cables on the response of the bushing in the base-isolated system was experimentally evaluated. In conclusion, the base isolation technology, when properly designed, is a highly effective measure for seismic protection of power transformers. Index Terms--Base isolation, bushing, earthquake simulator, electric power transformer, seismic, substations.

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.

The uniaxial compressive strength of concrete is the most widely used criterion in producing concrete. Although testing of the uniaxial compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. At this point, it is too late to make improvements if the test result does not satisfy the required strength. Therefore, the strength estimation before the placement of concrete is highly desirable. This study presents the first effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions. Back-propagation neural networks were developed, trained, and tested using actual data sets of concrete mix proportions provided by two ready-mixed concrete companies. The compressive strengths estimated by the neural networks were verified by laboratory testing results. This study demonstrated that the neural network techniques are effective in estimating the compressive strength of concrete based on the mix proportions. Application of these techniques will contribute significantly to the concrete quality assurance. CE Database subject headings: Neural networks; Compressive strength; Estimation; Concrete; Mixing.