STRUCTURAL DAMAGE ASSESSMENT USING A GENERALIZED MINIMUM RANK PERTURBATION THEORY

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.

In this paper, we propose a damage identification method of laminated composite structures based on dynamic residual forces. Dynamic residual forces are evaluated using an analytical model of undamaged structures and measured vibration data of damaged structures. In the first step, a rough damage region is identified from the dynamic residual forces. Then, error vectors of the residual forces are minimized to identify the accurate location and extent of structural damage. Effect of measurement points and measurement errors on the identification results is examined through numerical examples on symmetric laminated plates.

Vibration health monitoring of structures involves solving inverse structural dynamic problems that are often mathematically ill-conditioned. Many solution approaches have been developed, of which dynamic residual minimization yielded some of the most computationally attractive algorithms. Minimum Rank Perturbation Theory (MRPT) offers a closed form solution of model matrix perturbation using only a subset of the measured modes equal to the rank of perturbation. The initial formulation of the theory assumed symmetric model matrices in the equations of motion, but was later extended to deal with skew-symmetric matrices. The development led to the discovery of a new matrix property known as "null-symmetry", which helped explain how MRPT preserves symmetry and skew-symmetry in its solution. To handle incomplete measurements, an iterative algorithm based on MRPT was developed using repeated substitution of the dynamic model reduction transformation to achieve convergence. However, the algorithm can become unstable, and hence does not guarantee convergence. Driven by the limitation, dynamic least squares method was developed to solve for a robust set of parameter perturbations in a least square sense regularized by the reduced dynamic residual constraint. The algorithm is capable of estimating mass and stiffness parameters simultaneously under noise and uncertainty without repeatedly solving the eigenproblem.

Artificial Neural Networks (ANNs) have been applied in structural damage detection as a classifier, but generally a capable ANNs has to be trained with a certain amount of samples. When both damage locations and damage extents are to be identified, the amount of training samples is tremendous because of the combinations of damage locations and extents. By wavelet transform of the structure free motion equations, the Residual Wavelet Coefficient Vector (RWCV) is deduced. A damage feature parameter is defined as the ratio between RWCVs in two different frequency bands. This parameter has a unique property that it's sensitive only to damage locations, and is independent of damage extents. The damage feature parameters are then fed to the neural network for damage localization. After the damage sites are detected, the damage extent is further identified by another neural network with RWCVs as inputs. This two-phase approach for damage localization and extent identification can simply the neural network and reduce the training samples tremendously. Finally a numerical example is given for damage detection of a 10 DOFs system using the proposed approach.

Damage detection and localization in civil or mechanical structures is a subject of active development and research. A few vibration‐based methods have been developed so far, requiring, for example, modal parameter estimates in the reference and damaged states of the investigated structure, and for localization in addition a finite element model. For structures in operation, temperature has been shown to be a major nuisance to the efficiency of such methods because the modal parameters are varying not only with damage but also due to temperature variations. For detection, a few rejection approaches have been developed. Besides the increased complexity, environmental variation is hardly taken into account in localization approaches. In this paper, we propose a sensitivity‐based correction of the identified modal parameters in the damaged state with respect to the temperature field in the reference state, on the basis of a sensitivity analysis with respect to temperature dependent Parameters of the finite element model in the reference state. The approach is then applied to the stochastic dynamic damage locating vector method, where its improved performance under nonuniform temperature variations is shown in a numerical application on a beam.

Considering the issue of misjudgment in railway bridge damage identification, a method combining the step-by-step damage detection method with the statistical pattern recognition is proposed to detect the structural damage of a railway continuous girder bridge. The whole process of damage identification is divided into three identification sub-steps, namely, damage early warning, damage location, and damage extent identification. The multi-class pattern classification algorithm of C-support vector machine and the regression algorithm of ε-support vector machine are engaged to identify the damage location and damage extent, respectively. For verifying the proposed method, both of the proposed method and the optimization method are used to deal with the measured data obtained from a specific railway continuous girder model bridge. The results show that the proposed method can not only identify the damage location correctly, but also obtain the damage extent which is consistent with the experimental results accurately. By uncoupling finite element analysis and damage identification, normalizing the index, and seeking the separation hyper plane with maximum margin, the proposed method has more favorable advantages in generalization and anti-noise. As a result, it has the ability to identify the damage location and extent, and can be applied to the damage identification in real bridge structures.

This paper presents an enhanced version and the validation of a recently proposed methodology for earthquake-induced damage detection and localization in masonry towers by using long-term vibration monitoring data. The proposed enhanced method is based on continuous operational modal analysis through stochastic subspace identification, dynamic multiple linear regressive analysis to remove the effects of changing environmental conditions and finite element (FE) model updating. Any anomalous frequency deviation from normal conditions resulting from an earthquake triggers the damage localization process. This task is performed by solving an inverse FE model calibration problem, where equivalent elastic properties of macrostructural elements are identified by minimizing an objective function considering experimentally identified and numerically predicted damage-induced decays in natural frequencies and changes in eigenvector components. To minimize the computational effort of this calibration procedure, a quadratic surrogate model is constructed using a tuned numerical FE model of the structure. The methodology is validated through application to the “Sciri Tower”, an historic civic masonry tower located in Perugia, Italy, that has been continuously monitored by the authors for more than 1 year. The validation is carried out by using simulated damage scenarios and a set of real far-field earthquake data and is based on a FE model of the tower including surrounding buildings, calibrated on the basis of the measured data from ambient vibration tests. The results demonstrate that the proposed procedure is capable of correctly detecting and localizing earthquake-induced damages.

