Damage detection in elastic structures using vibratory residual forces and weighted sensitivity

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.

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

Advances in optic fiber sensing technology provide easy and reliable way for the vibration-based strain measurement of engineering structures. As a typical optic fiber sensing techniques with high accuracy and resolution, long-gauge Fiber Bragg Grating (FBG) sensors have been widely employed in health monitoring of civil engineering structures. Therefore, the development of macro strain-based identification methods is crucial for damage detection and structural condition evaluation. In the previous study by the authors, a damage detection algorithm for a beam structure with the direct use of vibration-based macro-strain measurement time history with neural networks had been proposed and validated with experimental measurements. In this paper, a damage locating and quantifying method was proposed using modal macrostrain vectors (MMSVs) which can be extracted from vibration induced macro-strain response measurement time series from long-gage FBG sensors. The performance of the proposed methodology for damage detection of a beam with different damage scenario was studied with numerical simulation firstly. Then, dynamic tests on a simply-supported steel beam with different damage scenarios were carried out and macro-strain measurements were employed to detect the damage severity. Results show that the proposed MMSV based structural identification and damage detection methodology can locate and identify the structural damage severity with acceptable accuracy.

Structural damage detection from measured vibration responses has gain popularity among the research community for a long time. Damage is identified in structures as reduction of stiffness and is determined from its sensitivity towards the changes in modal properties such as frequency, mode shape or damping values with respect to the corresponding undamaged state. Damage can also be detected directly from observed changes in frequency response function (FRF) or its derivatives and has become popular in recent time. A damage detection algorithm based on FRF curvature is presented here which can identify both the existence of damage as well as the location of damage very easily. The novelty of the present method is that the curvatures of FRF at frequencies other than natural frequencies are used for detecting damage. This paper tries to identify the most effective zone of frequency ranges to determine the FRF curvature for identifying damages. A numerical example has been presented involving a beam in simply supported boundary condition to prove the concept. The effect of random noise on the damage detection using the present algorithm has been verified.

In the present paper, Structural health monitoring has become an evolving area of research in last few decades with increasing need of online monitoring the health of large structures. The damage detection by visual inspection of the structure can prove impractical, expensive and ineffective in case of large structures like offshore platforms, multistoried buildings and bridges. Structural health monitoring is defined as the process of detecting damage in a structural system. Damage in the system causes a change in dynamic properties of a system. The structural damage is typically a local phenomenon, which tends to be captured by higher frequency signals. Most of vibration-based damage detection methods require the modal properties that are obtained from measured signals through the system identification techniques. However, the modal properties such as natural frequencies and mode shapes are not such a good sensitive indication of structural damage. Structural damage detection and damage localization of jacket platforms, based on wavelet packet transforms is presented in this paper. Dynamic signals measured from the structure by the finite element software package ANSYS are first decomposed into wavelet packet components. Component energies are then calculated and used for damage assessment. The results show that the WPT-based component energies are good candidate indices that are sensitive to structural damage. These component energies can be used for damage assessment including identifying damage occurrence and location.

