896 resultados para Structural health monitoring


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This research has successfully developed a novel synthetic structural health monitoring system model that is cost-effective and flexible in sensing and data acquisition; and robust in the structural safety evaluation aspect for the purpose of long-term and frequent monitoring of large-scale civil infrastructure during their service lives. Not only did it establish a real-world structural monitoring test-bed right at the heart of QUT Gardens Point Campus but it can also facilitate reliable and prompt protection for any built infrastructure system as well as the user community involved.

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Acoustic emission technique has become a significant and powerful structural health monitoring tool for structures. Researches to date have been done on crack location, fatigue crack propagation in materials and severity assessment of failure using acoustic emission technique. Determining severity of failure in steel structures using acoustic emission technique is still a challenge to accurately determine the relationship between the severity of crack propagation and acoustic emission activities. In this study three point bending test on low carbon steel samples along with acoustic emission technique have been used to determine crack propagation and severity. A notch is introduced at the tension face of the loading point to the samples to initiate the crack. The results show that the percentage of load drop of the steel specimen has a reciprocal relationship with the crack opening i.e. crack opening zones are influenced by the loading rate. In post yielding region, common acoustic emission signal parameters such as, signal strength, energy and amplitudes are found to be higher than those at pre-yielding and at yielding.

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Structural Health Monitoring (SHM) schemes are useful for proper management of the performance of structures and for preventing their catastrophic failures. Vibration based SHM schemes has gained popularity during the past two decades resulting in significant research. It is hence evitable that future SHM schemes will include robust and automated vibration based damage assessment techniques (VBDAT) to detect, localize and quantify damage. In this context, the Damage Index (DI) method which is classified as non-model or output based VBDAT, has the ability to automate the damage assessment process without using a computer or numerical model along with actual measurements. Although damage assessment using DI methods have been able to achieve reasonable success for structures made of homogeneous materials such as steel, the same success level has not been reported with respect to Reinforced Concrete (RC) structures. The complexity of flexural cracks is claimed to be the main reason to hinder the applicability of existing DI methods in RC structures. Past research also indicates that use of a constant baseline throughout the damage assessment process undermines the potential of the Modal Strain Energy based Damage Index (MSEDI). To address this situation, this paper presents a novel method that has been developed as part of a comprehensive research project carried out at Queensland University of Technology, Brisbane, Australia. This novel process, referred to as the baseline updating method, continuously updates the baseline and systematically tracks both crack formation and propagation with the ability to automate the damage assessment process using output only data. The proposed method is illustrated through examples and the results demonstrate the capability of the method to achieve the desired outcomes.

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As the society matures, there was an increasing pressure to preserve historic buildings. The economic cost in maintaining these important heritage legacies has become the prime consideration of every state. Dedicated intelligent monitoring systems supplementing the traditional building inspections will enable the stakeholder to carry out not only timely reactive response but also plan the maintenance in a more vigilant approach; thus, preventing further degradation which was very costly and difficult to address if neglected. The application of the intelligent structural health monitoring system in this case studies of ‘modern heritage’ buildings is on its infancy but it is an innovative approach in building maintenance. ‘Modern heritage’ buildings were the product of technological change and were made of synthetic materials such as reinforced concrete and steel. Architectural buildings that was very common in Oceania and The Pacific. Engineering problems that arose from this type of building calls for immediate engineering solution since the deterioration rate is exponential. The application of this newly emerging monitoring system will improve the traditional maintenance system on heritage conservation. Savings in time and resources can be achieved if only pathological results were on hand. This case study will validate that approach. This publication will serve as a position paper to the on-going research regarding application of (Structural Health Monitoring) SHM systems to heritage buildings in Brisbane, Australia. It will be investigated with the application of the SHM systems and devices to validate the integrity of the recent structural restoration of the newly re-strengthened heritage building, the Brisbane City Hall.

