961 resultados para Structural damage detection


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This paper discusses the application of a damage detection methodology to monitor the location and extent of partial structural damage. The methodology combines, in an iterative way, the model updating technique based on frequency response functions (FRF) with monitoring data aiming at identifying the damage area of the structure. After the updating procedure reaches a good correlation between the models, it compares the parameters of the damage structure with those of the undamaged one to find the deteriorated area. The influence of the FEM mesh size on the evaluation of the extent of the damage has also been discussed. The methodology is applied using real experimental data from a spatial frame structure.

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Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.

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Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the. monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are. many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure. Copyright © 2007 by ABCM.

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Structural Health Monitoring (SHM) denotes a system with the ability to detect and interpret adverse changes in structures in order to improve reliability and reduce life-cycle costs. The greatest challenge for designing a SHM system is knowing what changes to look for and how to classify them. Different approaches for SHM have been proposed for damage identification, each one with advantages and drawbacks. This paper presents a methodology for improvement in vibration signal analysis using statistics information involving the probability density. Generally, the presence of noises in input and output signals results in false alarms, then, it is important that the methodology can minimize this problem. In this paper, the proposed approach is experimentally tested in a flexible plate using a piezoelectric (PZT) actuator to provide the disturbance.

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This paper describes an image compounding technique based on the use of different apodization functions, the evaluation of the signals phases and information from the interaction of different propagation modes of Lamb waves with defects for enhanced damage detection, resolution and contrast. A 16 elements linear array is attached to a 1 mm thickness isotropic aluminum plate with artificial defects. The array can excite the fundamental A0 and S0 modes at the frequencies of 100 kHz and 360 kHz, respectively. For each mode two synthetic aperture (SA) images with uniform and Blackman apodization and one image of Coherence Factor Map (CFM) are obtained. The specific interaction between each propagation mode and the defects and the characteristics of acoustic radiation patterns due to different apodization functions result in images with different resolution and contrast. From the phase information one of the SA images is selected at each pixel to compound the final image. The SA images are multiplied by the CFM image to improve contrast and for the dispersive A0 mode it is used a technique for dispersion compensation. There is a contrast improvement of 47.5 dB, reducing the dead zone and improving resolution and damage detection. © 2012 IEEE.

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Structural damage identification is basically a nonlinear phenomenon; however, nonlinear procedures are not used currently in practical applications due to the complexity and difficulty for implementation of such techniques. Therefore, the development of techniques that consider the nonlinear behavior of structures for damage detection is a research of major importance since nonlinear dynamical effects can be erroneously treated as damage in the structure by classical metrics. This paper proposes the discrete-time Volterra series for modeling the nonlinear convolution between the input and output signals in a benchmark nonlinear system. The prediction error of the model in an unknown structural condition is compared with the values of the reference structure in healthy condition for evaluating the method of damage detection. Since the Volterra series separate the response of the system in linear and nonlinear contributions, these indexes are used to show the importance of considering the nonlinear behavior of the structure. The paper concludes pointing out the main advantages and drawbacks of this damage detection methodology. © (2013) Trans Tech Publications.

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This paper presents a novel time domain approach for Structural Health Monitoring (SHM) systems based on Electromechanical Impedance (EMI) principle and Principal Component Coefficients (PCC), also known as loadings. Differently of typical applications of EMI applied to SHM, which are based on computing the Frequency Response Function (FRF), in this work the procedure is based on the EMI principle but all analysis is conducted directly in time-domain. For this, the PCC are computed from the time response of PZT (Lead Zirconate Titanate) transducers bonded to the monitored structure, which act as actuator and sensor at the same time. The procedure is carried out exciting the PZT transducers using a wide band chirp signal and getting their time responses. The PCC are obtained in both healthy and damaged conditions and used to compute statistics indexes. Tests were carried out on an aircraft aluminum plate and the results have demonstrated the effectiveness of the proposed method making it an excellent approach for SHM applications. Finally, the results using EMI signals in both frequency and time responses are obtained and compared. © The Society for Experimental Mechanics 2014.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Sudden changes in the stiffness of a structure are often indicators of structural damage. Detection of such sudden stiffness change from the vibrations of structures is important for Structural Health Monitoring (SHM) and damage detection. Non-contact measurement of these vibrations is a quick and efficient way for successful detection of sudden stiffness change of a structure. In this paper, we demonstrate the capability of Laser Doppler Vibrometry to detect sudden stiffness change in a Single Degree Of Freedom (SDOF) oscillator within a laboratory environment. The dynamic response of the SDOF system was measured using a Polytec RSV-150 Remote Sensing Vibrometer. This instrument employs Laser Doppler Vibrometry for measuring dynamic response. Additionally, the vibration response of the SDOF system was measured through a MicroStrain G-Link Wireless Accelerometer mounted on the SDOF system. The stiffness of the SDOF system was experimentally determined through calibrated linear springs. The sudden change of stiffness was simulated by introducing the failure of a spring at a certain instant in time during a given period of forced vibration. The forced vibration on the SDOF system was in the form of a white noise input. The sudden change in stiffness was successfully detected through the measurements using Laser Doppler Vibrometry. This detection from optically obtained data was compared with a detection using data obtained from the wireless accelerometer. The potential of this technique is deemed important for a wide range of applications. The method is observed to be particularly suitable for rapid damage detection and health monitoring of structures under a model-free condition or where information related to the structure is not sufficient.