Recently, the authors proposed computationally attractive algorithms to determine the location and extent of structural damage for undamped structures assuming damage results in a localized change in stiffness properties. The algorithms make use of a finite-element model and a subset of measured eigenvalues and eigenvectors. The developed theories approach the damage location and extent problem in a decoupled fashion. First, a theory is developed to determine the location of structural damage. With location determined, a damage extent theory is then developed. The damage extent algorithm is a minimum rank perturbation, which is consistent with the effects of many classes of structural damage on a finite-element model. In this work, the concept of the minimum rank perturbation theory (MRPT) is adopted to determine the damage extent on the mass properties of an undamped structure. In addition, the MRPT is extended to the case of proportionall y damped structures. For proportionally damped structures, the MRPT is used to find the damage extent in any two of the three structural property matrices (mass, damping, or stiffness). Finally, illustrative case studies using both numerical and actual experimental data are presented. HE advent of the Space Shuttle has prompted considerable attention to the design and control of large space structures. Due to the large size and complexity of envisioned structures, as well as the use of advanced materials to reduce structural weight, it may become necessary to develop a structural health monitoring system to detect and locate structural damage as it occurs. From experience gained in the machinery health monitoring field, one would expect the vibration signature of the structure, either frequency response functions and/or modal parameters, to provide useful information in determining the location and extent of structural damage. Assume that a refined finite element model (FEM) of the structure has been developed before damage has occurred. By refined, we mean that the measured and analytical modal properties are in agreement. Next, assume that at a later date some form of structural damage has occurred. If significant, the damage will result in a change in the structures modal parameters. The question is: can the discrepancy between the original FEM modal properties and postdamage modal properties be used to ascertain structural damage? Most prior work in damage detection has used the general framework of FEM refinement (system identification) in the development of damage assessment algorithms. The motivation behind the development of FEM refinement techniques is based on the need to validate engineering FEMs before their acceptance as the basis for final design analysis. The standard problem has been to seek a refined FEM that is as close to the original FEM and whose modal properties are in agreement with those that are measured subject to various constraints such as symmetry and sparsity preservation. A considerable amount of work in this area has been

Detection of structural damage is an inverse problem in structural engineering. There are three main questions in the damage detection: existence, location and extent of the damage. In concept, the natural frequency and mode shapes of any structure must satisfy an eigenvalue problem. But, if a potential damage exists in a structure, an error resulting from the substitution of the refined analytical finite element model and measured modal data into the structural eigenvalue equation will occur, which is called the residual modal forces, and can be used as an indicator of potential damage in a structure. In this study, a useful damage detection method is proposed and compared with other two methods. Two degree-of-freedom system and Cantilever beam are used to demonstrate the approach. And the results of three introduced method are compared.

The representation of structural dynamics in the absence of physics-based models, is often accomplished through the identification of parametric models, such as the autoregressive with exogenous inputs, e.g. ARX models. When the structure is amenable to environmental variations, parameter-varying extensions of the original ARX model can be implemented, allowing for tracking of the operational variability. Yet, the latter occurs in sufficiently longer time-scales (days, weeks, months), as compared to system dynamics. For inferring a “global”, long time-scale varying ARX model, data from a full operational cycle has to typically become available. In addition, when the sensor network comprises multiple nodes, the identification of long time-scale varying, vector ARX models grow in complexity. We address these issues by proposing a distributed framework for structural identification, damage detection and localization. Its main features are: (i) the individual estimation of local, single-input-single-output ARX models at every operational point; (ii) the long time-scale representation of each individual ARX coefficient via a Gaussian process regression, which captures dependency on varying Environmental and Operational Conditions (EOCs); (iii) the establishment of a distributed residual generation algorithm for damage detection, which produces time-series of well-defined stationary statistics, with detected discrepancies used for damage diagnosis; and, (iv) exploitation of ARX-inferred mode shape curvatures, obtained via ARX-inferred global state-space models, of the healthy and damaged states, for damage localization. The method is assessed via application on two numerical case studies of different complexity, with the results confirming its efficacy for diagnostics under varying EOCs.

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.

EXTRACTION OF RITZ VECTORS FROM VIBRATION TEST DATA

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

BackgroundDiagnosis and/or classification of a patient with axial Spondyloarthritis (SpA) generally relies on the presence of structural damage of the sacroiliac joints (SIJ) assessed by plain X-rays. However, the psychometric...

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

A novel procedure for damage identification of framed structures is proposed, where both the location and the extent of structural damage in framed structures can be correctly determined using only a limited number of measured natural frequencies. No knowledge of the modal shapes of the damaged structure is required. On the basis of the characteristic equations for the original and the damaged structure, a set of equations is generated. Two computational techniques, the direct iteration (DI) technique and the Gauss–Newton least-squares (GNLS) technique, are utilized to determine structural damage from the derived equations. Finally, different numerical examples are used to demonstrate the effectiveness of the proposed method. © 1997 John Wiley & Sons, Ltd.

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.