• In December 2012, Professor Moaveni received an NSF CAREER award titled "CAREER: Probabilistic Nonlinear Structural Identification for Health Monitoring of Civil Structures."
    This Early Career Development (CAREER) award will provide new and improved structural health monitoring (SHM) methods for damage diagnosis and prognosis (estimating the remaining useful life) of structures. The research will focus on developing a new methodology for vibration-based SHM, based on probabilistic calibration of nonlinear finite element models of structures using their measured nonlinear response to moderate to large amplitude excitations such as earthquakes. The educational component of this project will be performed through K-12 outreach, undergraduate student education, graduate student education, and evaluation of outcomes of these educational goals.

    More information about this grant can be found at the NSF web site.
  • Professor Moaveni plans to recruit two research assistants (PhD students) starting Fall 2013.
    For more information, see current projects of Professor Moaveni on the research page or contact him directly at
  • In September 2012, Professor Moaveni was awarded an NSF grant to dynamically test a reinforced concrete frame building in-filled with unreinforced masonry walls.
    In this project titled "Pre/Post Earthquake Damage Assessment for Infilled RC Frame Buildings” Moaveni and lead investigator, Andreas Stavridis, assistant professor at the University of Texas, Arlington, will use dynamic shakers to induce damage on an existing two-story 1920s structure. The experimental data collected will enhance the understanding of the complex behavior of these structures and their failure mechanisms. The tests will also provide benchmark data for earthquake engineering researchers and practitioners.

    More information about this grant can be found at the NSF web site.
  • In February 2012, two magazines wrote about Professor Sanayei's research on structural health monitoring of The Powder Mill Bridge over Vernon Avenue, Barre, MA.

    Structure Magazine, February 2012
    "Structural Forensics: Investigating Structures and their Components, Vernon Avenue Bridge"
    Read article

    Rebuilding America's Infrastructure Magazine, December 2011
    "Monitoring 'work horse' bridges: Structural health monitoring system with more than 200 sensors is tested on a smaller, non-signature bridge"
    Read article

  • In August 2011, Professor Sanayei received a subcontract from the Long-Term Bridge Performance Program, Rutgers University.
    Source of Funding: Federal Highway Administration

    Title: Inclusion of Vernon Avenue Bridge in the Long Term Bridge Performance Program
    Duration: August 2011 through December 2012

    PI: Professor Masoud Sanayei, Tufts University
    Co-PI: Professor of Practice Brian R. Brenner, Tufts & FST
    Co-PI at UNH: Associate Professor Erin Santini Bell, UNH
  • In August 2011, Professor Moaveni received an NSF BRIGE award titled "BRIGE: Continuous Structural Health Monitoring Framework for Bridge Structures".
    This Broadening Participation Research Initiation Grant in Engineering (BRIGE) provides funding for the development of a probabilistic continuous structural health monitoring framework. The novel framework will allow structural damage to be estimated as a loss of stiffness in probabilistic terms. This new framework will be applied to a prototype footbridge (Dowling Hall Footbridge) located at the Tufts University campus. The footbridge is exposed to a wide range of environmental conditions and is large enough to exhibit complex structural behavior, providing an opportunity for a realistic assessment of structural integrity in the presence of varying environmental effects. As part of this framework, modal parameters of the Dowling Hall Footbridge are extracted based on the low-amplitude ambient vibration response measured using accelerometers and strain gages. The continuous stream of identified natural frequencies, mode shapes, and their statistical characteristics are fed into a recursive Bayesian finite element model updating algorithm for probabilistic damage identification.