Structural Identification of a 52-Story High-Rise in Downtown Los Angeles Based on Short-Term Wind Vibration Measurements

Abstract

This paper presents a case study of a realistic application and evaluation of a promising structural health monitoring approach that exploits some topological features of building-like structures to develop a reduced-order, reduced-complexity, not-necessarily-linear, substructure model. The approach not only reliably detects the occurrence of anomalous features that can reflect incipient damage and deterioration but also provides the locations of the structure’s regions where a single or multiple changes have been detected. The target structure used in this study is a 52-story building in Los Angeles that is instrumented with a relatively dense sensor array and is being continuously monitored through the efforts of the community seismic network (CSN). Two qualitatively different system identification approaches (global and substructuring) are applied to the large data set of ambient acceleration measurements produced by a strong wind event (“Santa Ana winds”) to identify the dominant modal characteristics of the building. The results are shown to match the corresponding results from a high-resolution computational model of the building based on a widely used structural analysis software package (ETABS) developed by Computers and Structures, Inc. The main contribution of this study is to demonstrate the practical feasibility of the proposed substructuring approach with a high-order system using both wind and low-amplitude ambient vibration measurements. The approach also assesses the accuracy and reliability of the estimates of the dominant modal features of the structure to subsequently provide a probabilistic measure of confidence in the extent and location of changes if an anomaly is detected. Due to the minimal computational resources needed to implement the proposed substructuring approach, it is efficient for near-real-time applications where important structures need to be continuously monitored for sustainability as well as resiliency requirements. The method is applicable to linear, nonlinear nonhysteretic, and hysteretic systems, with no restriction on the source of the signal for identification purposes.