Bayesian model updating of a 20-story office building utilizing operational modal analysis results. Issue 16 (December 2019)

• Record Type: Journal Article
• Authors: Lam, Heung Fai; Hu, Jun; Adeagbo, Mujib Olamide
• Other Names: Zhou Lu guest-editor.; Ni Yi-Qing guest-editor.
• Is Part Of: Advances in structural engineering. Volume 22:Issue 16(2019)
• Page Start: 3385
• Page End: 3394
• Publication Date: 2019-12
• Subjects: ambient vibration test -- Bayesian model updating -- Markov chain Monte Carlo simulation -- operational modal identification -- system identification; Structural engineering -- Periodicals; Construction, Technique de la; Structural engineering; Periodicals; 624.1
• DOI: 10.1177/1369433218825043
• Languages: English
• ISSNs: 1369-4332

Abstract

Most existing buildings are not equipped with long-term monitoring system. For the structural model updating and damage detection of this type of structures, ambient vibration test is popular as artificial excitation is not required. This article presents in detail the full-scale ambient vibration test, operational modal analysis, and model updating of a tall building. To capture the dynamic properties of the target 20-story building with limited number of sensors, a 15-setup ambient vibration test was designed to cover at least three measurement points (each consists of a vertical and two orthogonal horizontal measured degrees of freedom) for each selected floor. The modal parameters of each setup were extracted from the measured acceleration signals using a frequency domain decomposition method and were combined to form the global mode shape through the least-squares method. Due to the regularity of the building, a simple class of shear building models was employed to capture the dynamic characteristics of the building under lateral vibration. The identified modal parameters of the building were employed for the model updating of the shear building model to identify the distribution of inter-story stiffness. Since the "amount" of the measured information is small when compared to the "amount" of required information for identifying the uncertain parameters, the model updating problem is unidentifiable. To handle this problem, the Markov chain Monte Carlo–based Bayesian model updating method is employed in this study. The identified modal parameters revealed interesting features about the dynamic properties of the building. The well-matched results between model-predicted and identified modal parameters show the validity of the shear building model in this case study. This study provides valuable experience in the area of structural model updating and structural health monitoring.