Please login first
Babak Moaveni     Other 
Timeline See timeline
Babak Moaveni published an article in September 2018.
Research Keywords & Expertise See all
0 A
0 Continuous Monitoring
0 Modal Analysis
0 Physical Properties
0 Structure
0 System Identification
Top co-authors See all
Geert Lombaert

111 shared publications

KU Leuven, Department of Civil Engineering, Structural Mechanics Section, Kasteelpark Arenberg 40, 3001 Leuven, Belgium

Joel P. Conte

57 shared publications

Professor, Dept. of Structural Engineering, Univ. of California San Diego, 9500 Gilman Dr., La Jolla, CA 92093-0085 (corresponding author)

Eleni N. Chatzi

56 shared publications

Institute of Structural Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zürich, Switzerland

Sami F. Masri

44 shared publications

Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089-2531

Ahmed Elgamal

33 shared publications

Univ. of California, San Diego, Dept. of Structural Engineering, La Jolla, CA 92093-0085

27
Publications
32
Reads
0
Downloads
95
Citations
Publication Record
Distribution of Articles published per year 
(2009 - 2018)
Total number of journals
published in
 
13
 
Publications See all
Article 0 Reads 0 Citations Bayesian model updating of nonlinear systems using nonlinear normal modes Mingming Song, Ludovic Renson, Jean-Philippe Noël, Babak Moa... Published: 19 September 2018
Structural Control and Health Monitoring, doi: 10.1002/stc.2258
DOI See at publisher website
BOOK-CHAPTER 0 Reads 0 Citations Hierarchical Bayesian Calibration and Response Prediction of a 10-Story Building Model Mingming Song, Iman Behmanesh, Babak Moaveni, Costas Papadim... Published: 31 July 2018
Conference Proceedings of the Society for Experimental Mechanics Series, doi: 10.1007/978-3-319-74793-4_20
DOI See at publisher website
BOOK-CHAPTER 0 Reads 0 Citations Structural Identification for Dynamic Strain Estimation in Wind Turbine Towers Mansure Nabiyan, Hamed Ebrahimian, Babak Moaveni, Faramarz K... Published: 12 June 2018
Conference Proceedings of the Society for Experimental Mechanics Series, doi: 10.1007/978-3-319-74421-6_32
DOI See at publisher website
BOOK-CHAPTER 0 Reads 0 Citations Model Updating of a Wing-Engine Structure with Nonlinear Connections Mingming Song, Ludovic Renson, Jean-Philippe Noël, Babak Moa... Published: 07 June 2018
Conference Proceedings of the Society for Experimental Mechanics Series, doi: 10.1007/978-3-319-74280-9_40
DOI See at publisher website
Article 0 Reads 3 Citations An application of finite element model updating for damage assessment of a two-story reinforced concrete building and co... Mingming Song, Seyedsina Yousefianmoghadam, Mohammad-Ebrahim... Published: 07 November 2017
Structural Health Monitoring, doi: 10.1177/1475921717737970
DOI See at publisher website
Article 0 Reads 4 Citations Structural Identification of an 18-Story RC Building in Nepal Using Post-Earthquake Ambient Vibration and Lidar Data Hanshun Yu, Mohammed A. Mohammed, Mohammad E. Mohammadi, Bab... Published: 24 February 2017
Frontiers in Built Environment, doi: 10.3389/fbuil.2017.00011
DOI See at publisher website ABS Show/hide abstract
Few studies have been conducted to systematically assess post-earthquake condition of structures using vibration measurements. This paper presents system identification and finite element (FE) modeling of an 18-story apartment building that was damaged during the 2015 Gorkha earthquake and its aftershocks in Nepal. In June 2015, a few months after the earthquake, the authors visited the building and recorded the building’s ambient acceleration response. The recorded data are analyzed, and the modal parameters of the structure are identified using an output-only system identification method. A linear FE model of the building is also developed to estimate numerically its dynamic properties. The identified modal parameters are compared to those of the model to identify possible shortcomings of the modeling and identification approaches. The identified natural frequencies and mode shapes for two of the three closely spaced vibration modes in the lower frequency range of interest (0.2–1.0 Hz) are in good agreement with the numerical model. The model is used to estimate the response of the building to the nearby recorded ground motion due to earthquake and the main aftershock. The maximum drift ratios are compared to the observed damage in the building and surface defects detected and quantified by the lidar scans as the research team performed a series of light detection and ranging (lidar) scans from interior of selected floors to document the damage patterns along the height of the building.
Conference papers
CONFERENCE-ARTICLE 32 Reads 0 Citations <strong>Integration of Sensor Data with Physics-based Models for Performance Assessment of Civil Structures</strong> Babak Moaveni Published: 14 November 2018
doi: 10.3390/ecsa-5-05761
DOI See at publisher website ABS Show/hide abstract

Structural identification methods using sensor data have received increased attention in the civil engineering research community with the objective of identifying structural performance, and evaluating the remaining useful life of structures. While many researchers have successfully applied various approaches to numerical and/or small-scale laboratory models of structures, the literature lacks many successful applications to large‐scale civil structures under real loading environment. This study highlights the challenges of structural health monitoring methods for applications to large‐scale civil structures, especially when dealing with changing ambient and environmental conditions. A hierarchical Bayesian framework is presented for probabilistic model updating and damage identification to account for inherent as well as parameter estimation and measurement uncertainties. It is shown that the proposed hierarchical framework allows to explicitly account for pertinent sources of variability such as ambient temperature and/or excitation amplitude and therefore yields more accurate predictions. The study also highlights the value of using point cloud data in addition to vibration measurements for structural performance assessment. The point clouds are informative about identification of cracks at their early stages while the vibration data provide measure of stiffness at later stages of damage. Performance of the proposed approach is demonstrated through application to three large-scale reinforced concrete building structures.

Top