Academic literature on the topic 'Structural monitoring'

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Journal articles on the topic "Structural monitoring"

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Ghodake, Prasad, and S. R. Suryawanshi. "Structural Health Monitoring." Journal of Advances and Scholarly Researches in Allied Education 15, no. 2 (April 1, 2018): 360–63. http://dx.doi.org/10.29070/15/56847.

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Rasool, Junaid. "IOT Based Structural Health Monitoring." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 771–73. http://dx.doi.org/10.31142/ijtsrd18743.

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Collacott, R. A., and H. Saunders. "Structural Integrity Monitoring." Journal of Vibration and Acoustics 110, no. 4 (October 1, 1988): 571–72. http://dx.doi.org/10.1115/1.3269570.

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Jones, R., W. K. Chiu, S. Pitt, and S. C. Galea. "Structural integrity monitoring." Engineering Failure Analysis 4, no. 2 (June 1997): 117–31. http://dx.doi.org/10.1016/s1350-6307(97)00001-0.

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Poulter, L. "Structural integrity monitoring." NDT International 20, no. 2 (April 1987): 131. http://dx.doi.org/10.1016/0308-9126(87)90361-0.

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Pines, Darryll J., and Fu-Kuo Chang. "Structural Health Monitoring." Journal of Intelligent Material Systems and Structures 9, no. 11 (November 1998): 875. http://dx.doi.org/10.1177/1045389x9800901101.

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Chu, Chia-Shang James, Maxwell Stinchcombe, and Halbert White. "Monitoring Structural Change." Econometrica 64, no. 5 (September 1996): 1045. http://dx.doi.org/10.2307/2171955.

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Kinkead, N. "Structural integrity monitoring." Engineering Structures 8, no. 4 (October 1986): 286–87. http://dx.doi.org/10.1016/0141-0296(86)90040-4.

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Del Grosso, Andrea E. "Structural Health Monitoring Standards." IABSE Symposium Report 102, no. 6 (September 1, 2014): 2991–98. http://dx.doi.org/10.2749/222137814814069804.

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Matzuoka, Kazumi. "Structural Deterioration and Monitoring." Zairyo-to-Kankyo 58, no. 5 (2009): 169. http://dx.doi.org/10.3323/jcorr.58.169.

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Dissertations / Theses on the topic "Structural monitoring"

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Dawood, Tariq Ali. "Structural health monitoring of GFRP sandwich beam structures." Thesis, University of Southampton, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.438529.

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Ullah, Israr. "Vibration-based structural health monitoring of composite structures." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/vibrationbased-structural-health-monitoring-of-composite-structures(f21abb03-5b46-4640-9447-0552d5e0c7d6).html.

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Composite materials are in use in several applications, for example, aircraft structural components, because of their light weight and high strength. However the delamination which is one of the serious defects often develops and propagates due to vibration during the service of the structure. The presence of this defect warrants the design life of the structure and the safety. Hence the presence of such defect has to be detected in time to plan the remedial action well in advance. There are a number of methods in the literature for damage detection. They are either 'baseline free/reference free method' or using the data from the healthy structure for damage detection. However very limited vibration-based methods are available in the literature for delamination detection in composite structures. Many of these methods are just simulated studies without experimental validation. Grossly 2 kinds of the approaches have been suggested in the literature, one related to low frequency methods and other high frequency methods. In low frequency approaches, the change in the modal parameters, curvatures, etc. is compared with the healthy structure as the reference, however in the high frequency approaches, excitation of structures at higher modes of the order of few kHz or more needed with distributed sensors to map the deflection for identification of delamination. Use of high frequency methods imposes the limitations on the use of the conventional electromagnetic shaker and vibration sensors, whereas the low frequency methods may not be feasible for practical purpose because it often requires data from the healthy state which may not be available for old structures. Hence the objective of this research is to develop a novel reference-free method which can just use the vibration responses at a few lower modes using a conventional shaker and vibration sensors (accelerometers/laser vibrometers). It is believed that the delaminated layers will interact nonlinearly when excited externally. Hence this mechanism has been utilised in the numerical simulations and the experiments on the healthy and delaminated composite plates. Two methods have been developed here - first method can quickly identify the presence of the delamination when excited at just few lower modes and other method identify the location once the presence of the delamination is confirmed. In the first approach an averaged normalised RMS has been suggested and experimentally validated for this purpose. Latter the vibration data have then been analysed further to identify the location of delamination and its size. Initially, the measured acceleration responses from the composite plates have been differentiated twice to amplify the nonlinear interaction clearly in case of delaminated plate and then kurtosis was calculated at each measured location to identify the delamination location. The method has further been simplified by just using the harmonics in the measured responses to identify the location. The thesis presents the process of the development of the novel methods, details of analysis, observations and results.
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Lannamann, Daniel L. "Structural health monitoring : numerical damage predictor for composite structures." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA390997.