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It is important to develop reliable finite element models for real structures not only in the design phase but also for the structural health monitoring and structural maintenance purposes. This paper describes the experience of the authors in using ambient vibration model identification techniques together with model updating tools to develop reliable finite element models of real civil engineering structures. Case studies of two real structures are presented in this paper. One is a 10 storey concrete building which is considered as a non-slender structure with complex boundary conditions. The other is a single span concrete foot bridge which is also a relatively inflexible planar structure with complex boundary conditions. Both structures are located at the Queensland University of Technology (QUT) and equipped with continuous structural health monitoring systems.

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Mode indicator functions (MIFs) are used in modal testing and analysis as a means of identifying modes of vibration, often as a precursor to modal parameter estimation. Various methods have been developed since the MIF was introduced four decades ago. These methods are quite useful in assisting the analyst to identify genuine modes and, in the case of the complex mode indicator function, have even been developed into modal parameter estimation techniques. Although the various MIFs are able to indicate the existence of a mode, they do not provide the analyst with any descriptive information about the mode. This paper uses the simple summation type of MIF to develop five averaged and normalised MIFs that will provide the analyst with enough information to identify whether a mode is longitudinal, vertical, lateral or torsional. The first three functions, termed directional MIFs, have been noted in the literature in one form or another; however, this paper introduces a new twist on the MIF by introducing two MIFs, termed torsional MIFs, that can be used by the analyst to identify torsional modes and, moreover, can assist in determining whether the mode is of a pure torsion or sway type (i.e., having a rigid cross-section) or a distorted twisting type. The directional and torsional MIFs are tested on a finite element model based simulation of an experimental modal test using an impact hammer. Results indicate that the directional and torsional MIFs are indeed useful in assisting the analyst to identify whether a mode is longitudinal, vertical, lateral, sway, or torsion.

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Modal flexibility is a widely accepted technique to detect structural damage using vibration characteristics. Its application to detect damage in long span large diameter cables such as those used in suspension bridge main cables has not received much attention. This paper uses the modal flexibility method incorporating two damage indices (DIs) based on lateral and vertical modes to localize damage in such cables. The competency of those DIs in damage detection is tested by the numerically obtained vibration characteristics of a suspended cable in both intact and damaged states. Three single damage cases and one multiple damage case are considered. The impact of random measurement noise in the modal data on the damage localization capability of these two DIs is next examined. Long span large diameter cables are characterized by the two critical cable parameters named bending stiffness and sag-extensibility. The influence of these parameters in the damage localization capability of the two DIs is evaluated by a parametric study with two single damage cases. Results confirm that the damage index based on lateral vibration modes has the ability to successfully detect and locate damage in suspended cables with 5% noise in modal data for a range of cable parameters. This simple approach therefore can be extended for timely damage detection in cables of suspension bridges and thereby enhance their service during their life spans.

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Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

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In order to assess the structural reliability of bridges, an accurate and cost effective Non-Destructive Evaluation (NDE) technology is required to ensure their safe and reliable operation. Over 60% of the Australian National Highway System is prestressed concrete (PSC) bridges according to the Bureau of Transport and Communication Economics (1997). Most of the in-service bridges are more than 30 years old and may experience a heavier traffic load than their original intended level. Use of Ultrasonic waves is continuously increasing for (NDE) and Structural Health Monitoring (SHM) in civil, aerospace, electrical, mechanical applications. Ultrasonic Lamb waves are becoming more popular for NDE because it can propagate long distance and reach hidden regions with less energy loses. The purpose of this study is to numerically quantify prestress force (PSF) of (PSC) beam using the fundamental theory of acoustic-elasticity. A three-dimension finite element modelling approach is set up to perform parametric studies in order to better understand how the lamb wave propagation in PSC beam is affected by changing in the PSF level. Results from acoustic-elastic measurement on prestressed beam are presented, showing the feasibility of the lamb wave for PSF evaluation in PSC bridges.

