A two-stage multi-damage detection approach for composite structures using MKECR-Tikhonov regularization iterative method and model updating procedure

The frequency equation of single damaged beam has been established for arbitrary boundary conditions that is the main tool for analysis as well as identification of damaged beam by using measured natural frequencies. A procedure for damage detection problem presented in this paper consists of three steps. First, the modelling error is reduced by a model updating procedure, in which the material, geometrical parameters and boundary conditions are updated. Then, measurement data are corrected based on the updated model. Finally, the damage parameters are identified using updated model and corrected measurement data. Theoretical investigation is illustrated by an example.

Download Full-text

A two-stage damage detection approach based on subset selection and genetic algorithms

Smart Structures and Systems ◽

10.12989/sss.2009.5.1.001 ◽

2009 ◽

Vol 5 (1) ◽

pp. 1-21 ◽

Cited By ~ 38

Author(s):

Gun Jin Yun ◽

Kenneth A. Ogorzalek ◽

Shirley J. Dyke ◽

Wei Song

Keyword(s):

Genetic Algorithms ◽

Damage Detection ◽

Subset Selection ◽

Two Stage ◽

Detection Approach

Download Full-text

An Improved Time-Domain Damage Detection Method for Railway Bridges Subjected to Unknown Moving Loads

Periodica Polytechnica Civil Engineering ◽

10.3311/ppci.15813 ◽

2020 ◽

Author(s):

Mahdi Shahbaznia ◽

Morteza Raissi Dehkordi ◽

Akbar Mirzaee

Keyword(s):

Damage Detection ◽

Health Monitoring ◽

Time Domain ◽

Structural Damage ◽

Model Updating ◽

Numerical Models ◽

Critical Parameters ◽

Moving Loads ◽

Railway Bridges ◽

Updating Procedure

There is considerable interest in structural health monitoring (SHM) and damage detection of bridges and considerable progress has been made in this field in recent years. However, several challenges such as sensitivity to low levels of damage and identification without the knowledge of the moving load remain and need to be precisely investigated by researchers. The current work addresses such challenges and proposes an efficient response sensitivity-based model updating procedure in time-domain for damage identification of railway bridges subjected to unknown moving loads. The bridge is modelled as an Euler-Bernoulli beam and the train is modelled as a set of sprung masses passing over the beam. Structural damage is considered as a reduction in the modulus of elasticity of the elements. Sensitivity analysis and Tikhonov regularization methods are adopted and used to solve the inverse problem of the model updating. To verify the efficiency of the model, two numerical models with multiple damage scenarios subjected to unknown moving loads are analyzed. In addition, the efficiency of the proposed method in the presence of measurement noise is also verified. Numerical results reveal that the proposed model-updating procedure simultaneously identifies structural damages as well as the unknown moving loads with an acceptable accuracy. The effect of critical parameters such as mass and speed of the moving vehicle on the accuracy of identification results is investigated as well. Based on the findings of this research, the proposed method can be adopted and applied to online and long-term health monitoring of real bridge structures.

Download Full-text

A Probabilistic Model Updating Algorithm for Fatigue Damage Detection in Aluminum Hull Structures

ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, Volume 2 ◽

10.1115/smasis2010-3838 ◽

2010 ◽

Cited By ~ 6

Author(s):

Masahiro Kurata ◽

Jun-Hee Kim ◽

Jerome P. Lynch ◽

Kincho H. Law ◽

Liming W. Salvino

Keyword(s):

Damage Detection ◽

Bayesian Model ◽

High Speed ◽

Model Updating ◽

Detection Method ◽

Residual Life ◽

Search Space ◽

Computational Effort ◽

Detection Approach ◽

Naval Engineering

The use of aluminum alloys in the design of naval structures offers the benefit of light-weight ships that can travel at high-speed. However, the use of aluminum poses a number of challenges for the naval engineering community including higher incidence of fatigue-related cracks. Early detection of fatigue induced cracks enhances maintenance of the ships and is critical for preventing the catastrophic failure of the hull. Furthermore, monitoring the integrity of the aluminum hull can provide valuable information for estimating the residual life of hull components. This paper presents a model-based damage detection methodology for fatigue assessment of hulls that are instrumented with a long-term hull monitoring system. At the core of the data driven damage detection approach is a Bayesian model updating algorithm enhanced with systematic enumeration and pruning of candidate solutions. The Bayesian model updating approach significantly reduce the computational effort by systematically narrowing the search space using errors functions constructed using the estimated modal properties associated with the condition of the structure. This study proposes the use of the Bayesian model updating technique to detect damage in an aluminum panel modeled using high-fidelity finite element models. The performance of the proposed damage detection method is tested through simulation of a progressively growing fatigue crack introduced in the vicinity of a welded stiffener element. An experimental study verifies the accuracy of the proposed damage detection method using an aluminum plate excited with a controlled excitation in the laboratory.

Download Full-text

Application of a Model Updating Technique for Health Condition Monitoring and Damage Detection of Flexible Structures

Volume 3C: 15th Biennial Conference on Mechanical Vibration and Noise — Vibration Control, Analysis, and Identification ◽

10.1115/detc1995-0704 ◽

1995 ◽

Author(s):

Joao A. Pereira ◽

Ward Heylen ◽

Stefan Lammens ◽

Paul Sas

Keyword(s):

Damage Detection ◽

Structural Damage ◽

Model Updating ◽

Flexible Structures ◽

Mesh Size ◽

Health Condition ◽

Frame Structure ◽

Damage Area ◽

Updating Procedure ◽

Real Experimental Data

Abstract This paper discusses the application of a damage detection methodology to monitor the location and extent of partial structural damage. The methodology combines, in an iterative way, the model updating technique based on frequency response functions (FRF) with monitoring data aiming at identifying the damage area of the structure. After the updating procedure reaches a good correlation between the models, it compares the parameters of the damage structure with those of the undamaged one to find the deteriorated area. The influence of the FEM mesh size on the evaluation of the extent of the damage has also been discussed. The methodology is applied using real experimental data from a spatial frame structure.

Download Full-text