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Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF UTAH, THE
Doing Business As Name:University of Utah
PD/PI:
  • Luis Ibarra
  • (801) 581-6903
  • luis.ibarra@utah.edu
Award Date:07/28/2021
Estimated Total Award Amount: $ 395,742
Funds Obligated to Date: $ 395,742
  • FY 2021=$395,742
Start Date:09/01/2021
End Date:08/31/2024
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:MCA: Using Machine Learning to Predict Seismic Failure Limit States in Buildings
Federal Award ID Number:2121169
DUNS ID:009095365
Parent DUNS ID:009095365
Program:ECI-Engineering for Civil Infr
Program Officer:
  • Joy Pauschke
  • (703) 292-7024
  • jpauschk@nsf.gov

Awardee Location

Street:75 S 2000 E
City:SALT LAKE CITY
State:UT
ZIP:84112-8930
County:Salt Lake City
Country:US
Awardee Cong. District:02

Primary Place of Performance

Organization Name:University of Utah
Street:
City:
State:UT
ZIP:84112-8930
County:Salt Lake City
Country:US
Cong. District:02

Abstract at Time of Award

This Mid-Career Advancement (MCA) award will enable the Principal Investigator (PI) to train in machine learning (ML) methodologies in order to bridge the gap between performance-based earthquake engineering and ML to estimate the collapse limit state of structures. Structural collapse is a building’s most catastrophic failure mode and the most difficult to evaluate using traditional methodologies. This study will apply ML algorithms to predict structural collapse of buildings under strong seismic events. These algorithms will be trained to learn without being explicitly programmed and can transform the way in which structural systems are designed and evaluated. Performance-based methodologies are used for evaluation or design of important structures, but there are significant limitations to estimate structural failure due to model complexity and the sensitivity of drift and other response parameters to small input parameter variations. Also, most buildings are designed using simplified elastic methods, and the expected structural damage under natural hazard events is only approximated from general considerations. In this study, ML algorithms will be trained using numerical simulations and experimental test results to efficiently predict collapse of structural design alternatives. The synergistic collaboration with research partners at Stanford University and the Massachusetts Institute of Technology will build the PI's research capabilities in this area. The study will be complemented by an educational program based on high school outreach, support of graduate and undergraduate research students, and training demonstrations. This award will contribute to the National Science Foundation (NSF) role in the National Earthquake Hazards Reduction Program. Project data will be archived in the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) Data Depot (https://www.designsafe-ci.org). Current collapse methodologies are based on sophisticated nonlinear finite element models, which can be an onerous task in the design process and even for performance evaluation of existing systems. The project research objectives include: (i) implementation of optimal ML techniques for application to failure limit states, (ii) data mining of dynamic response of building structural components from available databases, and (iii) development of approaches to improve the performance of structural systems. Several promising strategies for the researched structural collapse application will be considered, such as variations of artificial neural networks, support vector machines, and response surface models. The following fundamental questions will be answered: (i) what level of building input data is required to efficiently predict collapse? and (ii) can ML algorithms be trained to assess the reserve capacity and redundancy of damaged systems, in which material deterioration is highly uncertain or key structural components (e.g., columns) are removed? The ML algorithms will be used to find hidden correlations associated to structural collapse and will be trained to consider several sets of input data, ranging from basic building information to nonlinear deteriorating input parameters. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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