Multisensor Aggregation Algorithms for Structural Damage Diagnosis Based on a Substructure Concept

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.

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

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

A damage detection method using BP neural network based on a novel damage index, the correlation characteristic of the acceleration response, is proposed, and is evaluated through the FEM simulation and experiment verification. On the basis of achievements in existence, the feasibility of using the correlation characteristic as damage index is validated theoretically. The damage detection for a simple-supported beam using the proposed method was FEM simulated. The results showed that the trained BP neural network can correctly detect the location and extent of damages in both single damage case and multi-damage case. A model test of a reinforced concrete simple-supported beam was performed to verify the validity and efficiency of the damage detection method. From the results of the model test, it is shown that the trained BP neural network can correctly locate the damage mostly detect the extent of damage. A conclusion is given that the novel damage detection method using the correlation characteristic of the acceleration response as damage index is feasible and efficient.

Bayesian‐optimized unsupervised learning approach for structural damage detection

Distributed long gauge strain sensing technology has solved the problem of difficult identification of local damage in traditional ‘point’ monitoring, and has received extensive attention in the field of structural damage identification. Owing to the inevitable presence of measurement noise and environmental factors in the macro strain response measurement, a single damage index has also underlined some drawbacks generally arising when multiple damages occur, or errors affect the identified dynamic properties of the systems. To address these challenges, this paper proposes a data fusion method based on the Dempster–Shafer evidence theory, relying on distributed strain sensing technology. The identification results of the modal macro strain-based index and quasi-static macro strain energy-based damage index are fused to make a comprehensive decision on structural damage location. Damage identification studies are conducted on different types of structures under impact loads and random wind loads to verify the effectiveness and accuracy of the proposed data fusion method in the case of single and multiple damage conditions. The results show that the proposed data fusion method can accurately identify the damage location and effectively reduce misjudgment on undamaged locations; it has potential application value in practical structural health monitoring.

The frequency-shift-based scheme is one of the popular structural damage detectionmethods used in the field. Assuming that the damage will cause structural parameter(e.g. mass or stiffness) changes, one can detect damage by observing natural frequencyvariations before and after damage occurs. One of the major limitations of thefrequency-shift-based damage detection approach is that the natural frequencies could beinsensitive to local and small parameter variations (damages) in the structure. To addressthis problem, the concept of sensitivity-enhancing control has been developed andexplored by researchers. Recently, an eigenstructure (dual eigenvalue–eigenvector)assignment-based approach has been proposed to advance the state of the artof sensitivity-enhancing control design for multiple degrees-of-freedom systems(Jiang et al 2007 Trans. ASME, J. Vib. Acoust. 129 771–83). While promisingfeatures have been shown, the approach has not been experimentally verified. Inaddition, such a scheme requires an accurate analytical model, which is in generaldifficult to obtain for complex structures. The purpose of this research is to advancethe state of the art by exploring an experimental-based sensitivity-enhancingcontrol approach for structural damage detection. That is, we aim at utilizingmathematical models identified from experimental measurement data for controllerdesign and system analysis, and hence eliminate the requirement of an accurateanalytical model. With the identified model, an eigenstructure assignment-basedoptimization scheme is utilized to design the closed-loop control to best enhance thenatural frequency sensitivity to structural damages. An experimental investigationbased on a test bed beam structure is conducted to demonstrate and verify theproposed method. Sensitivity-enhancing controls are designed to increase thefirst two natural frequency sensitivities to mass changes in the beam structure.Experimental results show that by applying the designed sensitivity-enhancing controls tothe beam structure, the natural frequency sensitivities to mass variations canbe significantly enhanced. Results also show that the damage locations can besuccessfully identified and the damage severity can be predicted by using the measuredfrequency changes of the closed-loop systems before and after damage occurrence.

Taking an underwater shell structure as engineering background, the Improved Genetic Algorithms (IGAs) is applied to detect the structural damage by static response data acquisition in the paper. Firstly, region water-level and finite point strains of the underwater shell structure are measured. Then, the Genetic Algorithms (GAs) are optimized. Finally, the structural damage location and degree are identified by comparison between measurement strains and calculation ones. When the measured strains are approached most close to the one among all the calculation strains, the structural damage condition is determined. In order to diagnose the large-scale structural damage by GAs, the decoding the damage location and damage index at same time is proposed, as well as, fine-tuning of variables and niche technique are also introduced to improve the convergence efficiency of GAs.

The state of a structure’s health can be determined by investigating its vibration characteristics. Vibration-based structural health monitoring (SHM) enables early detection and diagnosis of damage as well as extension of service life. Herein, linear and nonlinear tracking metrics are proposed to track deterioration in the condition of multistory structures and assess their instantaneous health in real-time via measurement of floor accelerations. The linear metrics, the amplitude damage index ADIn(t¯) and the frequency damage index FDIn(t¯), are based on tracking the power spectra of floor accelerations. The nonlinear metric e(t¯) is based on a novel implementation of the phase-space warping method and obtained from the orbits representing the floors’ motions in pseudo phase-space. A scaled-down model of a four-floor moment-resisting frame building is designed and fabricated to demonstrate and to compare the capabilities of the three damage indices. Structural damage is introduced to individual columns, to mimic damage initiation, by cutting two notches on opposite sides of a column cross-section at midheight. The ADIn(t¯) detected large damage events, fast deterioration beyond them, and the onset of failure using any of the four floor accelerations. The FDIn(t¯) proved insensitive to damage compared with the other two metrics. The nonlinear metric e(t¯) detected gradual (fatigue-induced) deterioration in the building’s health before introduction of damage, large damage events, fast deterioration beyond them, and the onset of failure, using any of the four floor accelerations. The e(t¯) metric varied slowly and continuously with gradual deterioration and exhibited larger discontinuous jumps with discrete damage events. This was true for all three damage experiments undertaken on the model building. The nonlinear e(t¯) metric was also found to be more efficient, in terms of signal utilization, in comparison with the linear ADIn(t¯) metric.

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.

Technique with the capability of detecting and localizing damage of structures using naturally operating environments can provide a possibility of developing more efficient and simpler structural health monitoring systems. This passive sensing technique would eliminate the need of active actuation which requires power either from battery or ambients to generate controlled excitation source. In a recent study, self-Green’s functions (GF) were reconstructed using auto-correlation (AC), combined with a damage index by comparing the differences in GFs between damaged and pristine metallic panels to locate the damage. In this paper, random decrement (RD) technique is proposed to reconstruct GF with computational efficiency. While the RD has been widely used for damage detection and structure parameter extraction in civil structures, in the frequency usually below 1 kHz; this study explores using RD up to 15 kHz for transient wave reconstruction and then damage localization. The concept is first validated through simulation for a plate structure, and the results show that the reconstructed self-Green’s function match well with the one from the auto-correlation technique after approximately 10,000 averages of the RD signatures.

Damage detection for beam-like structures using the normalized curvature of a uniform load surface

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

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.

In situ damage detection and localization using real acceleration structural health monitoring technique are the main idea of this study. The statistical and model identification time series, the response spectra, and the power density of the frequency domain are used to detect the behavior of Yonghe cable-stayed bridge during the healthy and damage states. The benchmark problem is used to detect the damage localization of the bridge during its working time. The assessment of the structural health monitoring and damage analysis concluded that (1) the kurtosis statistical moment can be used as an indicator for damage especially with increasing its percentage of change as the damage should occur; (2) the percentage of change of the Kernel density probability for the model identification error estimation can detect and localize the damage; (3) the simplified spectrum of the acceleration-displacement responses and frequencies probability changes are good tools for detection and localization of the one-line bridge damage.