MS 3-1: Vibration-based Health Monitoring, Damage Identification, and Parameter Estimation for Civil Engineering Structures


Geert Lombaert, Katholieke Universiteit Leuven, Leuven, Belgium

Guido De Roeck, Katholieke Universiteit Leuven, Leuven, Belgium

Edwin Reynders, Katholieke Universiteit Leuven, Leuven, Belgium

Costas Papadimitriou, University of Thessaly, Volos, Greece

Geert Lombaert    Guido De Roeck    Edwin Reynders    Costas Papadimitriou    

Brochure

This mini-symposium deals with vibration-based health monitoring, damage identification and parameter identification for civil engineering structures (bridges and viaducts, buildings, towers, …).

In vibration-based structural health monitoring and damage identification (detection, localisation, quantification, and prognosis), an attempt is made to identify structural damage from vibration data of a structure. For civil engineering structures, data obtained under ambient excitation is often used due to the difficulties associated with the forced excitation of large structures. A distinction is made between model-based and non model-based damage identification methods, depending on whether the identification relies on a physical model, e.g. Finite Element model, of the structure. A two-stage procedure is often applied where (1) a black-box model of the structure is identified from time or frequency domain data and (2) features of the black-box model such as the modal parameters (natural frequencies, mode shapes) are used to update a physics-based model. This methodology can be used to identify damage as a local reduction of the stiffness in the structure or to tune the structural model, such that the response predictions are in line with the observed behaviour of the structure. The resulting model can be used to update response and reliability predictions.

This minisymposium welcomes novel contributions on vibration-based structural health monitoring, damage identification, as well as parameter identification, using black-box as well as physics-based models. Relevant topics are linear and nonlinear system identification, statistical system identification methods (maximum-likelihood, Bayesian inference) for parameter and state estimation, model updating and correlation, uncertainty quantification in parameter identification, model class selection based on system response data, stochastic simulation techniques for state estimation and model class selection, optimal strategies for experimental design, optimal sensor location methods, updating response and reliability predictions using data.


 

Dynamic methods for health monitoring and structural identification of bridges
F. Benedettini, A. Morassi, F. Vestroni

Structural health monitoring of a centenary iron arch bridge
F. Busatta, C. Gentile, A. Saisi

Structural identification of a super-tall tower by GPS and accelerometer data fusion using a multi-rate Kalman filter
E. Chatzi, C. Fuggini

Maintenance and rehabilitation of 19th century masonry buildings – Life-cycle aspects
A. Kolbitsch

Dynamic damage identification using linear and nonlinear testing methods on a two span prestressed concrete bridge
J. Mahowald, S. Maas, F. Scherbaum, D. Waldmann, A. Zuerbes

Structural health monitoring from on-line monitored vibration measurements
T. Maung, H. Chen, A. Alani

Damage detection on the Champangshiehl bridge using blind source separation
V. Nguyen, C. Rutten, J. Golinval, J. Mahowald, S. Maas, D. Waldmann

Fast Bayesian structural damage localization and quantification using high fidelity FE models and CMS techniques
D. Papadioti, C. Papadimitriou

Output-only structural health monitoring by vibration measurements under changing weather conditions
E. Reynders, G. Wursten, G. De Roeck

Influence of the prediction error correlation model on Bayesian FE model updating results
E. Simoen, C. Papadimitriou, G. De Roeck, G. Lombaert

Hybrid genetic algorithm to system identification and damage assessment of a high-rise building
G. Wang, F. Huang

Non-stationary random vibration for a high-pier bridge under vehicular loads
X. Yin, C. Cai, Y. Liu, J. Zhang

Monitoring of a riveted steel railway bridge
V. Zabel, A. Schmidt, I. Reichert, S. Höll

Fast Bayesian ambient modal identification with separated modes incorporating multiple setups
F. Zhang, S. Au


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