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A field experiment was conducted on a real continuous steel Gerber-truss bridge with artificial damage applied. This article summarizes the results of the experiment for bridge damage detection utilizing traffic-induced vibrations. It investigates the sensitivities of a number of quantities to bridge damage including the identified modal parameters and their statistical patterns, Nair’s damage indicator and its statistical pattern and different sets of measurement points. The modal parameters are identified by autoregressive time-series models. The decision on bridge health condition is made and the sensitivity of variables is evaluated with the aid of the Mahalanobis–Taguchi system, a multivariate pattern recognition tool. Several observations are made as follows. For the modal parameters, although bridge damage detection can be achieved by performing Mahalanobis–Taguchi system on certain modal parameters of certain sets of measurement points, difficulties were faced in subjective selection of meaningful bridge modes and low sensitivity of the statistical pattern of the modal parameters to damage. For Nair’s damage indicator, bridge damage detection could be achieved by performing Mahalanobis–Taguchi system on Nair’s damage indicators of most sets of measurement points. As a damage indicator, Nair’s damage indicator was superior to the modal parameters. Three main advantages were observed: it does not require any subjective decision in calculating Nair’s damage indicator, thus potential human errors can be prevented and an automatic detection task can be achieved; its statistical pattern has high sensitivity to damage and, finally, it is flexible regarding the choice of sets of measurement points.

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Methamphetamine (METH) is a potent psychostimulant highly used worldwide. Recent studies evidenced the involvement of METH in the breakdown of the blood-brain-barrier (BBB) integrity leading to compromised function. The involvement of the matrix metalloproteinases (MMPs) in the degradation of the neurovascular matrix components and tight junctions (TJs) is one of the most recent findings in METH-induced toxicity. As BBB dysfunction is a pathological feature of many neurological conditions, unveiling new protective agents in this field is of major relevance. AcetylL-carnitine (ALC) has been described to protect the BBB function in different paradigms, but the mechanisms underling its action remain mostly unknown. Here, the immortalized bEnd.3 cell line was used to evaluate the neuroprotective features of ALC in METH-induced damage. Cells were exposed to ranging concentrations of METH, and the protective effect of ALC 1 mM was assessed 24 h after treatment. F-actin rearrangement, TJ expression and distribution, and MMPs activity were evaluated. Integrin-linked kinase (ILK) knockdown cells were used to assess role of ALC in ILK mediated METHtriggered MMPs’ activity. Our results show that METH led to disruption of the actin filaments concomitant with claudin-5 translocation to the cytoplasm. These events were mediated by MMP-9 activation in association with ILK overexpression. Pretreatment with ALC prevented METH-induced activation of MMP-9, preserving claudin-5 location and the structural arrangement of the actin filaments. The present results support the potential of ALC in preserving BBB integrity, highlighting ILK as a new target for the ALC therapeutic use.

