Structural identification methods using sensor data have received increased attention in the civil engineering research community with the objective of identifying structural performance, and evaluating the remaining useful life of structures. While many researchers have successfully applied various approaches to numerical and/or small-scale laboratory models of structures, the literature lacks many successful applications to large‐scale civil structures under real loading environment. This study highlights the challenges of structural health monitoring methods for applications to large‐scale civil structures, especially when dealing with changing ambient and environmental conditions. A hierarchical Bayesian framework is presented for probabilistic model updating and damage identification to account for inherent as well as parameter estimation and measurement uncertainties. It is shown that the proposed hierarchical framework allows to explicitly account for pertinent sources of variability such as ambient temperature and/or excitation amplitude and therefore yields more accurate predictions. The study also highlights the value of using point cloud data in addition to vibration measurements for structural performance assessment. The point clouds are informative about identification of cracks at their early stages while the vibration data provide measure of stiffness at later stages of damage. Performance of the proposed approach is demonstrated through application to three large-scale reinforced concrete building structures.
Integration of Sensor Data with Physics-based Models for Performance Assessment of Civil Structures
Published: 14 November 2018 by MDPI in 5th International Electronic Conference on Sensors and Applications session Structural Health Monitoring Technologies and Sensor Networks
Keywords: Structural identification; Uncertainty quantification; Model updating; Hierarchical Bayes modeling; Reinforced concrete buildings