scholarly journals Bayesian Probabilistic Framework for Damage Identification of Steel Truss Bridges under Joint Uncertainties

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Zheng ◽  
Yi Yu
Keyword(s):  
Damage Identification ◽  
Model Updating ◽  
Element Model ◽  
Truss Structures ◽  
Future Research ◽  
Model Parameters ◽  
Probabilistic Framework ◽  
Parameter Uncertainties ◽  
Truss Bridges ◽  
Uncertain Model

The vibration-based structural health monitoring has been traditionally implemented through the deterministic approach that relies on a single model to identify model parameters that represent damages. When such approach is applied for truss bridges, truss joints are usually modeled as either simple hinges or rigid connections. The former could lead to model uncertainties due to the discrepancy between physical configurations and their mathematical models, while the latter could induce model parameter uncertainties due to difficulty in obtaining accurate model parameters of complex joint details. This paper is to present a new perspective for addressing uncertainties associated with truss joint configurations in damage identification based on Bayesian probabilistic model updating and model class selection. A new sampling method of the transitional Markov chain Monte Carlo is incorporated with the structure’s finite element model for implementing the approach to damage identification of truss structures. This method can not only draw samples which approximate the updated probability distributions of uncertain model parameters but also provide model evidence that quantify probabilities of uncertain model classes. The proposed probabilistic framework and its applicability for addressing joint uncertainties are illustrated and examined with an application example. Future research directions in this field are discussed.

Updating Predictive Models: Calibration, Bias Correction and Identifiability

2010 ◽  
Author(s):  
Paul D. Arendt ◽  
Wei Chen ◽  
Daniel W. Apley
Keyword(s):  
Computer Model ◽  
Bayesian Approach ◽  
Bias Correction ◽  
Process Model ◽  
Model Updating ◽  
Future Research ◽  
Model Parameters ◽  
Sources Of Uncertainty ◽  
Uncertain Model ◽  
Many Sources

Model updating, which utilizes mathematical means to combine model simulations with physical observations for improving model predictions, has been viewed as an integral part of a model validation process. While calibration is often used to “tune” uncertain model parameters, bias-correction has been used to capture model inadequacy due to a lack of knowledge of the physics of a problem. While both sources of uncertainty co-exist, these two techniques are often implemented separately in model updating. This paper examines existing approaches to model updating and presents a modular Bayesian approach as a comprehensive framework that accounts for many sources of uncertainty in a typical model updating process and provides stochastic predictions for the purpose of design. In addition to the uncertainty in the computer model parameters and the computer model itself, this framework accounts for the experimental uncertainty and the uncertainty due to the lack of data in both computer simulations and physical experiments using the Gaussian process model. Several challenges are apparent in the implementation of the modular Bayesian approach. We argue that distinguishing between uncertain model parameters (calibration) and systematic inadequacies (bias correction) is often quite challenging due to an identifiability issue. We present several explanations and examples of this issue and bring up the needs of future research in distinguishing between the two sources of uncertainty.

scholarly journals Detection of Sparse Damages in Structures

2019 ◽  
Author(s):  
Natalia Sabourova ◽  
Niklas Grip ◽  
Ulf Ohlsson ◽  
Lennart Elfgren ◽  
Yongming Tu ◽  
...  
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Model Updating ◽  
Element Model ◽  
Total Variation Regularization ◽  
Regularization Technique ◽  
Spatial Properties ◽  
Ill Posed ◽  
Pros And Cons ◽  
Kirchhoff Plate Model

<p>Structural damage is often a spatially sparse phenomenon, i.e. it occurs only in a small part of the structure. This property of damage has not been utilized in the field of structural damage identification until quite recently, when the sparsity-based regularization developed in compressed sensing problems found its application in this field.</p><p>In this paper we consider classical sensitivity-based finite element model updating combined with a regularization technique appropriate for the expected type of sparse damage. Traditionally, (I), &#119897;2- norm regularization was used to solve the ill-posed inverse problems, such as damage identification. However, using already well established, (II), &#119897;l-norm regularization or our proposed, (III), &#119897;l-norm total variation regularization and, (IV), general dictionary-based regularization allows us to find damages with special spatial properties quite precisely using much fewer measurement locations than the number of possibly damaged elements of the structure. The validity of the proposed methods is demonstrated using simulations on a Kirchhoff plate model. The pros and cons of these methods are discussed.</p>

Application of a FRF Based Model Updating Technique for the Validation of Finite Element Models of Components of the Automotive Industry

1995 ◽  
Author(s):  
Stefan Lammens ◽  
Marc Brughmans ◽  
Jan Leuridan ◽  
Ward Heylen ◽  
Paul Sas
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Automotive Industry ◽  
Stiffness Matrix ◽  
Model Updating ◽  
Dynamic Stiffness ◽  
Element Model ◽  
Model Parameters ◽  
Finite Element Models ◽  
Analytical Dynamic

Abstract This paper presents two applications of the RADSER model updating technique (Lammens et al. (1995) and Larsson (1992)). The RADSER technique updates finite element model parameters by solution of a linearised set of equations that optimise the Reduced Analytical Dynamic Stiffness matrix based on Experimental Receptances. The first application deals with the identification of the dynamic characteristics of rubber mounts. The second application validates a coarse finite element model of a subframe of a Volvo 480.

