Abdollah Bagheri

Reference-free damage detection by means of wavelet transform and empirical mode decomposition applied to Lamb waves

Guided ultrasonic waves are increasingly used in all those structural health monitoring applications that benefit from built-in transduction, moderately large inspection ranges, and high sensitivity to small flaws. This article describes a monitoring system based on the generation and detection of the guided ultrasonic waves from an array of sparse transducers. In a round-robin manner, ultrasonic waves are generated and measured from all possible different pairs of excitation and sensing transducers. The ultrasonic signals are then processed using continuous wavelet transform and empirical mode decomposition to extract few damage-sensitive features that enable the detection and localization of damage. With respect to most of the existing guided ultrasonic wave–based methods, the proposed approach does not require to record data from a pristine structure (baseline data), and damage is inferred by examining the selected features obtained from all the possible combinations of actuator–sensor pairs of the array. In this study, the method is validated using commercial finite element software to model the presence of 10 ultrasonic transducers bonded onto an aluminum plate. The results are promising and ongoing studies are focusing on the experimental validation and the application to other waveguides.

Structural damage detection based on incomplete modal data using pattern search algorithm

Incomplete sensed data in structures have made exact structural damage detection a serious challenge. In this paper, an effective method is presented for damage detection and estimation in structures based on incomplete modal data of a damaged structure via a pattern search algorithm. An objective function based on the condensed mass and stiffness matrices is formulated. The proposed method determines the damage to structural elements using optimization of the objective function by using pattern search algorithm. The performance of the presented method has been verified through two numerical examples, namely, a two-span continuous beam and a three-story plane frame with and without noise in the modal data containing several damages. Also, the effect of the discrepancy in mass and stiffness between the finite-element model and the actual tested dynamic system has been investigated. Furthermore, the experimental data from the vibration test of a mass–stiffness system are used for verification of the proposed approach. The results show that the presented method is sensitive to the location and severity of structural damage in spite of the incomplete modal data.

Damage prognosis by means of modal residual force and static deflections obtained by modal flexibility based on the diagonalization method

In this paper, two effective damage detection methods for localizing and quantifying structural damage in shear frames are presented. Both of them are based on the diagonalization of the stiffness matrix of shear frames. The first method is devoted to estimating structural damage by the modification of modal residual force while the second method is based on the computation of the static displacements under a unique static force by using only a few modes. The most important feature of the presented methods is their simplicity in the computation of damage. The proposed algorithms have been numerically applied to two shear frames to show that they can successfully detect and quantify damage in these structures. Also, the efficiency of the presented methods has been demonstrated through damage simulations when the modal data have been contaminated with noise. Finally, the applicability of the presented methods has been demonstrated by studying a five-story shear frame on a shaking table. All of the obtained results emphasize the robustness and good performance of the presented methods in the damage diagnosis of shear frames. (Some figures may appear in colour only in the online journal)

A flexibility-based method via the iterated improved reduction system and the cuckoo optimization algorithm for damage quantification with limited sensors

In this paper, a novel and effective damage diagnosis algorithm is proposed to localize and quantify structural damage using incomplete modal data, considering the existence of some limitations in the number of attached sensors on structures. The damage detection problem is formulated as an optimization problem by computing static displacements in the reduced model of a structure subjected to a unique static load. The static responses are computed through the flexibility matrix of the damaged structure obtained based on the incomplete modal data of the structure. In the algorithm, an iterated improved reduction system method is applied to prepare an accurate reduced model of a structure. The optimization problem is solved via a new evolutionary optimization algorithm called the cuckoo optimization algorithm. The efficiency and robustness of the presented method are demonstrated through three numerical examples. Moreover, the efficiency of the method is verified by an experimental study of a five-story shear building structure on a shaking table considering only two sensors. The obtained damage identification results for the numerical and experimental studies show the suitable and stable performance of the proposed damage identification method for structures with limited sensors.

Determination of the Neutral Temperature of Slender Beams by Using Nonlinear Solitary Waves

AbstractSlender columns subjected to compressive stress are common in many civil structures. The rapid in situ measurement of this stress may prevent structural buckling. In this study, the authors applied an artificial neural network (ANN) to process numerical data that describe the coupling mechanism between highly nonlinear solitary waves (HNSWs) propagating along a granular system and a beam in contact with the granular medium. The aim is to evaluate the ability of HNSWs to measure stress in thermally loaded structures and to estimate the neutral temperature, i.e., the temperature at which the stress is null. Nonlinear solitary waves are compact nondispersive waves that can form and travel in nonlinear systems such as one-dimensional chains of particles, where they are conventionally generated by the mechanical impact of a striker. The authors numerically investigated a straight chain of spherical particles in contact with a prismatic beam subjected to thermal stress. The effect of the neutral tempera...

