Vibration based damage detection of steel plate

Vibration-based damage monitoring in model plate-girder bridges under uncertain temperature conditions

A method is proposed for probabilistically model updating an initial deterministic finite element model using measured statistical changes in natural frequencies and mode shapes (i.e., modal parameters). The approach accounts for variations in the modal properties of a structure (due to experimental errors in the test procedure). A perturbation of the eigenvalue problem is performed to yield the relationship between the changes in eigenvalues and in the global stiffness matrix. This stiffness change is represented as a sum over every structural member by a product of a stiffness reduction factor and a stiffness submatrix. Monte Carlo simulations, in conjunction with the variations of the structural modal parameters, are used to determine the variations of the stiffness reduction factors. These values will subsequently be used to estimate statistics for the corrected stiffness parameters. The effectiveness of the proposed technique is illustrated using simulated data on an aluminum cantilever Euler-Bernoulli beam.

Delamination detection with error and noise polluted natural frequencies using computational intelligence concepts

Damage within a structure refers to changes in both its geometric and material characteristics, resulting in a drop in the stiffness that impacts the structure’s performance adversely. This decrease in stiffness causes alterations in modal parameters, including natural frequencies and mode shapes. Utilizing modal analysis allows for the extraction of modal frequencies and mode shapes, facilitating the analysis of mode shape curvature to detect structural damage. In recent years, artificial neural networks (ANNs) have achieved significant application, mainly for their exceptional capability in pattern recognition, which proves invaluable for identifying structural damage. This article proposes a novel method based on mode shape curvature and ANNs for detecting damage in beam-like structures. Experimental study is conducted to analysis damaged and undamaged structural modal behaviours. A feed-forward neural network with two hidden layers, trained on damage indices from mode shape data, is used to accurately pinpoint damage locations within the structure. The proposed approach for damage detection is validated and proves its ability to precisely pinpoint the location of damage. The results of this study demonstrate that ANNs trained with modal curvatures hold significant promise for identifying structural damage, enabling early detection in beam-like structures and contributing to ensuring their safe operation.

In vibration-based damage detection, it is fundamentally important to distinguish between changes in the dynamics of a structure and those related to environmental effects. Modal properties of structures are widely used as damage-sensitive features and indicate changes in physical properties due to structural damage or degradation. However, civil structures such as bridges are influenced by various operational and environmental conditions (such as traffic, wind, humidity, and, most importantly, temperature), and these variables also alter the modal properties of structures. Changes in modal properties caused by environmental effects can mask the effect of structural damage, resulting in unreliable structural damage assessments. It is thus fundamentally important to understand the relationship between environmental effects and modal properties. In this paper, a full-scale finite element (FE) model is used in a simulation-based study to investigate the effects of temperature distribution on natural frequencies. Two approaches for modeling the temperature effects on natural frequencies (thermal prestress imposed at an element level and temperature-dependent Young's modulus) are implemented in the FE model, and results are compared. Then, temperature profiles of the model are hypothetically simulated. These simulation results are used to develop a linear model and the three most popular machine learning models to reproduce the effects of temperature distribution on natural frequencies. Finally, a comprehensive comparison is made between the performances of these data-driven models.

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.

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

Structural damage identification using static test data and changes in frequencies

Fundamental mode shape and its derivatives in structural damage localization

This paper reports on the potential of using the impact resonance method (IRM) for detecting fatigue damage in strengthened reinforced concrete (RC) beams. In this experimental program, 1.2 m long RC beams strengthened with a carbon fibre reinforced polymer (CFRP) plate have been employed. The specimens were subjected to fatigue loading under four-point bending for up to 2 × 106 cycles at 3 Hz. The load amplitude was varying from 15% to 75% of the cycles yielding load of the beam. Throughout fatigue testing, the cycling was stopped for IRM measurements to be taken. The obtained data provided information about changes in modal properties, such as, fundamental frequencies and damping ratios. Moreover, the results have shown that the IRM technique was successfully employed in laboratory for detecting fatigue damage in concrete beams strengthened with CFRP laminates.Key words: impact resonance method, modal properties, RC beam, FRP-strengthening, fatigue test, cracking.