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RESUMO: Enthesitis is the hallmark of spondyloarthritis (SpA), and is observed in all subtypes. Wide information on SpA abnormalities, including synovitis, tendinitis and enthesitis, can be efficiently perceived by Doppler ultrasound. Furthermore, several studies on imaging of enthesis showed that imaging techniques are better than clinical examination to detect enthesis alterations; and vascularized enthesitis detected by Doppler ultrasound appears to be a valuable diagnostic tool to confirm SpA diagnosis. However, data published until now concerning entheseal elementary alterations that characterize SpA enthesitis (enthesis inflammatory activity) or enthesopathy (permanent structural changes) reflect rather the authors’ empiric opinion than a methodological validation process. In this sense it seems crucial to identify elementary entheseal lesions associated with activity or damage, in order to improve monitoring and treatment response in SpA patients. The development of better assessment tools is today a challenge and a need in SpA. The first study of this thesis focused on the analysis of the reliability of inter-lector and inter-ultrasonography equipment of Madrid sonography enthesitis index (MASEI). Fundamental data for the remaining unrolling project validity. In the second and third studies we concerned about two entheseal elemental lesions: erosions and bursa. In literature erosions represent a permanent structural damage, being useful for monitoring joint injury, disease activity and therapeutic response in many rheumatic diseases; and to date, this concept has been mostly applied in rheumatoid arthritis (RA). Unquestionably, erosion is a tissue-related damage and a structural change. However, the hypothesis that we decided to test was if erosions represent a permanent structural change that can only grow and worsen over time, as occurs in RA, or a transitory alteration. A longitudinal study of early SpA patients was undertaken, and the Achilles enthesis was used as a model. Our results strongly suggested that previously detected erosions could disappear during the course of the disease, being consistent with the dynamic behavior of erosion over time. Based on these striking results it seems reasonable to suggest that the new-bone formation process in SpA could be associated with the resolution of cortical entheseal erosion over time. These results could also be in agreement with the apparent failure of anti-tumor necrosis factor (TNF) therapies to control bone proliferation in SpA; and with the relation of TNF-α, Dickkopf-related protein 1 (Dkk-1) and the regulatory molecule of the Wnt signaling pathway in the bone proliferation in SpA. In the same model, we then proceeded to study the enthesis bursa. Interestingly, the Outcome Measures in Rheumatology Clinical Trials (OMERACT) enthesopathy definition does not include bursa as an elementary entheseal lesion. Nonetheless, bursa was included in 46% of the enthesis studies in a recently systematic literature review, being in agreement with the concept of “synovio-entheseal complex” that includes the link between enthesitis and osteitis in SpA. It has been clarified in recent data that there is not only a close functional integration of the enthesis with the neighboring bone, but also a connection between enthesitis and synovitis. Therefore, we tried to assess the prevalence and relevance of the bursa-synovial lesion in SpA. Our findings showed a significant increase of Achilles bursa presence and thickness in SpA patients compared to controls (healthy/mechanical controls and RA controls). These results raise awareness to the need to improve the enthesopathy ultrasonographic definition. In the final work of this thesis, we have explored new perspectives, not previously reported, about construct validity of enthesis ultrasound as a possible activity outcome in SpA. We performed a longitudinal Achilles enthesis ultrasound study in patients with early SpA. Achilles ultrasound examinations were performed at baseline, six- and twelve-month time periods and compared with clinical outcome measures collected at basal visit. Our results showed that basal erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) are higher in patients with Doppler signal in enthesis, and even that higher basal ESR, CRP and Ankylosing Spondylitis Disease Activity Score (ASDAS) predicted a higher Doppler signal (an ultrasound alteration accepted as representative of inflammation) six months later. Patients with very high disease activity assessed by ASDAS (>3.5) at baseline had significantly higher Achilles total ultrasound score verified at the same time; and ASDAS <1.3 predicted no Doppler signal at six and twelve months. This seems to represent a connection between classical biomarkers and clinical outcomes associated with SpA activity and Doppler signal, not only at the same time, but also for the following months. Remarkably, patients with inactive disease (ASDAS < 1.3) at baseline had no Doppler signal at six and twelve months. These findings reinforce the potential use of ultrasound related techniques for disease progression assessment and prognosis purposes. Intriguingly, Ankylosing Spondylitis Disease Activity Index (BASDAI) didn’t show significant differences between different cut-offs concerning ultrasound lesions or Doppler signal, while verified with ASDAS. These results seem to indicate that ASDAS reflects better than BASDAI what happens in the enthesis. The work herein discussed clearly shows the potential utility of ultrasound in enthesis assessment in SpA patients, and can be important for the development of ultrasound activity and structural damage scores for diagnosis and monitoring purposes. Therefore, local promotion of this technique constitutes a medical intervention that is worth being tested in SpA patients for diagnosis, monitoring and prognosis purposes.

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The effects of a single dose (100 mg/kg-body weight of mouse) of oxamniquine on the worm's tegument and paranchyma in relation to the process of immunological granulomatous reaction of the host's liver are described under light and electron microscopy (EM). The lesions caused by the drug are sequentially and simultaneously described in form of swelling, surface bulble and disruption with erosions. Ulceration in the tubercules with loss of spines is often more extensive and severe in male worms and concentration of host's mononuclear cells is observed. The possible role of host's immune response is discussed.

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In this work, a previously-developed, statistical-based, damage-detection approach was validated for its ability to autonomously detect damage in bridges. The damage-detection approach uses statistical differences in the actual and predicted behavior of the bridge caused under a subset of ambient trucks. The predicted behavior is derived from a statistics-based model trained with field data from the undamaged bridge (not a finite element model). The differences between actual and predicted responses, called residuals, are then used to construct control charts, which compare undamaged and damaged structure data. Validation of the damage-detection approach was achieved by using sacrificial specimens that were mounted to the bridge and exposed to ambient traffic loads and which simulated actual damage-sensitive locations. Different damage types and levels were introduced to the sacrificial specimens to study the sensitivity and applicability. The damage-detection algorithm was able to identify damage, but it also had a high false-positive rate. An evaluation of the sub-components of the damage-detection methodology and methods was completed for the purpose of improving the approach. Several of the underlying assumptions within the algorithm were being violated, which was the source of the false-positives. Furthermore, the lack of an automatic evaluation process was thought to potentially be an impediment to widespread use. Recommendations for the improvement of the methodology were developed and preliminarily evaluated. These recommendations are believed to improve the efficacy of the damage-detection approach.

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The objective of this work was to develop a low-cost portable damage detection tool to assess and predict damage areas in highway bridges. The proposed tool was based on standard vibration-based damage identification (VBDI) techniques but was extended to a new approach based on operational traffic load. The methodology was tested using numerical simulations, laboratory experiments, and field testing.