Structural damage identification based on finite element (FE) model updating has been a research direction of increasing interest over the last decade in the mechanical, civil, aerospace, etc., engineering fields. Various studies have addressed direct, sensitivity-based, probabilistic, statistical, and iterative methods for updating FE models for structural damage identification. In contrast, evolutionary algorithms (EAs) are a type of modern method for FE model updating. Structural damage identification using FE model updating by evolutionary algorithms is an active research focus in progress but lacking a comprehensive survey. In this situation, this study aims to present a review of critical aspects of structural damage identification using evolutionary algorithm-based FE model updating. First, a theoretical background including the structural damage detection problem and the various types of FE model updating approaches is illustrated. Second, the various residuals between dynamic characteristics from FE model and the corresponding physical model, used for constructing the objective function for tracking damage, are summarized. Third, concerns regarding the selection of parameters for FE model updating are investigated. Fourth, the use of evolutionary algorithms to update FE models for damage detection is examined. Fifth, a case study comparing the applications of two single-objective EAs and one multi-objective EA for FE model updating-based damage detection is presented. Finally, possible research directions for utilizing evolutionary algorithm-based FE model updating to solve damage detection problems are recommended. This study should help researchers find crucial points for further exploring theories, methods, and technologies of evolutionary algorithm-based FE model updating for structural damage detection.

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Structural damage detection using finite element model updating with evolutionary algorithms: a survey

Vibration-based structural health monitoring (SHM) for long-span bridges has become a dominant research topic in recent years. The Nam O Railway Bridge is a large-scale steel truss bridge located on the unique main rail track from the north to the south of Vietnam. An extensive vibration measurement campaign and model updating are extremely necessary to build a reliable model for health condition assessment and operational safety management of the bridge. The experimental measurements are carried out under ambient vibrations using piezoelectric sensors, and a finite element (FE) model is created in MATLAB to represent the physical behavior of the structure. By model updating, the discrepancies between the experimental and the numerical results are minimized. For the success of the model updating, the efficiency of the optimization algorithm is essential. Particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are employed to update the unknown model parameters. The result shows that PSO not only provides a better accuracy between the numerical model and measurements, but also reduces the computational cost compared to GA. This study focuses on the stiffness conditions of typical joints of truss structures. According to the results, the assumption of semi-rigid joints (using rotational springs) can most accurately represent the dynamic characteristics of the truss bridge considered.

https://biblio.ugent.be/publication/8582579/file/8582583.pdf

Model Updating for Nam O Bridge Using Particle Swarm Optimization Algorithm and Genetic Algorithm.

Structural health monitoring plays an increasingly significant role in detecting damages for large and complex structures to ensure their serviceability and sustainability. Optimal sensor placement...

Computational methodologies for optimal sensor placement in structural health monitoring: A review:

The aim of this article is to propose novel damage indices for damage localization and quantification based on time series modeling. In order to extract damage-sensitive features from time series m...

/pdf/an-unsupervised-learning-approach-by-novel-damage-indices-in-45cuav0yhz.pdf

An unsupervised learning approach by novel damage indices in structural health monitoring for damage localization and quantification

Sensitivity-based approaches to model updat- Q2 ing have become widely used because of their capability to calibrate the model by taking into account the influence of updating parameters associated to different structural elements. Global sensitivity analysis (SA) allows model updating to be carried out even in the case of elevated un- certainty about the material characteristics. Architectural heritage structures deserve specific attention on account of their intrinsic geometrical complexity and heterogene- ity. In this article, the concept of global SA is applied for the first time to complex monumental structures, and a comparative view is offered on more classical local sen- sitivity approaches. Different finite element (FE) calibra- tion techniques—via global and local SA—were applied to the intriguing case of the church of S. Maria del Suf- fragio in L'Aquila (Italy), severely damaged by the 2009 earthquake. The FE updating was based on experimental data acquired by a dynamic monitoring system. Finally, calibration strategies were assessed through time history analyses by comparing the responses to the recorded seismic event.

Tomáš Harčarik

Papers

Modal Assurance Criterion

Frequency analysis of acoustic signal using the Fast Fourier Transformation in MATLAB

Symmetries of differential equations describing beams and plates on elastic foundations