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A reduced 3D continuum model of dynamic piezoelectricity in a thin-film surface-bonded to the substrate/host is presented in this article. While employing large area flexible thin piezoelectric films for novel applications in device/diagnostics, the feasibility of the proposed model in sensing the surface and/or sub-surface defects is demonstrated through simulations - which involve metallic beams with cracks and composite beam with delaminations of various sizes. We have introduced a set of electrical measures to capture the severity of the damage in the existing structures. Characteristics of these electrical measures in terms of the potential difference and its spatial gradients are illustrated in the time domain. Sensitivity studies of the proposed measures in terms of the defected areas and their region of occurence relative to the sensing film are reported. The simulations' results for electrical measures for damaged hosts/substrates are compared with those due to undamaged hosts/substrates, which show monotonicity with high degree of sensitivity to variations in the damage parameters.

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Cracks in civil structures can result in premature failure due to material degradation and can result in both financial loss and environmental consequences. This thesis reports an effective technique using Acoustic Emission (AE) technique to assess the severity of the crack propagation in steel structures. The outcome of this work confirms that combination of AE parametric analysis and signal processing techniques can be used to evaluate crack propagation under different loading configurations. The technique has potential application to assess and monitor the condition of civil structures.

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Active Fiber Composites (AFC) possess desirable characteristics over a wide range of smart structure applications, such as vibration, shape and flow control as well as structural health monitoring. This type of material, capable of collocated actuation and sensing, call be used in smart structures with self-sensing circuits. This paper proposes four novel applications of AFC structures undergoing torsion: sensors and actuators shaped as strips and tubes; and concludes with a preliminary failure analysis. To enable this, a powerful mathematical technique, the Variational Asymptotic Method (VAM) was used to perform cross-sectional analyses of thin generally anisotropic AFC beams. The resulting closed form expressions have been utilized in the applications presented herein.

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The change in extension-twist Coupling due to delamination in antisymmetric laminates is experimentally measured. Experimental results are compared with the results from analytical expression existing in literature and finite element analysis. The application of the Macro-Fiber Composite (MFC) developed at the NASA Langley Research Center for sensing the delamination in the laminates is investigated. While many applications have been reported in the literature using the MFC as an actuator, here its use as a twist sensor has been studied. The real-life application envisaged is structural health monitoring of laminated composite flexbeams taking advantage of the symmetry in the structure. Apart from the defect detection under symmetric conditions, other methods of health monitoring for the same structure are reported for further validation. Results show that MFC works well as a sensor.

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With the increased utilization of advanced composites in strategic industries, the concept of Structural Health Monitoring (SHM) with its inherent advantages is gaining ground over the conventional methods of NDE and NDI. The most attractive feature of this concept is on-line evaluation using embedded sensors. Consequently, development of methodologies with identification of appropriate sensors such as PVDF films becomes the key for exploiting the new concept. And, of the methods used for on-line evaluation acoustic emission has been most effective. Thus, Acoustic Emission (AE) generated during static tensile loading of glass fiber reinforced plastic composites was monitored using a Polyvinylidene fluoride (PVDF) film sensor. The frequency response of the film sensor was obtained with pencil lead breakage tests to choose the appropriate band of operation. The specimen considered for the experiments were chosen to characterize the differences in the operation of the failure mechanisms through AE parametric analysis. The results of the investigations can be characterized using AE parameter indicating that a PVDF film sensor was effective as an AE sensor used in structural health monitoring on-line.

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A damage detection and imaging methodology based on symmetry of neighborhood sensor path and similarity of signal patterns with respect to radial paths in a circular array of sensors has been developed It uses information regarding Limb wave propagation along with a triangulation scheme to rapidly locate and quantify the severity of damage without using all of the sensor data. In a plate like structure, such a scheme can be effectively employed besides full field imaging of wave scattering pattern from the damage, if present in the plate. This new scheme is validated experimentally. Hole and corrosion type damages have been detected and quantified using the proposed scheme successfully. A wavelet based cumulative damage index has been studied which shows monotonic sensitivity against the severity of the damage. which is most desired in a Structural Health Monitoring system. (C) 2010 Elsevier Ltd. All rights reserved.