Guided Ultrasonic Wave Imaging for Immersed Plates Based on Wavelet Transform and Probabilistic Analysis

We propose a nondestructive evaluation method for immersed structures based on the propagation of ultrasonic waves induced by means of laser pulses and detected with an array of immersion transducers. In the study presented in this article, a laser operating at 532 nm is employed to excite leaky guided waves on an aluminum plate immersed in water. An array of immersion transducers is used to record the waves radiating from the laser-illuminated point. The detected signals are processed with an imaging algorithm based on continuous wavelet transform and probabilistic analysis to localize the presence of artificial defects machined in the plate. With respect to the existing imaging methods for plates, the proposed algorithm is pseudo baseline free, because it does not require data recorded from a pristine plate, but it requires that only a portion of the plate is free from defects. We find that the proposed algorithm enables the detection of surface cracks. Advantages and limitations of the algorithm for the nondestructive evaluation of underwater structures are discussed.

Empirical mode decomposition and neural network for the classification of electroretinographic data.

Abstract The processing of biosignals is increasingly being utilized in ambulatory situations in order to extract significant signals’ features that can help in clinical diagnosis. However, this task is hampered by the fact that biomedical signals exhibit a complex behavior characterized by strong nonlinear and non-stationary properties that cannot always be perceived by simple visual examination. New processing methods need be considered. In this context, we propose a signal processing method, based on empirical mode decomposition and artificial neural networks, to analyze electroretinograms, i.e., the retinal response to a light flash, with the aim to detect and classify retinal diseases. The present application focuses on two retinal pathologies: achromatopsia, which is a cone disease, and congenital stationary night blindness, which affects the photoreceptoral signal transmission. The results indicate that, under suitable conditions, the method proposed here has the potential to provide a powerful tool for routine clinical examinations, since it is able to recognize with high level of confidence the eventual presence of one of the two pathologies.

GENERATION OF UNIFORM HAZARD EARTHQUAKE ACCELEROGRAMS AND NEAR-FIELD GROUND MOTIONS

The sets of records developed for the SAC Steel Project are classified according to the level of seismic hazard and specific geographic region, and have been used extensively for structural response and seismic hazard analyses. This study presents a parametric analysis of these record data sets for generation of uniform hazard earthquake and near-field records. The record parameters define far-field characteristics such as power spectral density and envelope function, and near-field effects such as acceleration pulse, power spectral density and envelope function. The proposed method for generation of near-field records uses the decomposing capabilities of wavelet transform on earthquake records. A set of uniform hazard earthquake accelerograms and near-field ground motions is generated based on the record parameters. The generated uniform hazard earthquake accelerograms representing a uniform level of seismic hazard for a particular geographic region involves seismic hazard studies, calibrated attenuation relationships, and local site amplification models. In order to assess the reliability and efficiency of the presented method, the statistical response spectra obtained from the generated accelerograms have been compared with those from the actual records. The obtained results showed that there is a good compatibility between the response spectra of the generated and actual records in the most of the frequencies.

Signal processing for the inspection of immersed structures

In this paper, we present a non-destructive inspection method for immersed waveguide. A laser operating at 532 nm is used to excite leaky guided waves on an aluminum plate immersed in water. The plate has a few artificial defects. An array of immersion transducers is used to detect the propagating waves. A signal processing based on continuous wavelet transform is utilized to extract a few damage-sensitive features that are used in an outlier analysis and in a probabilistic-based imaging method. The experimental results show that the proposed system can be used for the inspection of underwater waveguides.

Structural Stiffness Identification of Skewed Slab Bridges with Limited Information for Load Rating Purpose

This paper presents a method for identifying structural stiffness of skewed reinforced concrete slab bridges with limited structural information using measured acceleration data. This information might be used for nondestructive evaluation, condition assessment, and load rating of bridges. A large number of slab bridges with different structural dimensions such as skew angle, span, width, and thickness was first analyzed using finite element method to obtain their first modal frequency. This population of data was then used to create an artificial neural network, which can predict a coefficient that plays an important role in identifying the flexural rigidity of slab bridges. This approach was applied to estimate the flexural rigidity of a highly skewed reinforced concrete slab bridge in the state of Virginia for load rating purpose. The bridge was instrumented with wireless accelerometers, and the vibration responses of the bridge under ambient loading and impact hammer test were recorded. An algorithm based on the variational mode decomposition was employed to identify modal properties of the bridge. Then, the flexural rigidity of bridge was computed from the established relationship between the first natural frequency and the flexural rigidity of bridge. Results show that the proposed method is capable of predicting structural stiffness, and can be used for load rating of bridges without structural information.