Dr. Chenhao Jin (金辰昊)学术报告通知(2018---76)


发布时间: 2018-11-28     浏览次数: 11

题  目:Damage Detection for Civil Structures using Monitoring Data

时  间:20181130日上午11:00-12:00

地  点:乐学楼832

报告人:Dr. Chenhao Jin (金辰昊), Lecturer, Institute of Construction Engineering Management, Hohai University

 

摘  要:

        Detecting structural damage in civil engineering structures has become an increasingly viable option for efficient maintenance and management of infrastructures. Current research assumes constant structural parameters and uses static statistical process control for damage detection. However, structural parameters are typically slow-changing due to variations such as environmental and operational effects. Hence, false alarms may easily be triggered when the data points falling outside of the static statistical process control range due to the environmental and operational effects. This talk introduces several novel methods to overcome this kind of problems for civil structures, especially buildings and bridges. For building structures, extended Kalman filter and dynamic statistical process control are integrated to detect structural damage in real time. Based on historical measurements of damage-sensitive parameters in the state-space model, extended Kalman filter is used to provide real-time estimations of these parameters as well as standard derivations in each time step, which are then used to update the control limits for dynamic statistical process control to detect any abnormality in the selected parameters. The simulation results demonstrate high detection accuracy rate and light computational costs of the developed extended Kalman filter–dynamic statistical process control damage detection method and the potential for implementation in structural health monitoring systems for in-service civil structures.

        For bridge structures, an extended Kalman filter-based artificial neural network (EKFNN) method is developed to eliminate the temperature effects and detect damage for structures equipped with long-term monitoring systems. Based on the vibration acceleration and temperature data obtained from an in-service highway bridge located in Connecticut, United States, the correlations between natural frequencies and temperature are analyzed to select proper input variables for the neural network model. Weights of the neural network are estimated by extended Kalman filter, which is also used to derive the confidence intervals of the natural frequencies to detect the damage. Numerical testing results show that the temperature induced changes in natural frequencies have been considered prior to the establishment of the threshold in the damage warning system, and the simulated damages have been successfully captured. The advantages of EKFNN method are presented through comparing with benchmark multiple linear regressions method, showing the potential of this method for structural health monitoring of highway bridge structures.