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Nayyerloo, Mostafa. "Real-time Structural Health Monitoring of Nonlinear Hysteretic Structures." Thesis, University of Canterbury. Department of Mechanical Engineering, 2011. http://hdl.handle.net/10092/6581.

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The great social and economic impact of earthquakes has made necessary the development of novel structural health monitoring (SHM) solutions for increasing the level of structural safety and assessment. SHM is the process of comparing the current state of a structure’s condition relative to a healthy baseline state to detect the existence, location, and degree of likely damage during or after a damaging input, such as an earthquake. Many SHM algorithms have been proposed in the literature. However, a large majority of these algorithms cannot be implemented in real time. Therefore, their results would not be available during or immediately after a major event for urgent post-event response and decision making. Further, these off-line techniques are not capable of providing the input information required for structural control systems for damage mitigation. The small number of real-time SHM (RT-SHM) methods proposed in the past, resolve these issues. However, these approaches have significant computational complexity and typically do not manage nonlinear cases directly associated with relevant damage metrics. Finally, many available SHM methods require full structural response measurement, including velocities and displacements, which are typically difficult to measure. All these issues make implementation of many existing SHM algorithms very difficult if not impossible. This thesis proposes simpler, more suitable algorithms utilising a nonlinear Bouc-Wen hysteretic baseline model for RT-SHM of a large class of nonlinear hysteretic structures. The RT-SHM algorithms are devised so that they can accommodate different levels of the availability of design data or measured structural responses, and therefore, are applicable to both existing and new structures. The second focus of the thesis is on developing a high-speed, high-resolution, seismic structural displacement measurement sensor to enable these methods and many other SHM approaches by using line-scan cameras as a low-cost and powerful means of measuring structural displacements at high sampling rates and high resolution. Overall, the results presented are thus significant steps towards developing smart, damage-free structures and providing more reliable information for post-event decision making.
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Kirikera, Goutham Raghavendra. "A Structural Neural System for Health Monitoring of Structures." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1155149869.

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Islami, Kleidi. "System identification and structural health monitoring of bridge structures." Doctoral thesis, Università degli studi di Padova, 2013. http://hdl.handle.net/11577/3423079.

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This research study addresses two issues for the identification of structural characteristics of civil infrastructure systems. The first one is related to the problem of dynamic system identification, by means of experimental and operational modal analysis, applied to a large variety of bridge structures. Based on time and frequency domain techniques and mainly with output-only acceleration, velocity or strain data, modal parameters have been estimated for suspension bridges, masonry arch bridges, concrete arch and continuous bridges, reticular and box girder steel bridges. After giving an in-depth overview of standard and advanced stochastic methods, differences of the existing approaches in their performances are highlighted during system identification on the different kinds of civil infrastructures. The evaluation of their performance is accompanied by easy and hard determinable cases, which gave good results only after performing advanced clustering analysis. Eventually, real-time vibration-based structural health monitoring algorithms are presented during their performance in structural damage detection by statistical models. The second issue is the noise-free estimation of high order displacements taking place on suspension bridges. Once provided a comprehensive treatment of displacement and acceleration data fusion for dynamic systems by defining the Kalman filter algorithm, the combination of these two kinds of measurements is achieved, improving the deformations observed. Thus, an exhaustive analysis of smoothed displacement data on a suspension bridge is presented. The successful tests were subsequently used to define the non-collocated sensor monitoring problem with the application on simplified models
Questo lavoro di ricerca mira a due obiettivi per l'identificazione delle caratteristiche strutturali dei sistemi infrastrutturali civili. Il primo è legato al problema della identificazione del sistema dinamico, mediante analisi modale sperimentale e operativa, applicata ad una grande varietà di strutture da ponte. Basandosi su tecniche nel dominio del tempo e delle frequenze e, soprattutto, su dati di output di accelerazione, velocità o strain, i parametri modali sono stati stimati per ponti sospesi, ponti ad arco in muratura, ponti a travi in calcestruzzo e ad arco, ponti reticolari e ponti in acciaio a cassone. Dopo aver dato una panoramica approfondita dei metodi stocastici standard ed avanzati, sono state evidenziate le differenze degli approcci esistenti nelle loro performance per l'identificazione del sistema sui diversi tipi di infrastrutture civili. La valutazione della loro performance viene accompagnata da casi facilmente e difficilmente determinabili, che hanno dato buoni risultati solo dopo l'esecuzione di analisi avanzate di Clustering. Inoltre, sono stati sviluppati algoritmi di identificazione dinamica automatica in tempo reale basandosi sulle vibrazioni strutturali dei ponti monitorati, a sua volta utilizzati nel rilevamento dei danni strutturali tramite modelli statistici. Il secondo problema studiato riguarda la stima di spostamenti di ordine superiore che si svolgono sui ponti sospesi, eliminando il rumore di misura e di processo. Una volta fornito un trattamento completo della fusione dei dati di spostamento e accelerazione per i sistemi dinamici tramite il filtro di Kalman, la combinazione di questi due tipi di misurazioni ha mostrato un miglioramento nelle deformazioni osservate. Pertanto, è stata presentata un'analisi esauriente di un ponte sospeso e dei sui dati dinamici e di spostamento filtrati. I test positivi sono stati successivamente utilizzati per definire il problema dei sensori non collocati alla stessa locazione ed applicazione su modelli semplificati
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Webb, Graham Thomas. "Structural health monitoring of bridges." Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708027.

