Babak Moaveni

Uncertainty and Sensitivity Analysis of Damage Identification Results Obtained Using Finite Element Model Updating

A full-scale seven-story reinforced concrete shear wall building structure was tested on the UCSD- NEES shake table in the period October 2005-January 2006. The shake table tests were designed so as to damage the building progressively through several historical seis- mic motions reproduced on the shake table. A sensitivity- based finite element (FE) model updating method was used to identify damage in the building. The estimation uncertainty in the damage identification results was ob- served to be significant, which motivated the authors to perform, through numerical simulation, an uncertainty analysis on a set of damage identification results. This study investigates systematically the performance of FE model updating for damage identification. The dam- aged structure is simulated numerically through a change in stiffness in selected regions of a FE model of the shear wall test structure. The uncertainty of the identified damage (location and extent) due to variability of five input factors is quantified through analysis-of-variance

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.

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.

Finite-Element Model Updating for Assessment of Progressive Damage in a 3-Story Infilled RC Frame

AbstractThis paper presents a study on the identification of progressive damage, using an equivalent linear finite-element model updating strategy, in a masonry infilled RC frame that was tested on a shake table. A two-thirds-scale, 3-story, 2-bay, infilled RC frame was tested on the UCSD–NEES shake table to investigate the seismic performance of this type of construction. The shake table tests induced damage in the structure progressively through scaled historical earthquake records of increasing intensity. Between the earthquake tests and at various levels of damage, low-amplitude white-noise base excitations were applied to the infilled RC frame. In this study, the effective modal parameters of the damaged structure have been identified from the white-noise test data with the assumption that it responded in a quasi-linear manner. Modal identification has been performed using a deterministic-stochastic subspace identification method based on the measured input–output data. A sensitivity-based finite-ele...

Uncertainty Quantification in the Assessment of Progressive Damage in a 7-Story Full-Scale Building Slice

AbstractIn this paper, Bayesian linear finite-element (FE) model updating is applied for uncertainty quantification (UQ) in the vibration-based damage assessment of a 7-story RC building slice. This structure was built and tested at full scale on the University of California at San Diego-Network for Earthquake Engineering Simulation shake table: progressive damage was induced by subjecting it to a set of historical earthquake ground motion records of increasing intensity. At each damage stage, modal characteristics, such as natural frequencies and mode shapes, were identified through low-amplitude vibration testing; these data are used in the Bayesian FE model updating scheme. To analyze the results of the Bayesian scheme and gain insight into the information contained in the data, a comprehensive uncertainty and resolution analysis is proposed and applied to the 7-story building test case. The Bayesian UQ approach and subsequent resolution analysis are shown to be effective in assessing uncertainty in FE...

Design and Deployment of a Continuous Monitoring System for the Dowling Hall Footbridge

Continuous monitoring of structural vibrations is becoming increasingly common as interest in structural health monitoring (SHM) grows, as equipment becomes more affordable, and as system and damage identification methods develop. In vibration-based SHM, the dynamic modal parameters of a structure may be used as damage-sensitive features. The modal parameters are often sensitive to changes in temperature or other environmental effects, so continuous monitoring systems must also measure environmental conditions. Necessary components of a continuous structural monitoring system include a well-designed sensor array, data acquisition and logging equipment, data transfer and storage functions, and routines for extracting modal parameters from vibration measurements. All processes must be automated to handle the large volume of data generated. Such a monitoring system has been installed on the Dowling Hall Footbridge at Tufts University and is currently providing live data for research in vibration-based SHM. This paper focuses on (1) the design and installation of the system hardware and (2) the strategy used to automate the monitoring system. Successful automation of modal analysis is emphasized as the key component of this strategy. To highlight the system’s capabilities, the pattern of variation of the natural frequencies is examined and compared with environmental data.

Special Issue on Real-World Applications of Structural Identification and Health Monitoring Methodologies

Although the importance of civil infrastructure is universally recognized, the funding to maintain the U.S. system has proven insufficient over the last several decades. As has been reported recently by ASCE, an estimated

Real-time damage assessment using fiber optic grating sensors

2.2 trillion is needed to bring the country’s overall infrastructure up to good working order. While the increase in infrastructure funding that will accompany this attention is welcome, it will likely be insufficient. Owners will continue to face budget shortfalls and continue to turn to the civil engineering profession to provide them with the tools needed to inform allocation decisions for their limited resources.

Structural Identification of an 18-Story RC Building in Nepal Using Post-Earthquake Ambient Vibration and Lidar Data

Over the past few years Blue Road Research and the University of California at San Diego have been collaborating to develop a bridge health monitoring system using long gage length fiber optic strain sensors and modal analysis. Two programs supporting this effort have been funded by the National Science Foundation and from this work several papers have been published showing its strong progress1-5. In 2002, the Federal Highway Administration and Caltrans performed a full-scale test on some of the components that will be used for the planned I-5/Gilman Advanced technology Bridge in California, USA. As a part of this test Blue Road Research used its developmental system to validate the use of this damage detection technique and to compare the results with conventional modal analysis tools.