Performance-Based Seismic Monitoring of Instrumented Buildings

This dissertation develops a new concept for performance-based monitoring (PBM) of instrumented buildings subjected to earthquakes. This concept is achieved by simultaneously combining and advancing existing knowledge from structural mechanics, signal processing, and performance-based earthquake engineering paradigms. The PBM concept consists of 1) optimal sensor placement, 2) dynamic response reconstruction, 3) damage estimation, and 4) loss analysis. Within the proposed concept, the main theoretical contribution is the derivation of a nonlinear model-based observer (NMBO) for state estimation in nonlinear structural systems. The NMBO employs an efficient iterative algorithm to combine a nonlinear model and limited noise-contaminated response measurements to estimate the complete nonlinear dynamic response of the structural system of interest, in the particular case of this research, a building subject to an earthquake. The main advantage of the proposed observer over existing nonlinear recursive state estimators is that it is specifically designed to be physically realizable as a nonlinear structural model. This results in many desirable properties, such as improved stability and efficiency. Additionally, a practical methodology is presented to implement the proposed PBM concept in the case of instrumented steel, wood-frame, and reinforced concrete buildings as the three main types of structural systems used for construction in the United States. The proposed methodology is validated using three case studies of experimental and real-world large-scale instrumented buildings. The first case study is an extensively instrumented six-story wood frame building tested in a series of full-scale seismic tests in the final phase of the NEESWood project at the E-Defense facility in Japan. The second case study is a 6-story steel moment resisting frame building located in Burbank, CA, and uses the recorded acceleration data from the 1991 Sierra Madre and 1994 Northridge earthquakes. The third case is a seven-story reinforced concrete structure in Van Nuys, CA, which was severely damaged during the 1994 Northridge earthquake. The results presented in this dissertation constitute the most accurate and the highest resolution seismic response and damage measure estimates obtained for instrumented buildings. The proposed PBM concept will help structural engineers make more informed and swift decisions regarding post-earthquake assessment of critical instrumented building structures, thus improving earthquake resiliency of seismic-prone communities

Seismic resilience of interdependent built environment for integrating structural health monitoring and emerging technologies in decision-making

The functionality of interdependent infrastructure and resilience to seismic hazards have become a topic of importance across the world. The ability to optimize an engineered solution and support informed decisionmaking is highly dependent on the availability of comprehensive datasets and requires substantial effort to ingest into community-scale models. In this paper, a comprehensive seismic resilience modeling methodology is developed, with detailed multi-disciplinary datasets, and is explored using the state-of-the-science algorithms within the Interdependent Networked Community Resilience Modeling Environment (IN-CORE). The methodology includes a six-step chained/linked process consists of 1) community data and information, 2) spatial seismic hazard analysis using next-generation attenuation, 3) interdependent community model development, 4) physical damage and functionality analysis, 5) socio-economic impact analysis and 6) structural health monitoring (SHM) and emerging technologies (ET). An illustrative case study is presented to demonstrate the seismic functionality and resilience assessment of Shelby County in Memphis, Tennessee, in the United States. From the results discussion, it is then concluded that data from structural health monitoring and emerging technologies is a viable approach to enhance characterising the seismic hazard resilience of infrastructure, enabling rapid and in-depth understanding of structural behaviour in emergency situations. Moreover, considering the momentum of the digitalization era, setting a holistic framework on resilience, which includes SHM and ET, will allow reducing uncertainties that still a challenge to quantify and propagate, supported by sequential updating techniques from Bayesian statistics