Large scale tests with progressive damage on a prestressed concrete highway bridge have been performed to investigate the sensitivity of several damage detection, localization, and quantification methods based on modal parameters. To investigate the quality of modal parameters, the data set of one damage step was analyzed by several output-only identification techniques. Although the bridge was severely cracked, natural frequencies as well as mode shapes display only minor changes. However, the relative changes of mode shapes are larger than those observed for natural frequencies. A novel damage indicator, called mode shape area index, based on changes of mode shapes, has been developed and found as the most sensitive damage detection approach. Damage detection or localization via changes of the flexibility matrix performed better than natural frequencies or mode shapes alone. The application of the direct stiffness calculation and a sensitivity-based model update technique showed results having a high level of ambiguity about the location and quantification of damage also at the highest damage level. Evaluating the information collected in this study the test results indicate that an early stage damage identification in prestressed concrete bridges is hardly possible because of the nearly complete recovery of stiffness after closing of cracks in prestressed concrete and the effect of environmental parameters on modal data.

Modeling and analysis of a cracked composite cantilever beam vibrating in coupled bending and torsion

This paper uses dynamic computer simulation techniques to develop and apply a multi-criteria procedure using non-destructive vibration-based parameters for damage assessment in truss bridges. In addition to changes in natural frequencies, this procedure incorporates two parameters, namely the modal flexibility and the modal strain energy. Using the numerically simulated modal data obtained through finite element analysis of the healthy and damaged bridge models, algorithms based on modal flexibility and modal strain energy changes before and after damage are obtained and used as the indices for the assessment of structural health state. The application of the two proposed parameters to truss-type structures is limited in the literature. The proposed multi-criteria based damage assessment procedure is therefore developed and applied to truss bridges. The application of the approach is demonstrated through numerical simulation studies of a single-span simply supported truss bridge with eight damage scenarios corresponding to different types of deck and truss damage. Results show that the proposed multi-criteria method is effective in damage assessment in this type of bridge superstructure.

Sensitivity Of Bonded And Composite Beams

Experiments were performed to elucidate the balance of energies involved in the formation of red blood cell (RBC) aggregates and in their disaggregation. In order to achieve a mean stable rouleau formation, the aggregating energy provided by macromolecular binding to the cell membrane must overcome the disaggregation energy of electrostatic repulsion between RBC surfaces and the effects of mechanical shear stress. In a quiescent suspension the net aggregation energy is largely stored in the membrane as a change in strain energy. The alterations in strain energy cause the curvature of the end cells in rouleaux of normal RBCs in Dx 80 to change from concave to convex and back again to concave as [Dx 80] was increased from 1 to 4 to 6 g/dl; computation of net aggregation energy per unit area (gamma) from changes in membrane strain energy yielded values on the order of 10(3) ergs/cm2. The end cells of neuraminidase-treated RBCs remained convex with [Dx 80] above 2 g/dl, and gamma is probably on the order of 10(2) ergs/cm2. The variations in gamma with [Dx 80] and RBC surface charge are similar to variations in reflectometric aggregation index without shear ( RAI0 ), indicating that RAI0 reflects gamma. The difference in gamma between normal and neuraminidase-treated RBCs represents the electrostatic repulsive energy, the magnitude of which varied inversely with dextran molecular size and directly with [Dx]. Moderate shearing in the reflectometer enhanced RBC aggregation by promoting cell-cell encounter, but high shear stresses cause RBC disaggregation. The energy required to disaggregate a unit interacting area of normal RBCs in Dx 80 in a flow channel is on the order of 10(4) ergs/cm2, which is much lower than gamma. These results suggest that the release of the stored membrane strain energy during disaggregation aids in the separation process. The results show that the understanding of RBC aggregation requires the considerations of surface charge, properties of aggregating agents, and the rheology of the cell membrane.