No 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.

This paper presents a Bayesian model updating methodology for dynamical systems with geometric nonlinearities based on their nonlinear normal modes (NNMs) extracted from broadband vibration data. Model parameters are calibrated by minimizing selected metrics between identified and model-predicted NNMs. In the first approach, a deterministic formulation is adopted, and parameters are updated by minimizing a nonlinear least-squares objective function. A probabilistic approach based on Bayesian inference is next investigated, where a Transitional Markov Chain Monte Carlo is implemented to sample the joint posterior probability distribution of the nonlinear model parameters. Bayesian model calibration has the advantage to quantify parameter uncertainty and to provide an estimation of model evidence for model class selection. The two formulations are evaluated when applied to a numerical cantilever beam with geometrical nonlinearity. The NNMs of the beam are derived from simulated broadband data through nonlinear subspace identification and numerical continuation. Accuracy of model updating results is studied with respect to the level of measurement noise, the number of available datasets, and modeling errors.

No abstract

No abstract

Mechanics-based dynamic models are commonly used in the design and performance assessment of structural systems, and their accuracy can be improved by integrating models with measured data. This paper provides an overview of hierarchical Bayesian model updating which has been recently developed for probabilistic integration of models with measured data, while accounting for different sources of uncertainties and modeling errors. The proposed hierarchical Bayesian framework allows one to explicitly account for pertinent sources of variability such as ambient temperatures and/or excitation amplitudes, as well as modeling errors, and therefore yields more realistic predictions. The paper reports observations from applications of hierarchical approach to three full-scale civil structural systems, namely (1) a footbridge, (2) a 10-story reinforced concrete (RC) building, and (3) a damaged 2-story RC building. The first application highlights the capability of accounting for temperature effects within the hierarchical framework, while the second application underlines the effects of considering bias for prediction error. Finally, the third application considers the effects of excitation amplitude on structural response. The findings underline the importance and capabilities of the hierarchical Bayesian framework for structural identification. Discussions of its advantages and performance over classical deterministic and Bayesian model updating methods are provided.

No abstract

No abstract