Hamed Ebrahimian

Hamed Ebrahimian

Assistant Professor
Hamed Ebrahimian
He, him, his

I believe in intuition and inspiration. Imagination is more important than knowledge. For knowledge is limited, whereas imagination embraces the entire world, stimulating progress, giving birth to evolution. It is, strictly speaking, a real factor in scientific research.
― Albert Einstein

Biography

Hamed Ebrahimian joined the University of Nevada, Reno in July 2019 as an assistant professor. Prior to joining the University, he was  a senior professional engineer in industry from 2017 to 2019, during which his work was partly focused on developing a novel technology solution for monitoring and damage diagnosis of aging bridges.

For about two years before that, Hamed was working as a postdoctoral scholar at the California Institute of Technology (Caltech), where he expanded the application of his research in geotechnical engineering and seismology fields. His Ph.D. is in structural engineering, from UC San Diego, and his two master’s degrees are in mechanical engineering (dynamic systems and control) and structural engineering. Before attending UC San Diego, he was serving as a structural engineering consultant and an academic lecturer since 2005.

Hamed’s technical background and expertise is in four fields:

  1. Bayesian inference method for estimation, identification, and model updating,
  2. Structural health monitoring and damage diagnosis,
  3. Computational structural and geotechnical mechanics, and
  4. Large-scale structural experiment and behavior study.


Hamed’s research is mainly focused on the integration of physics-based computational models with sensory data for monitoring, diagnosis, and prognosis of civil and mechanical systems in operational conditions and after extreme events. A numerical model trained with data is sometimes referred to as a digital twin of the real asset, which can be utilized in several industries to solve challenging remote diagnosis problems. Hamed’s research objective is to develop technology solutions to embed intelligence into operational management and disaster preparation, response, and recovery process for infrastructure systems and communities.

Education
  • Ph.D., Structural Engineering, University of California, San Diego, 2015
  • M.S., Mechanical Engineering, University of California, San Diego, 2013
  • M.S., Structural Engineering, Shiraz University, 2006
  • B.S., Civil Engineering, Shiraz University, 2004
Professional certifications & memberships

• Professional Civil Engineer (P.E.) License # C84371, Board for Professional Engineers, Land Surveyors, and Geologists, California
• Member and Committee Secretary, ASCE EMI Dynamics Committee
• Member, ASCE EMI Structural Health Monitoring & Control Committee

Prospective graduate students

Currently, I have a number of openings for highly motivated Ph.D. students to join my research group. A high-level description of prospect projects and required background is provided below. If you are interested, please send me your C.V. and a cover letter describing your interest and related experience.

Project theme 1 – Damage diagnosis technology for civil structures

The prospective research will be focused on developing practical technologies for operational monitoring and rapid post-disaster assessment of civil structures (emphasis on bridges, buildings, and energy-related infrastructures). Through this research, the graduate student will become an expert in (i) nonlinear finite element (FE) modeling and response simulation techniques, and (ii) structural health monitoring, nonlinear system identification, and model updating techniques.

You should be interested in learning theoretical methods, solving mathematical problems, and developing and implementing large-scale computer codes. To be eligible, you should have a solid background and interest in one or multiple of the following subjects:

  • Nonlinear computational mechanics, nonlinear FE modeling and response simulation in OpenSees and/or LS-DYNA. Experience in nonlinear mechanics of reinforced concrete is a plus.
  • Computer programming – past experience with Matlab, Python, and/or C++. Experience with cloud computing and GPU-based computing is a plus.
  • Methods for structural sensing, monitoring, and system identification
  • Random vibrations, stochastic signal processing
  • Engineering mathematics, statistics, and probability
  • Neural networks and artificial intelligence
  • Parameter estimation, model inversion, optimization

Project theme 2 – Experimental structural mechanics

The prospect research will be focused on laboratory experiment of large-scale specimens, developing computational models, and concluding practical design and assessment guidelines. Through this research, the graduate student will gain experience in (i) large-scale experimental research (including specimen design, construction, instrumentation, and testing), and (ii) nonlinear modeling and response simulation techniques.

To be eligible, you should have a solid background and interest in one or multiple of the following subjects:

  • Laboratory experiment of large-scale specimens
  • Nonlinear computational mechanics, nonlinear FE modeling and response simulation in OpenSees and/or LS-DYNA
  • Developing and/or implementing nonlinear material models for structural components
  • Methods for structural instrumentation, sensing, and monitoring
  • Structural design

Research interests

  • System identification (linear/nonlinear) and uncertainty quantification
  • Structural health monitoring, diagnosis, and prognosis
  • Integration of computational models with sensory data / Digital Twins
  • Bayesian inference, Bayesian model updating and model inversion
  • Stochastic filtering and data assimilation
  • Risk assessment / decision making under uncertainty
  • Nonlinear computational solid and structural mechanics, nonlinear finite element response simulation
  • Nonlinear mechanics of reinforced concrete
  • Soil-structure interaction
  • Large/full-scale experimental studies
  • Multi-hazard analysis, design, and assessment of civil structures
  • Disaster resilience
  • Structural dynamics and earthquake engineering