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Grisso, Benjamin Luke. "Advancing Autonomous Structural Health Monitoring." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/29960.

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The focus of this dissertation is aimed at advancing autonomous structural health monitoring. All the research is based on developing the impedance method for monitoring structural health. The impedance technique utilizes piezoelectric patches to interrogate structures of interested with high frequency excitations. These patches are bonded directly to the structure, so information about the health of the structure can be seen in the electrical impedance of the piezoelectric patch. However, traditional impedance techniques require the use of a bulky and expensive impedance analyzer. Research presented here describes efforts to miniaturize the hardware necessary for damage detection. A prototype impedance-based structural health monitoring system, incorporating wireless based communications, is fabricated and validated with experimental testing. The first steps towards a completely autonomous structural health monitoring sensor are also presented. Power harvesting from ambient energy allows a prototype to be operable from a rechargeable power source. Aerospace vehicles are equipped with thermal protection systems to isolate internal components from harsh reentry conditions. While the thermal protection systems are critical to the safety of the vehicle, finding damage in these structures presents a unique challenge. Impedance techniques will be used to detect the standard damage mechanism for one type of thermal protection system. The sensitivity of the impedance method at elevated temperatures is also investigated. Sensors are often affixed to structures as a means of identifying structural defects. However, these sensors are susceptible to damage themselves. Sensor diagnostics is a field of study directed at identifying faulty sensors. The influence of temperature on these techniques is largely unstudied. In this dissertation, a model is generated to identify damaged sensors at any temperature. A sensor diagnostics method is also adapted for use in developed hardware. The prototype used is completely digital, so standard sensor diagnostics techniques are inapplicable. A new method is developed to work with the digital hardware.
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Ward, Jacob Thomas Elliott. "Guided wave structural health monitoring." Thesis, University of Bristol, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.682233.

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Routine airframe Non-Destructive Testing (NDT) procedures are costly and prone to human error. Guided wave structural health monitoring (GWSHM) shows great promise to in future assist these carefully regulated aerospace NDT practices. Using automatic GWSHM to both detect and localise damage can better focus the human NDT effort and ultimately lead to safer operation of airframes. The thesis presents structural health monitoring techniques for airframes using measurements of guided waves. Work is presented on both metal plates and carbon fibre reinforced plastic panels. An active GWSHM method is considered in its capability to detect and localise damage by measurements of scattered Lamb waves from artificially placed damage. The contribution to knowledge on active GWSHM has been towards effective and practical strategies for placing a low number of transducers into arrays suitable for global coverage. Much early active GWSHM studies often adopted a uniformly sparse distribution of transducer elements, perhaps in an attempt to gain the best possible global coverage. In this thesis, active GWSHM performance has been evaluated for arrays of different geometry and has shown that a uniformly sparse distribution of transducer elements may not be the most effective strategy when using a minimal number of sensors. Simulated and artificial damage, placed with different orientations over a large area, has been used to test candidate array layouts. It finds the layout optimal for damage detection is not necessarily the layout optimal for damage localisation. The zeroth order anti-symmetric Lamb wave mode has been used at low frequency-thickness. The mode, referred to as the flexural mode when propagating with low frequency-thickness, is favoured for its short wave length and long range. At low frequency-thickness this mode is quickly outrun by its symmetric counterpart, causing coherent noise in the signals recorded. Baseline subtraction is used to suppress the coherent noise before imaging. Benign structural features, that would usually hinder damage-localisation from an image, are actually found to assist damage localisation for some array layouts when using the reference baseline signal subtraction technique. A passive GWSHM method is considered in its capability to localise impacts. Impact events on carbon fibre panels are localised using a low frequency passive array. The technique is suggested for evaluating damage from tyre-burst or propeller debris impacts to airframe surfaces. It is particularly relevant to new airframe designs that have significant usage of composite materials on their outer surface. Historically the aerospace sector has readily adopted time of arrival estimation methods similar to those found on a standard oscilloscope. As an example, acoustic emission monitoring, in recent decades has routinely used threshold-crossing as a means of time of arrival measurement. An alternative is presented requiring the whole time series to be post-processed. It extracts an alternative arrival time from propagating waves resulting from the impact, which can be used in time-difference of arrival algorithms. This method is shown to be more reliable and accurate for impact localisation than historical techniques.
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Engelbrecht, André. "Structural integrity monitoring using vibration measurements." Pretoria : [s.n.], 2006. http://upetd.up.ac.za/thesis/available/etd-07032006-122342/.

