Structural damage detection using a multi-stage improved differential evolution algorithm (Numerical and experimental)

A multi-stage gravitational search algorithm (MSGSA) is proposed here to solve the optimization-based damage detection of structural systems. Natural frequency changes of a structure are considered as a criterion for damage occurrence. Finite element method and structural dynamic principles are also employed to evaluate the required natural frequencies. The structural damage detection problem is first transformed into a standard optimization problem dealing with continuous variables, and then the MSGSA is utilized to solve the optimization problem for finding the site and severity of damage. In order to assess the performance of the proposed method for damage identification, an example with experimental data and two numerical examples with considering measurement noise are considered. All the results demonstrate the effectiveness of the proposed method for accurately determining the site and extent of multiple structural damage. Also, the performance of the MSGSA for damage detection compared to the standard gravitational search algorithm (GSA) is confirmed by examples.

Nowadays, structural health monitoring has attracted much attention, due to the construction of important and complex structures and their safety. One branch of structural health monitoring is damage detection, estimating the location and extent of eventual damages in the structural systems. In this study, an efficient method is introduced to determine the location and extent of damage in the frame, beam and truss structures using time domain response and an optimization algorithm. First of all, the structural damage detection problem is formulated as a standard optimization problem. So that, the optimization objective function is defined by using the acceleration of damaged structures and analytical model acceleration. The acceleration is obtained using Newmark method. Damage is simulated by reducing the elasticity modulus of structural members. Then, damage problem converted to an optimization problem are solved by teaching–learning-based optimization (TLBO) algorithm therefore, the exact location and extent of damage in structure can be determined. In order to show the capabilities of the proposed approach for identifying structural damage, four illustrative test examples are considered with considering measurement noise effect. The results clearly show that the proposed method is as a powerful method to detect multiple damage in structures.

Comparative study of using rotation influence lines and their derivatives for structural damage detection

Stiffness-based structural health monitoring methods are widely used for detecting the damage in a structure. These stiffness-based structural health monitoring methods uses change in natural frequencies and modeshapes for damage detection. These methods are based on identifying the change in stiffness of the healthy and damage structure to predict the damage in the structure. These stiffness-based methods are not efficient for detecting a small damage in a structure as there is a negligible change in natural frequencies and modeshapes due to a small damage in a structure, however the damping characteristics of the structure are highly sensitive to the damage in a structure. In this paper, new damping-based damage detection procedure has been proposed. In the proposed procedure, the changes in damping matrix of the structure has been used to detect the damage in the structure. The proposed procedure is able (or can) to detect both the location of the damage and the extend of the damage in the structure. The proposed procedure of damping-based damage detection is a 2-step procedure. In the first step, damping matrices of both the healthy and damage structure are identified and in the second step, the identified damping matrices are used for damage detection. Numerical and experimental case studies are presented to demonstrate the effectiveness of the proposed procedure. The results have shown that the proposed damping-based damage detection procedure can be used for detecting damage in a structure with confidence.

Based on the measured vibration information, a method for detecting reduction in stiffness parameters of a structure using generalized flexibility matrix and changes in natural frequencies is presented in this paper. For incomplete measured data of the first mode shape, dynamic expansion of mode shape is introduced. The generalized flexibility matrix is approximately constructed by using the measured first frequency and the extended mode shape only, and it is further used to detect the possible damaged elements. An iterative algorithm using the changes of the first several natural frequencies only is subsequently used to update the damage extent of these possible damaged elements, while the number of damage-extent parameters to be determined in the algorithm is remarkably reduced. Finally, two numerical examples concerning a two-span beam and a two-story frame are used to illustrate the effectiveness of the proposed method.

