A health monitoring system was designed and installed on Dafosi Bridge, the largest cable-stayed bridge across the Yangtze River in western China. This article briefly describes an online deflection monitoring system, the most important sensing subsystem of the health monitoring system. It can be divided into two major components, one for measurement, and one for control and data processing. The measurement system itself includes two types of deflection sensors to monitor the lateral displacement of the two towers and the flexural and torsional displacement of the main girder. Sensor outputs are pre-processed locally and sent to the host computer at the management center via the Internet. The system design and implementation are reviewed, and the results of data from two kinds of sensors are presented.

No abstract

This paper briefly describes a health monitoring system designed for use on the Dafosi Bridge, the largest cablestayed bridge across the Yangtze River in western China. The system can be divided into two major components, one for measurement, and one for control and data processing. The measurement system itself includes four sensing subsystems relating to: 1) fiber optic strain sensing, 2) displacement sensing, 3) temperature sensing, and 4) dynamic measurements. The control and data processing system consists of three subsystems: 1) a local computer, 2) a communication subsystem, and 3) a host computer. Sensor outputs are pre-processed locally and sent to the host computer at the management center via the Internet. The system design and implementation are reviewed, and the results of data from two sensing subsystems are presented.

No abstract

As the damage diagnosis of bridge structure is highly nonlinear in nature, it is difficult to develop a comprehensive model taking into account all of the independent variables, such as the structural and environmental properties, using conventional modeling techniques. In this study, a method was introduced for damage diagnosis of bridge structure by integration of BP artificial neural network (ANN) and information fusion based on D-S evidential theory. The basic probability assignment functions for data fusion were constructed according to the demand of the damage diagnosis and the real conditions of the bridge structure. And an application example of the proposed method was demonstrated. The results showed that the integration of the two strategy can remove the shortcoming of BP ANN with remaining of its advantages and promote the identified veracity of the whole diagnosis system.