Localized health monitoring for seismic resilience quantification and safety evaluation of smart structures

Abstract

Natural hazards such as earthquakes, hurricanes, and floods cause billions of dollars of economic loss and claim thousands of lives every year. Researchers have proposed various tools and techniques to evaluate, manage, and mitigate seismic risk and improve resilience. Nonetheless, given the uncertainties involved, accurate system assessment requires real-time data on structural health and performance which can be attained through structural health monitoring (SHM) systems. This paper integrates component-based resilience quantification methods and SHM techniques to present a new probabilistic framework for seismic structural system evaluation and decision analysis. A trilateral framework is introduced which (1) uses the data obtained from a localized wireless sensor network to detect and locate damage, (2) develops component-based functionality curves to quantify the seismic resilience of the structure, and (3) makes post-quake rapid decisions based on a multi-criteria safety evaluation method. A nonlinear autoregressive exogenous (ARX) model is used to identify structural response and a statistical damage-sensitive coefficient is used to detect, locate, and measure damage in the system. Minor damages due to corrosion and major damages due to past extreme events are studied in the functionality analysis. Two groups of archetype building structures located in a high seismic region are studied, namely steel diagrid and special reinforced concrete structures. The results show that maximum absolute floor acceleration is the most accurate demand parameter for locating damage whereas maximum floor displacement and interstory drift ratio can effectively detect and measure damages.