Structural damage identification via physics-guided machine learning: a methodology integrating pattern recognition with finite element model updating

2020 ◽  
pp. 147592172092748 ◽  
Author(s):  
Zhiming Zhang ◽  
Chao Sun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Finite Element ◽  
Finite Element Model ◽  
Health Monitoring ◽  
Damage Identification ◽  
Model Updating ◽  
Element Model ◽  
Data Driven ◽  
Finite Element Model Updating

Structural health monitoring methods are broadly classified into two categories: data-driven methods via statistical pattern recognition and physics-based methods through finite elementmodel updating. Data-driven structural health monitoring faces the challenge of data insufficiency that renders the learned model limited in identifying damage scenarios that are not contained in the training data. Model-based methods are susceptible to modeling error due to model idealizations and simplifications that make the finite element model updating results deviate from the truth. This study attempts to combine the merits of data-driven and physics-based structural health monitoring methods via physics-guided machine learning, expecting that the damage identification performance can be improved. Physics-guided machine learning uses observed feature data with correct labels as well as the physical model output of unlabeled instances. In this study, physics-guided machine learning is realized with a physics-guided neural network. The original modal-property based features are extended with the damage identification result of finite element model updating. A physics-based loss function is designed to evaluate the discrepancy between the neural network model output and that of finite element model updating. With the guidance from the scientific knowledge contained in finite element model updating, the learned neural network model has the potential to improve the generality and scientific consistency of the damage detection results. The proposed methodology is validated by a numerical case study on a steel pedestrian bridge model and an experimental study on a three-story building model.

The Design of a Robust Weighted Measurement Fusion Steady-State Kalman Filter

2014 ◽  
Vol 701-702 ◽  
pp. 624-629
Author(s):  
Wen Qiang Liu ◽  
Xue Mei Wang ◽  
Zi Li Deng
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Lyapunov Equation ◽  
Model Parameters ◽  
Invariant System ◽  
Parameter Uncertainties ◽  
Worst Case ◽  
Uncertain Model ◽  
Time Invariant ◽  
Time Invariant System

For the linear discrete-time multisensor time-invariant system with uncertain model parameters and measurement noise variances, by introducing fictitious noise to compensate the parameter uncertainties, using the minimax robust estimation principle, based on the worst-case conservative multisensor system with conservative upper bounds of measurement and fictitious noises variances, a robust weighted measurement fusion steady-state Kalman filter is presented. By the Lyapunov equation approach, it is proved that when the region of the parameter uncertainties is sufficient small, the corresponding actual fused filtering error variances are guaranteed to have a less-conservative upper bound. Simulation results show the effectiveness and correctness of the proposed results.

Robust Centralized Fusion Steady-State Kalman Filter with Uncertain Parameters and Measurement Noise Variances

2014 ◽  
Vol 701-702 ◽  
pp. 532-537 ◽  
Author(s):  
Xue Mei Wang ◽  
Wen Qiang Liu ◽  
Zi Li Deng
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Lyapunov Equation ◽  
Small Region ◽  
Measurement Noise ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
New Approach ◽  
Uncertain Model ◽  
Robust Kalman Filter

For the linear discrete time multisensor system with uncertain model parameters and measurement noise variances, the centralized fusion robust steady-state Kalman filter is presented by a new approach of compensating the parameter uncertainties by a fictitious noise. Based on the Lyapunov equation, it is proved that for given fictitious noise variance, the variances of the actual filtering errors have a less-conservative upper bound when the uncertainty of parameters is limited in a sufficiently small region which is called as robust region of the parameter uncertainties. Further, a simulation example demonstrates how to search the robust region. It is also proved that the robust accuracy of the centralized fusion robust steady-state Kalman filter is higher than that of each local robust Kalman filter. A simulation example shows its effectiveness.

Finite Element Model Updating Approach to Damage Identification in Beams Using Particle Swarm Optimization

2008 ◽  
Author(s):  
Mohamed M. Saada ◽  
Mustafa H. Arafa ◽  
Ashraf O. Nassef
Keyword(s):  
Finite Element ◽  
Particle Swarm Optimization ◽  
Finite Element Model ◽  
Natural Frequencies ◽  
Damage Identification ◽  
Model Updating ◽  
Particle Swarm ◽  
Element Model ◽  
Pso Algorithm ◽  
Swarm Optimization

The use of vibration-based techniques in damage identification has recently received considerable attention in many engineering disciplines. While various damage indicators have been proposed in the literature, those relying only on changes in the natural frequencies are quite appealing since these quantities can conveniently be acquired. Nevertheless, the use of natural frequencies in damage identification is faced with many obstacles, including insensitivity and non-uniqueness issues. The aim of this paper is to develop a viable damage identification scheme based only on changes in the natural frequencies and to attempt to overcome the challenges typically encountered. The proposed methodology relies on building a Finite Element Model (FEM) of the structure under investigation. A modified Particle Swarm Optimization (PSO) algorithm is proposed to facilitate updating the FEM in accordance with experimentally-determined natural frequencies in order to predict the damage location and extent. The method is tested on beam structures and was shown to be an effective tool for damage identification.

scholarly journals Finite element model updating using Lagrange interpolation

2019 ◽  
Vol 23 (1) ◽  
pp. 228-232
Author(s):  
F. Asma
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Degrees Of Freedom ◽  
Model Updating ◽  
Element Model ◽  
Truss Structures ◽  
Finite Element Model Updating ◽  
Initial Model ◽  
Information Matrices ◽  
Matrix Interpolation

Abstract In this paper, an iterative finite element model updating method in structural dynamics is proposed. This uses information matrices and element connectivity matrices to reconstruct the corrected model by reproducing the frequency response at measured degrees of freedom. Indicators have been proposed to quantify the mismodelling errors based on a development in Lagrange matrix interpolation. When applied on simulated truss structures, the model gives satisfactory results by detecting and quantifying the defaults of the initial model.

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