Modern societies increasingly rely on the built environment to provide essential services that promote economic growth, social inclusion, governance, and quality of life. Severe natural hazards can manifest themselves in various direct and indirect losses to the built environment and disrupt the essential services, which results in a significant burden for individuals and communities seeking to recover from these hazards.

My lab’s research aims to develop smart resilient structural, infrastructure and urban systems that minimize the impacts of damaging hazards and facilitate better informed post-hazard planning and recovery in disaster-prone areas.

We are interested in the development of novel modeling and data fusion approaches that can combine information from multiple sources for quantitative structural monitoring and resilience assessment of civil infrastructure systems and, subsequently, for informed decision-making.

Additionally, we are interested in technological advancement toward the design and construction of resilient structural systems that could maintain or promptly recover functionality.

Research Project Highlights

Effects of Data Availability on Physical, Social and Economic Resilience Metrics for Informed Decision-making in Natural Hazards

Funded by National Institute of Standards and Technology (NIST)

This research developed a systematic approach to quantify the implication of data availability and accuracy on resilience metrics for informed decision-making across engineering, economic and sociological dimensions at the community level. The method of approach consists of 1) data and information availability, 2) community model development, 3) spatial hazard analysis, 4) physical damage and functionality analysis, and 5) socio-economic impact analysis. The proposed methodology is demonstrated using the illustrative example of the Memphis Metropolitan Statistical Area (MMSA) in TN, USA.

Performance-based post-earthquake decision-making for instrumented buildings

Funded by National Science Foundation (NSF)

Funded by NSF, this project developed a decision-making framework for post-earthquake assessment of instrumented buildings in a manner consistent with performance-based design criteria. This framework is achieved by simultaneously combining and advancing existing knowledge from seismic structural health monitoring and performance-based earthquake engineering paradigms. The framework consists of (1) measurement, (2) uncertainty modeling, (3) dynamic response reconstruction, (4) damage estimation, and (5) performance-based assessment and decision making. Since the proposed framework is probabilistic, the outcome can be used to obtain the probability of losses based on the defined decision variables and be integrated into a risk-based decision-making process by city officials, building owners, and emergency managers. The framework is illustrated using data from the Van Nuys hotel testbed, a seven-story reinforced concrete building instrumented by the California Strong Motion Instrumentation Program (CSMIP Station 24386).

An extended model-based observer for state estimation in nonlinear hysteretic structural systems

Funded by National Science Foundation (NSF)

Funded by NSF, this innovative research developed a model-based observer for state estimation in nonlinear hysteretic structural systems. The observer combines a nonlinear model and response measurements to estimate the complete dynamic response of the structure of interest. The main feature of the proposed observer (and its main advantage with respect to existing nonlinear state estimators) is that it is designed to be physically realizable as a nonlinear structural model, which allows the user to benefit from modeling capabilities available in finite element solvers. The performance of the proposed observer was 1) numerically verified in comparison with an unscented Kalman filter and 2) validated experimentally using data from a six-story full-scale benchmark wood frame building tested by the NEESWood project at the E-Defense facility in Miki, Japan. The results showed the effectiveness of the proposed observer to estimate the dynamic response of large-scale structural systems exhibiting a strong nonlinear behavior.

Nonlinear seismic response reconstruction and performance assessment of instrumented wood-frame buildings

Funded by National Science Foundation (NSF)

This research developed a methodology to reconstruct nonlinear seismic response and assess seismic performance of instrumented wood-frame buildings subjected to earthquakes. The methodology proposes the use of a nonlinear model-based state observer that combines global acceleration measurements and a nonlinear structural model of the building to estimate the complete dynamic response including displacements, velocities, accelerations at all degrees of freedom of the model, and internal forces in all structural members. From the estimated dynamic response, engineering demand parameters are obtained and com-pared with performance-based acceptance criteria to determine post-earthquake building occupancy classification. The methodology was successfully verified and validated using seismic response measurements and photographic records from the 2009 NEESWood Capstone building full-scale tests conducted at the E-Defense facility in Japan.

Cooperative Research to Enable Mass Timber Multi-Family Housing Technologies

Funded by United States Department of Housing and Urban Development (US-HUD)

This project aims to enable construction of low-cost resilient timber buildings by developing a novel balloon-style cross-laminated timber (CLT) structural system. In this project, I developed index building archetypes and performed computational OpenSees modeling and seismic collapse assessment based on the FEMA P695 in direct coordination and feedback from an Expert Panel comprised of stakeholders heavily involved in the mass timber wood industry and construction. The outcome of this project will serve directly as the design code proposal to the Provisions Update Committee of the Building Seismic Safety Council, and the ASCE Standard 7.

Estimation of element-by-element demand-to-capacity ratios in instrumented buildings using measured seismic response

Funded by National Science Foundation (NSF)

This project presented a methodology to estimate element-by-element demand-to-capacity ratios in instrumented steel moment-resisting frames subject to earthquakes. The methodology combines a finite element model and acceleration measurements at various points throughout the building to estimate time history of displacements and internal force demands in all members. The estimated demands and their uncertainty are compared with code-based capacity from which probabilistic bounds of demand-to-capacity ratios are obtained. The proposed methodology was verified using a simulated six-story building and validated using acceleration data from California Strong Motion Instrumentation Program station 24370 during the Northridge and Sierra Madre earthquakes.

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