Vibration-based Structural Damage Detection Using Twin Gaussian Process (TGP)

Two major challenges associated with a vibration-based damage detection method using changes in natural frequencies are addressed: accurate modeling of structures and the development of a robust inverse algorithm to detect damage, which are defined as the forward and inverse problems, respectively. To resolve the forward problem, new physics-based finite element modeling techniques are developed for fillets in thin-walled beams and for bolted joints, so that complex structures can be accurately modeled with a reasonable model size. To resolve the inverse problem, a logistic function transformation is introduced to convert the constrained optimization problem to an unconstrained one, and a robust iterative algorithm using the Levenberg-Marquardt method is developed to accurately detect the locations and extent of damage. The new methodology can ensure global convergence of the iterative algorithm in solving under-determined system equations and deal with damage detection problems with relatively large modeling error and measurement noise. It is applied to various engineering structures including lightning masts, a space frame structure and one of its components, and a pipeline. The exact locations and extent of damage can be detected in the numerical simulation, and the locations and extent of damage can be successfully detected in experimental damage detection.

Genetic fuzzy system for damage detection in beams and helicopter rotor blades

In this study, we investigated the effect of fatigue crack propagation of the beams which have a vital importance in engineering applications, on the natural frequency of the system. Beams which have a wide range of applications, are used as fundamental structural elements in engineering structures. Therefore, early detection of any damages in these structures is of vital importance for the prevention of possible destructive damages. One of the widely used methods of early detection of damages is the vibration analysis of the structure. Hence, it is of vital importance to detect and monitor any changes in the natural frequencies of the structure. From this standpoint, in this study we experimentally investigated the effect of fatigue crack propagation on beams produced from 4140 steel, of the natural frequency of the beam. A crack was opened on the 8

Two-stage structural damage detection using fuzzy neural networks and data fusion techniques

Structural damage identification using static test data and changes in frequencies

On the basis of the theory that natural frequency changes and curvature mode shapes can be employed to determine the locations and degrees of damage of structures, a BP neural network technique with an improved input structure was developed. The two networks were used for diagnosis of structural damage, and structural damages were predicted using gray theory. The results showed that the gray theory to predict the structural damage neural network was applicable to irregular objects such injury problem diagnosis.

Damage detection of Kevlar woven fabric using optical fiber multimode interferometer

Need for developing efficient non-destructive damage detection procedures for civil engineering structures is growing rapidly. This paper presents a methodology for detection and quantification of structural damage using modal information obtained from transfer matrix technique. Vibration characteristics of beam-like structure have been determined using the computer program developed based on the formulations presented in the paper. It has been noted from reported literature that detection and quantification of damage using mode shape information is difficult and further, extraction of mode shape information has practical difficulties and limitations. Hence, a methodology for detection and quantification of damage in structure using tranfer matrix technique based on the changes in the natural frequencies has been developed. With an assumption of damage at a particular segment of the beam-like structure, an iterative procedure has been formulated to converge the calculated and measured frequencies by adjusting flexural rigidity of elements and then, the intersections are used for detection and quantification of damage. Eventhough the developed methodology is iterative, computational effort is reduced considerably by using transfer matrix technique. It is observed that the methodology is capable of predicting the location and magnitude of damage quite accurately.

Vibration-based damage detection methods have inherent uncertainties due to the perturbation of measurement noise, modeling errors, and environmental changes, such as variations in temperature. However, other measured local information, including the static strain and deflection measured by the structural health monitoring system, have not been taken full advantage of. The integration of such global and local information has the potential to lead to more accurate structural damage detection. This paper proposes a method for integrating the global and local information for structural damage detection based on Bayesian theory. First, the Bayesian probability model associated with natural frequencies, displacement mode shapes, and strain modes is developed. In this model, the local strain information is also used as an input. Second, to reduce the model's computational cost in complex structures, the strain energy damage index is employed to determine the potential damage ranges. Finally, the exact damage elements are detected by a proposed sequence elimination method. Numerical simulations on a 14-bay planar rigid structure and the experiment on a 20-bay rigid truss are carried out to demonstrate the effectiveness of the proposed method while considering model uncertainty and measurement noise. The results show that the proposed damage detection method can increase the model's identification accuracy and decrease its misjudgment rate, compared with the method that only uses global information.