Damage localization in plate-like structures using time-varying feature and one-dimensional convolutional neural network

Damage diagnosis has become a valuable tool for asset management, enhanced by advances in sensor technologies that allows for system monitoring and providing massive amount of data for use in health state diagnosis. However, when dealing with massive data, manual feature extraction is not always a suitable approach as it is labor intensive requiring the intervention of domain experts with knowledge about the relevant variables that govern the system and their impact on its degradation process. To address these challenges, convolutional neural networks (CNNs) have been recently proposed to automatically extract features that best represent a system’s degradation behavior and are a promising and powerful technique for supervised learning with recent studies having shown their advantages for feature identification, extraction, and damage quantification in machine health assessment. Here, we propose a novel deep CNN‐based approach for structural damage location and quantification, which operates on images generated from the structure’s transmissibility functions to exploit the CNNs’ image processing capabilities and to automatically extract and select relevant features to the structure’s degradation process. These feature maps are fed into a multilayer perceptron to achieve damage localization and quantification. The approach is validated and exemplified by means of two case studies involving a mass‐spring system and a structural beam where training data are generated from finite element models that have been calibrated on experimental data. For each case study, the models are also validated using experimental data, where results indicate that the proposed approach delivers satisfactory performance and thus being an appropriate tool for damage diagnosis.

The present paper investigates the impact performance of woven-fabric carbon-fibre composites based upon both thermoplastic- and thermoset-matrix polymers under high-velocity impact loading by conducting gas-gun experiments at impact velocities of up to 100 m.s−1. The carbon-fibre reinforced-polymers (CFRPs) are impacted using soft- (i.e. gelatine) and hard- (i.e. aluminium-alloy) projectiles to simulate either a soft bird-strike or a hard foreign-body impact (e.g. runway debris), respectively, on typical composites employed in civil aircraft. The out-of-plane displacements of the impacted composite specimen are obtained by means of a three-dimensional Digital Image Correlation (DIC) system for the soft-projectile impact on the composites and the extent of damage is assessed both visually and by using portable C-scan equipment. The perforation resistance and energy absorbing capability of the composites are also studied by performing high-velocity impact experiments using the hard-projectile and the resulting extent and type of damage are identified. In addition, a Finite Element (FE) model is also developed to investigate the interaction between the projectile and the composite target.

A new efficient two-stage method for damage localization and quantification in shell structures

Transmissibility of non-linear output frequency response functions with application in detection and location of damage in MDOF structural systems

This paper is concerned with robust strategies to perform a model-based damage detection of multistory buildings effectively. Theoretically, the model-based damage detection can be reasonably carried out through vibration monitoring and system identification. The change in modal properties due to damage is identified accurately by time series model if structural vibrations are measured appropriately. Then, the reduction of local stiffness is estimated correctly from the change in modal properties by using sensitivity equations. However, in practice, it is not an easy task to pinpoint the location and extent of local damage because of noise contamination, system nonlinearity, estimation error, model uncertainty and so on. To overcome these problems, several robust strategies are presented in the model-based damage detection of multistory buildings. The basic idea consists of decreasing the number of physical parameters to be estimated, increasing the number of modal properties with good accuracy, enhancing sensitivity to damage and reducing noise and nonlinear effects. The effectiveness and limitations of these strategies are discussed through a series of shaking table tests of two small-scaled test structures.

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

This paper presents the operational modal analysis of a 90-m-high RC minaret of an iconic mosque considered as a landmark of the city. The minaret was monitored for three days with 11 tri-axial MEMS accelerometers. The purpose of the study was to observe the behavior, develop a representative finite element (FE) model, and establish baseline data for health monitoring studies. The modal properties were extracted using three operational modal analysis techniques (OMA): Enhanced Frequency Domain Decomposition (EFDD), Stochastic Subspace Identification (SSI), and Natural Excitation Technique with Eigensystem Realization Algorithm (NExT-ERA). The first 10 identified modes were below 7 Hz. Eight modes out of the ten were bending-dominant, while the remaining two were torsion-dominant. A FE model was also developed in ETABS to ascertain and compare the response of the structure with the identified results. From the FE model, the modes corresponding to the first ten identified modes were considered for comparison with the identified frequencies from ambient monitoring. The maximum 7.71% error was observed between the experimental and numerical frequencies. The error was minimized by using the manual updating the material properties and adding the weight of nonstructural elements. The variation of identified modal frequencies with ambient temperature was observed to be linearly dependent to a reasonable degree. A general trend of decreasing identified frequencies was observed with the rise in temperature.