An application of finite element model updating for damage assessment of a two-story reinforced concrete building and comparison with lidar

Abstract

This article investigates the performance of finite element model updating to identify the induced damage in a two-story reinforced concrete masonry-infilled building using vibration data as well as lidar (light detection and ranging) scans. The building, located in El Centro, California, was severely damaged due to the 2010 El Mayor–Cucapah (Baja California, Mexico) Earthquake, and it was planned to be demolished following a number of ambient and forced vibration tests. The forced vibration tests were performed using an eccentric mass shaker. During the testing sequence, damage was induced to the building by removing four exterior walls. The modal parameters of the structure are estimated using the ambient vibration and forced vibration measurements at the reference state and damaged state. Lidar data are also used to detect surface defects and quantify the temporal changes of surface defects caused by the wall removal and forced vibration tests. Based on site inspections, geometry measurements, and material test data, two initial finite element models are built, namely the un-tuned initial model and the tuned initial model. The tuned initial model implements stiffness reduction factors to account for the observed damage in the building at its reference state while the un-tuned model does not. Two sets of reference models are calibrated to represent the structure at the reference state using the un-tuned and tuned initial models. The reference models are then updated to fit the measured data at the damaged state of the building with damage being estimated as the loss of stiffness in updating substructures. The estimated damage is compared to the nominal value of induced damage and surface defects detected by lidar scans. The analysis of the results indicates that the un-tuned and tuned initial models provide similar updated models and damage identification results which are in good agreement with the nominal values of damage and lidar detection results.

Development of Image Processing for Crack Detection on Concrete Structures through Terrestrial Laser Scanning Associated with the Octree Structure

“…Above these, many researchers have carried out various studies on the damage detection and deformation quantification on various structures by TLS or other equivalent methods, such as photography [15][16][17][18][19][20]. Although these studies have evaluated TLS for the purpose of structural health monitoring, the data capacity, resolution, and accuracy of crack recognition are still pointed out as drawbacks of TLS [21,22].…”

Parameter Estimation of Autoregressive-Exogenous and Autoregressive Models Subject to Missing Data Using Expectation Maximization

“…Structural vibration data has popularly been used in the field for system identification (Juang and Pappa, 1985;James III et al, 1993;Andersen, 1997;Huang, 2001;Pakzad et al, 2011;Dorvash and Pakzad, 2012;Pakzad, 2013, 2014;Dorvash et al, 2013;Cara et al, 2014;Matarazzo and Pakzad, 2016c;Nagarajaiah and Chen, 2016), finite element model updating (Shahidi and Pakzad, 2014a,b;Yousefianmoghadam et al, 2016;Nozari et al, 2017;Song et al, 2017), and damage-sensitive feature extraction (Sohn et al, 2001;Gul and Catbas, 2009;Kullaa, 2009;Dorvash et al, 2015;Shahidi et al, 2015), with the ultimate goal of inferring information about the current condition of the monitored structure. Regarding regression models, He and De Roeck (1997) shows their utility for describing structural vibration responses, and Shahidi et al (2015) and Yao and Pakzad (2012) provide structural damage-sensitive features created using the parameters of regression models.…”

Bayesian model updating of nonlinear systems using nonlinear normal modes

“…FE model updating methods were initially formulated and used for application to linear elastic structural systems and have been widely used since then (e.g., previous studies). However, the application of model updating to nonlinear structures is still sparse.…”

“…NNMs were also shown to predict the behavior of the nonlinear structure at resonance where the risk of failure is the greatest. NNMs may therefore be considered as key nonlinear dynamical features and exploited for nonlinear model updating, as linear normal modes (natural frequencies, mode shapes) have been widely used in linear model updating with great successes . Peter et al pioneered model updating using NNMs in a deterministic approach.…”