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State-space (SS) model is proposed to identify the time lag between the asynchronous accelerations at different locations of the Jiangyin Bridge measured during a ship-bridge collision. One of the accelerations is chosen as the reference signal, and the time axis of the rest of them are shifted relative to that of the reference signal with a series of shifting times. For each pair of reference and time shifted signals, SS models in correspondence to the shifting time series are formulated. Their system matrices are identified with data-driven stochastic subspace identification algorithm, and their model order is determined by Akaike's information theoretic criterion and final prediction error. If the 2 accelerations for model fitting are asynchronous, errors may be introduced into the SS model and its prediction error is expected to be greater than the counterpart obtained with synchronous accelerations. Therefore, the actual time lag between them is identified from the shifting time that corresponds to the minimum of loss function, which is used to map the prediction error vector sequence to a real number. In addition, asynchronous acceleration data measured 2 hr ahead of the ship-bridge collision and synchronous acceleration data measured long after the ship-bridge collision are also analyzed. The former dataset is exploited to evaluate the reproducibility of the SS model for time synchronization, and the latter dataset is utilized to examine its anti-false-identification capability. The results show that the SS model achieves a satisfactory performance in the identification of time lag for both asynchronous and synchronous measurement data. [ABSTRACT FROM AUTHOR]

The Stonecutters Bridge (SCB) in Hong Kong is the third-longest cable-stayed bridge in the world with a main span stretching 1,018 m between two 298 m high single-leg tapering composite towers. A Wind and Structural Health Monitoring System (WASHMS) is being implemented on SCB by the Highways Department of The Hong Kong SAR Government, and the SCB-WASHMS is composed of more than 1,300 sensors in 15 types. In order to establish a linkage between structural health monitoring and maintenance management, a Structural Health Rating System (SHRS) with relevant rating tools and indices is devised. On the basis of a 3D space frame finite element model (FEM) of SCB and model updating, this paper presents the development of an SHR-oriented 3D multi-scale FEM for tire purpose of load-resistance analysis and damage evaluation in structural element level, including modeling, refinement and validation of the multi-scale FEM. The refined 3D structural segments at deck and towers are established in critical segment positions corresponding to maximum cable forces. The components in the critical segment region are modeled as a full 3D FEM and fitted into the 3D space frame FEM. The boundary conditions between beam and shell elements are perfonned conforming to equivalent stiffness, effective mass and compatibility of deformation. The 3D multi-scale FEM is verified by the in-situ measured dynamic characteristics and static response. A good agreement between the FEM and measurement results indicates that the 3D multi-scale FEM is precise and efficient for WASHMS and SHRS of SCB. In addition, stress distribution and concentration of the critical segments in the 3D multi-scale FEM under temperature loads, static wind loads and equivalent seismic loads are investigated. Stress concentration elements under equivalent seismic loads exist in the anchor zone in steel/concrete beam and the anchor plate edge in steel anchor box of the towers. [ABSTRACT FROM AUTHOR]

In Hong Kong, a sophisticated long-term structural health monitoring system has been devised by the Highways Department of HKSAR Government to monitor the structural performance and health conditions of three cable-supported bridges. On-structure instrumentation systems for two new long-span bridges are also being implemented. The implementation of these monitoring systems highlights the necessity for developing a monitoring-based structural health evaluation paradigm for long-span bridges. This paper describes the research directed towards this that has been conducted in the Hong Kong Polytechnic University. Taking the instrumented cable-stayed Ting Kau Bridge as a paradigm, the research covers the development of an index system and a database system for monitoring data management, the modelling of the environmental variability of measured modal properties with the intention of eliminating environmental effects in vibration-based damage detection, and the feasibility of using measured modal properties from the deployed vibration sensors for structural damage identification. [ABSTRACT FROM AUTHOR]

The parametric approach to eliminating the temperature-caused modal variability in vibration-based structural damage detection requires a correlation model between the modal properties and environmental temperatures. This paper examines the generalization capability of neural network models, established using long-term monitoring data, for correlation between the modal frequencies and environmental temperatures. A total of 770 h modal frequency and temperature data obtained from an instrumented bridge are available for this study, which are further divided into three sets: training data, validation data, and testing data. A two-layer back-propagation neural network (BPNN) is first trained using the training data by the conventional training algorithm, in which the number of hidden nodes is optimally determined using the validation data. Then two new BPNNs are configured with the same data by applying the early stopping technique and the Bayesian regularization technique, respectively. The reproduction and prediction capabilities of the two new BPNNs are examined in respect of the training data and the unseen testing data, and compared with the performance of the baseline BPNN model. This study indicates that both the early stopping and Bayesian regularization techniques can significantly ameliorate the generalization capability of BPNN-based correlation models, and the BPNN model formulated using the early stopping technique outperforms that using the Bayesian regularization technique in both reproduction and prediction capabilities. [ABSTRACT FROM AUTHOR]

