H. F. Zhou

Modal Flexibility Analysis of Cable-Stayed Ting Kau Bridge for Damage Identification

:xa0The 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.

Investigation concerning structural health monitoring of an instrumented cable-stayed bridge

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.

Generalization Capability of Neural Network Models for Temperature-Frequency Correlation Using Monitoring Data

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.

Eliminating Temperature Effect in Vibration-Based Structural Damage Detection

False-positive or false-negative damage may be signaled by vibration-based structural damage detection methods when the environmental effects on the changes of dynamic characteristics of a structure are not accounted for appropriately. In this paper, a parametric approach for eliminating the temperature effect in vibration-based structural damage detection is proposed that is applicable to structures where dynamic properties and temperature are measured. First, a correlation model between damage-sensitive modal features and temperature is formulated with the back-propagation neural network (BPNN) technique. With the correlation model, the modal features measured under different temperature conditions are normalized to an identical reference status of temperature to eliminate the temperature effect. The normalized modal features are then applied for structural damage identification. The proposed approach is examined in the instrumented Ting Kau Bridge in Hong Kong. Using the long-term monitoring data of both modal frequencies and temperatures, a BPNN correlation model with validated generalization capability is formulated, and the normalized modal frequencies before and after damage are derived and applied for the structural damage alarm using the autoassociative neural network (AANN)–based novelty detection technique. The proposed approach is competent for eliminating the temperature effect and eschewing the false-positive damage alarm that originally occurred when using the measured modal frequencies directly. Case studies assuming damage at different structural components of the bridge are carried out to verify the proposed approach and the detectability of damage using the AANN-based novelty detection technique. The results show that the approach can detect damage when the damage-induced frequency change is as small as 1%. Nevertheless, it is worth mentioning that the frequency-based approach is most effective for detecting damage of a certain severity rather than detecting the onset of damage.

Guangzhou New TV Tower: Integrated Structural Health Monitoring and Vibration Control

The Guangzhou New TV Tower (GNTVT) with a total height of 610 m is the landmark of the Guangzhou city in China and the tallest TV tower in the world. It comprises a 454 m high main tower and a 156 m high antenna mast. The main tower is a tube-in-tube structure consisting of a steel lattice outer structure and a reinforced concrete inner structure. The antenna mast is a steel structure founded on the top of the main tower. To ensure the safety during construction and the operational performance during typhoons and earthquakes of this challenging structure, a sophisticated long-term structural health monitoring system consisting of about 800 sensors has been implemented for on-line monitoring at both construction and service stages. In the meanwhile, a hybrid mass damper control system is installed on the main tower and two tuned mass dampers are suspended on the antenna mast for suppressing wind-induced vibration of GNTVT. This paper outlines the structural health monitoring system and the vibration control system for GNTVT and their integration.

Performance of neural networks for simulation and prediction of temperature-induced modal variability

Vibration-based damage detection methods use changes in modal parameters to diagnose structural degradation or damage. Structures in reality are subject to varying environmental effects which also cause changes in modal parameters. The well-defined nature of the environmental effects on modal properties is essential for reliable damage diagnosis based on vibration measurement. In this paper, the performance of artificial neural networks (ANNs) for simulation and prediction of temperature-caused variability of modal frequencies is investigated. Making use of one-year measurement data of modal frequencies and temperatures from an instrumented cable-stayed bridge, three- layer back-propagation (BP) neural networks are configured to model the correlation between the temperatures and frequencies. Two approaches are adopted in defining the training samples to train the neural networks and the testing samples to verify the prediction capability of the neural networks. It is shown that when using appropriate training data covering a wide range of temperature variations, the trained neural networks exhibit satisfactory performance in both reproduction (simulation) and prediction (generalization). A good mapping between the temperatures and frequencies is obtained by the neural network models for all measured modes.

Instrumentation for durability monitoring of a long-span cable-stayed bridge

This paper outlines the design of an instrumentation system for durability monitoring of the worlds longest cable-stayed bridge: the Sutong Bridge with a central span of 1088 m. As part of the Structural Health Monitoring And Safety Evaluation System (SHMASES) for the Sutong Bridge, the durability monitoring system is designed to monitor the corrosion in reinforced concrete structures. The sensors for durability monitoring include two categories. The first category refers to the sensors to monitor the causes leading to corrosion, such as temperature and relative humidity. The second category is electrode assemblies which are used to monitor the end results of corrosion. Data from the sensory system are then periodically collected using a portable or remotely computerized data acquisition system. The collected data from this system will provide useful information on maintenance and repair of concrete structures, and are envisaged to be incorporated into the reliability-based safety evaluation system developed for the Sutong Bridge

Field investigation of a vibration monitoring wireless sensor network on a huge cantilever structure

To advance wireless structural monitoring systems mature into a reliable substitute to wired structural monitoring systems, efforts should be paid to investigate their in-field performance on real civil structures, especially complex mega structures. This study carries out an investigation into a vibration monitoring wireless sensor network (WSN) for modal identification of a huge cantilever structure. The testbed under study is the New Headquarters of Shenzhen Stock Exchange (NHSSE). One outstanding feature of NHSSE is its huge floating platform, which is a steel truss structure with an overall plan dimension of 98x162 m and a total height of 24 m. It overhangs from the main tower 36 m along the long axis and 22 m along the short axis at a height of 36 m above the ground, making it the largest cantilever structure in the world. Recognizing the uniqueness of this floating platform, the performance of the WSN for ambient vibration measurement of this structure is examined. A preliminary two-point simultaneous acceleration measurement using the WSN is reported in this paper. The preliminary study demonstrates that the WSN is capable of measuring the ambient vibration and identifying the modal properties of a huge cantilever structure.

Optimal Sensor Layout for Bridges Subject to Ship Collision

Structural health monitoring (SHM) systems provide an effective means to monitor bridge response during ship collisions and evaluate the structural damage. However, reliable damage evaluation using the monitoring data from a SHM system depends largely on the sensor layout. In this paper, a sensor layout optimization (SLO) method targeting the post-collision damage evaluation of bridges is proposed. The sensor layout is optimized by a multi-objective optimization algorithm which simultaneously minimizes the information entropy for each ship collision scenario. The cable stayed Ting Kau Bridge is used to testify the feasibility and effectiveness of the proposed method.