Xianfei He

System Identification Study of a 7-Story Full-Scale Building Slice Tested on the UCSD-NEES Shake Table

A full-scale 7-story reinforced concrete building slice was tested on the unidirectional University of California-San Diego Net- work for Earthquake Engineering Simulation (UCSD-NEES) shake table during the period from October 2005 to January 2006. A rectangular wall acted as the main lateral force resisting system of the building slice. The shake table tests were designed to damage the building pro- gressively through four historical earthquake records. The objective of the seismic tests was to validate a new displacement-based design methodology for reinforced concrete shear wall building structures. At several levels of damage, ambient vibration tests and low-amplitude white noise base excitation tests were applied to the building, which responded as a quasi-linear system with dynamic parameters evolving as a function of structural damage. Six different state-of-the-art system identification algorithms, including three output-only and three input- output methods were used to estimate the modal parameters (natural frequencies, damping ratios, and mode shapes) at different damage levels based on the response of the building to ambient as well as white noise base excitations, measured using DC-coupled accelerometers. The modal parameters estimated at various damage levels using different system identification methods are compared to (1) validate/ cross-check the modal identification results and study the performance of each of these system identification methods, and to (2) investigate the sensitivity of the identified modal parameters to actual structural damage. For a given damage level, the modal parameters identified using different methods are found to be in good agreement, indicating that these estimated modal parameters are likely to be close to the actual modal parameters of the building specimen. DOI: 10.1061/(ASCE)ST.1943-541X.0000300.

Sensor Network for Structural Health Monitoring of a Highway Bridge

A bridge monitoring TestBed is developed as a research environment for sensor networks and related decision-support technologies. A continuous monitoring system, capable of handling a large number of sensor data channels and three video signals, is deployed on a four-span, 90-m long, reinforced concrete highway bridge. Of interest is the integration of the image and sensor data acquisition into a single computer, thereby providing accurate time synchronization between the response and corresponding traffic loads. Currently, video and acceleration records corresponding to traffic induced vibration are being recorded. All systems operate online via a high-speed wireless Internet network, allowing real-time data transmission. Elements of the above health monitoring framework are presented herein. Integration of these elements into an automated functional system is emphasized. The recorded data are currently being employed for structural system identification via a model-free technique. Effort is also underway to correlate the moving traffic loads with the recorded accelerations. Finally, the TestBed is available as a resource for verification of new sensor technologies, data acquisition/ transmission algorithms, data mining strategies, and for decision-support applications.

Damage Identification of a Composite Beam Using Finite Element Model Updating

The damage identification study presented in this paper leveraged a full-scale sub-component experiment conducted in the Charles Lee Powell Structural Research Laboratories at the University of California, San Diego. As payload project attached to a quasi-static test of a full-scale composite beam, a high-quality set of low-amplitude vibration response data was acquired from the beam at various damage levels. The Eigensystem Realization Algorithm was applied to identify the modal parameters (natural frequencies, damping ratios, displacement and macro-strain mode shapes) of the composite beam based on its impulse responses recorded in its undamaged and various damaged states using accelerometers and long-gage fiber Bragg grating strain sensors. These identified modal parameters are then used to identify the damage in the beam through a finite element model updating procedure. The identified damage is consistent with the observed damage in the composite beam.

Modal Identification Study of Vincent Thomas Bridge Using Simulated Wind-Induced Ambient Vibration Data

In this paper, wind-induced vibration response of Vincent Thomas Bridge, a suspension bridge located in San Pedro near Los Angeles, California, is simulated using a detailed three-dimensional finite element model of the bridge and a state-of-the-art stochastic wind excitation model. Based on the simulated wind-induced vibration data, the modal parameters (natural frequencies, damping ratios, and mode shapes) of the bridge are identified using the data-driven stochastic subspace identification method. The identified modal parameters are verified by the computed eigenproperties of the bridge model. Finally, effects of measurement noise on the system identification results are studied by adding zero-mean Gaussian white noise processes to the simulated response data. Statistical properties of the identified modal parameters are investigated under increasing level of measurement noise. The framework presented in this paper will allow to investigate the effects of various realistic damage scenarios in long-span cable-supported (suspension and cable-stayed) bridges on changes in modal identification results. Such studies are required in order to develop robust and reliable vibration-based structural health monitoring methods for this type of bridges, which is a long-term research objective of the authors.