Selected publications

  1. H. Ebrahimian, R. Astroza, J.P. Conte, and R.R. Bitmead, "An Information-theoretic Approach for Identifiability Assessment of Nonlinear Structural Finite Element Models," ASCE Journal of Engineering Mechanics, 145(7), 2019.
  2. R. Astroza, H. Ebrahimian, and J.P. Conte, "Performance Comparison of Kalman−Based Filters for Nonlinear Structural Finite Element Model Updating," Journal of Sound and Vibration, 438 (6), 2019, 520–542.
  3. H. Ebrahimian, M. Kohler, A. Massari, D. Asimaki, "Parametric Estimation of Dispersive Viscoelastic Layered Media with Application to Structural Health Monitoring," Soil Dynamics and Earthquake Engineering, 105, 2018, 204–223.
  4. H. Ebrahimian, R. Astroza, J.P. Conte, and C. Papadimitriou, "Bayesian Optimal Estimation for Output-only Nonlinear System and Damage Identification of Civil Structures," Structural Control and Health Monitoring, DOI: 10.1002/stc.2128, 2018.
  5. H. Ebrahimian, R. Astroza, J.P. Conte, and T.C. Hutchinson, "Pre-test Nonlinear Finite Element Simulation of Five-story Reinforced Concrete Building Tested on the UCSD-NEES Shake Table," ASCE Journal of Structural Engineering, 144(3), 2018.
  6. R. Astroza, H. Ebrahimian, Y. Li, J.P. Conte, "Bayesian Nonlinear Structural FE Model and Seismic Input Identification for Damage Assessment of Civil Structures," Mechanical Systems and Signal Processing, 93(1), 2017, 661–687.
  7. H. Ebrahimian, R. Astroza, J.P. Conte, and R.A. de Callafon, "Nonlinear Finite Element Model Updating for Damage Identification of Civil Structures using Batch Bayesian Estimation," Mechanical Systems and Signal Processing, 10.1016/j.ymssp.2016.02.002, 2016.
  8. R. Astroza, H. Ebrahimian, J.P. Conte, J.I. Restrepo, and T.C. Hutchinson, "Influence of the Construction Process and Nonstructural Components on the Modal Properties of a Five-Story Building," Earthquake Engineering and Structural Dynamics, 10.1002/eqe.2695, 2016.
  9. R. Astroza, H. Ebrahimian, J.P. Conte, J.I. Restrepo, and T.C. Hutchinson, "System Identification of a Full-Scale Five-Story Reinforced Concrete Building Tested on the NEES-UCSD Shake Table," Structural Control and Health Monitoring, 23(3), 2016, 535-559.
  10. H. Ebrahimian, R. Astroza, and J.P. Conte, "Extended Kalman Filter for Material Parameter Estimation in Nonlinear Structural Finite Element Models using Direct Differentiation Method," Earthquake Engineering and Structural Dynamics, 44(10), 2015, 1495-1522.
  11. R. Astroza, H. Ebrahimian, and J.P. Conte, "Material Parameter Identification in Distributed Plasticity FE Models of Frame-type Structures using Nonlinear Stochastic Filtering," ASCE Journal of Engineering Mechanics, 141(5), 2014, 04014149.
  12.  M.C. Chen, E. Pantoli, X. Wang, R. Astroza, H. Ebrahimian, T.C. Hutchinson, J.P. Conte, J.I. Restrepo, C. Marin, K. Walsh, R. Bachman, M. Hoehler, R. Englekirk, and M. Faghihi, "Full-scale Structural and Nonstructural Building System Performance During Earthquakes: Part I – Specimen Description, Test Protocol and Structural Response," Earthquake Spectra, DOI: 10.1193/012414EQS016M, 2015.
  13. E. Pantoli, M.C. Chen, X. Wang, R. Astroza, H. Ebrahimian, T.C. Hutchinson, J.P. Conte, J.I. Restrepo, C. Marin, K. Walsh, R. Bachman, M. Hoehler, R. Englekirk, and M. Faghihi, "Full-scale Structural and Nonstructural Building System Performance during Earthquakes: Part II – NCS Damage States," Earthquake Spectra, DOI: 10.1193/012414EQS017M, 2015.
  14. E. Pantoli, M.C. Chen, T.C. Hutchinson, R. Astroza, J.P. Conte, H. Ebrahimian, J.I. Restrepo, and X. Wang, "Landmark Dataset from the Building Nonstructural Components and Systems (BNCS) Project," Earthquake Spectra, DOI: 10.1193/100614EQS150, 2015.