Nonlinear Model-Data Fusion with Minimal Sensing for Performance-Based Seismic Monitoring of Instrumented Buildings

Abstract

This paper presents a new concept for performance-based monitoring (PBM) of instrumented buildings subject 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) measurement, 2) dynamic response reconstruction, 3) damage analysis, and 4) loss analysis and decision making. The main theoretical contribution of the proposed concept is the derivation of a nonlinear model-data fusion algorithm called nonlinear model-based observer (NMBO) for state estimation in nonlinear structural systems with minimal sensing (i.e., a limited number of global response measurements). 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. 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. The proposed methodology is validated using three case studies of experimental and real-world large-scale instrumented buildings. The first case study is a 6-story steel moment-resisting frame building in Burbank, CA, using the recorded acceleration data from the 1994 Northridge earthquakes. The second 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 third case is a seven-story reinforced concrete structure in Van Nuys, CA, severely damaged during the 1994 Northridge earthquake.