A new static-based method for damage detection of cable-stayed bridges using the changes in cable forces is presented in this paper. In recognizing the fact that damage incurring in bridge girders will cause a redistribution of forces in stay cables, the proposed method uses the measured changes in cable forces to detect the damage in bridge girders. In this method, the damage identification is formulated as an optimization problem in which the cable force error between measurement results and analytical model predictions is minimized. The sensitivities of cable forces with respect to the structural parameters of bridge girders for the linear and geometrically nonlinear bridge structures are obtained by using the direct differentiation method and the finite difference method, respectively, and are used to solve the optimization problem. Regularization technique is used to alleviate ill-conditioning in the solution course of the optimization problem. The validity of the method is illustrated by numerical studies of damage detection of the cable-stayed Sutong Bridge with a main span of 1,088 m. The effect of measurement noises and the number of measured cable forces on the accuracy of damage quantification is investigated. The proposed method correctly identifies the damage locations and damage magnitudes in bridge girders when using noise-free cable forces. It is also shown that structural damage can be quantified with acceptable accuracy in the case of low-level measurement noises and the damaged members being well spaced, and the identification accuracy is greatly improved when the forces in the cables connected to the damaged members are available for damage quantification. [ABSTRACT FROM AUTHOR]

The cable-stayed Ting Kau Bridge has been permanently instrumented with more than 230 sensors for long-term structural health monitoring. In this article, the feasibility of using the measured dynamic characteristics of the bridge for damage detection is studied. Making use of a validated three-dimensional (3D) finite element model (FEM), modal flexibility matrices of the bridge are constructed using a few truncated modes and incomplete modal vectors at the sensor locations. The relative flexibility change (RFC) between intact and damage states is then formulated as an index to locate damage. The applicability of this flexibility index for damage location in the cable-stayed bridge is examined by investigating various damage scenarios including those at stay cables, longitudinal stabilizing cables, bearings and supports, longitudinal girders and cross girders, and taking into account measurement noise in modal data. The influence of two ambient factors, that is, temperature change and traffic loading, on the damage detectability is also analyzed by approximately considering an equivalent alteration in the material and structural behaviors. It is revealed that in the absence of ambient effects the RFC index performs well for locating damage of different severities in single-damage cases. In multi-damage cases the RFC index may provide false-negative identification for damage at the members with low sensitivity. Eliminating ambient effects is requisite for reliable detection of damage at stay cables and cross girders. The capability of the RFC index for locating damage at cross girders is significantly dropped in the presence of measurement noise. [ABSTRACT FROM AUTHOR]

A procedure for assessment of bridge expansion joints making use of long-term monitoring data is presented in this paper. Based on the measurement data of expansion joint displacement and bridge temperature, the normal correlation pattern between the effective temperature and thermal movement is first established. Alarms will be raised if a future pattern deviates from this normal pattern. With the established correlation pattern, the expansion joint displacements under the design maximum and minimum temperatures are predicted and compared with the design allowable values for validation. The extreme temperatures for a certain return period are also derived using the measurement data for design verification. Then the annual or daily-average cumulative movements experienced by expansion joints are estimated from the monitoring data for comparison with the expected values in design. Because the service life and interval for replacement of expansion joints rely to a great extent on the cumulative displacements, an accurate prediction of the cumulative displacements will provide a robust basis for determining a reasonable interval for inspection or replacement of expansion joints. The proposed procedure is applied to the assessment of expansion joints in the cable-stayed Ting Kau Bridge with the use of one-year monitoring data. [ABSTRACT FROM AUTHOR]

This paper presents a novel finite element (FE) model for analyzing coupled flutter of long-span bridges using the commercial FE package ANSYS. This model utilizes a specific user-defined element Matrix27 in ANSYS to model the aeroelastic forces acting on the bridge, wherein the stiffness and damping matrices are expressed in terms of the reduced wind velocity and flutter derivatives. Making use of this FE model, damped complex eigenvalue analysis is carried out to determine the complex eigenvalues, of which the real part is the logarithm decay rate and the imaginary part is the damped vibration frequency. The condition for onset of flutter instability becomes that, at a certain wind velocity, the structural system incorporating fictitious Matrix27 elements has a complex eigenvalue with zero or near-zero real part, with the imaginary part of this eigenvalue being the flutter frequency. Case studies are provided to validate the developed procedure as well as to demonstrate the flutter analysis of cable-supported bridges using ANSYS. The proposed method enables the bridge designers and engineering practitioners to analyze flutter instability by using the commercial FE package ANSYS. [ABSTRACT FROM AUTHOR]