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Books on the topic "Structural monitoring"

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Bui, Tinh Quoc, Le Thanh Cuong, and Samir Khatir, eds. Structural Health Monitoring and Engineering Structures. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0945-9.

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Ganguli, Ranjan. Structural Health Monitoring. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4988-5.

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Balageas, Daniel, Claus-Peter Fritzen, and Alfredo Gemes, eds. Structural Health Monitoring. London, UK: ISTE, 2006. http://dx.doi.org/10.1002/9780470612071.

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Yan, Ruqiang, Xuefeng Chen, and Subhas Chandra Mukhopadhyay, eds. Structural Health Monitoring. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56126-4.

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Farrar, Charles R., and Keith Worden. Structural Health Monitoring. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118443118.

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Daniel, Balageas, Fritzen Claus-Peter, and Güemes Alfredo, eds. Structural health monitoring. London: ISTE, 2006.

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Daniel, Balageas, Fritzen Claus-Peter, and Güemes Alfredo, eds. Structural health monitoring. Newport Beach, CA: ISTE, 2005.

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Structural integrity monitoring. London: Chapman and Hall, 1985.

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Rainieri, Carlo, Giovanni Fabbrocino, Nicola Caterino, Francesca Ceroni, and Matilde A. Notarangelo, eds. Civil Structural Health Monitoring. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74258-4.

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Limongelli, Maria Pina, and Mehmet Çelebi, eds. Seismic Structural Health Monitoring. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13976-6.

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Book chapters on the topic "Structural monitoring"

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Ganguli, Ranjan. "Introduction." In Structural Health Monitoring, 1–5. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4988-5_1.

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Ganguli, Ranjan. "Fuzzy Logic and Probability in Damage Detection." In Structural Health Monitoring, 7–35. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4988-5_2.

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Ganguli, Ranjan. "Modal Curvature Based Damage Detection." In Structural Health Monitoring, 37–78. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4988-5_3.

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Ganguli, Ranjan. "Damage Detection in Composite Plates." In Structural Health Monitoring, 79–101. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4988-5_4.

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Ganguli, Ranjan. "Damage Detection in Smart Composite Plates." In Structural Health Monitoring, 103–26. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4988-5_5.

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Ganguli, Ranjan. "Damage Growth Monitoring in Composite Plates." In Structural Health Monitoring, 127–60. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4988-5_6.

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Ganguli, Ranjan. "Wavelet Based Damage Detection." In Structural Health Monitoring, 161–92. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4988-5_7.

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Ganguli, Ranjan. "Fractal Dimension Based Damage Detection." In Structural Health Monitoring, 193–214. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4988-5_8.

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Vanlanduit, Steve, Mario Sorgente, Aydin R. Zadeh, Alfredo Güemes, and Nadimul Faisal. "Strain Monitoring." In Structural Health Monitoring Damage Detection Systems for Aerospace, 219–41. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72192-3_8.

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AbstractThis chapter provides an overview of the use of strain sensors for structural health monitoring. Compared to acceleration-based sensors, strain sensors can measure the deformation of a structure at very low frequencies (up to DC) and enable the measurement of ultrasonic responses. Many existing SHM methods make use of strain measurement data. Furthermore, strain sensors can be easily integrated in (aircraft) structures. This chapter discusses the working principle of traditional strain gauges (Sect. 8.1) and different types of optical fiber sensors (Sect. 8.2). The installation requirements of strain sensors and the required hardware for reading out sensors are provided. We will also give an overview of the advantages and the limitations of commonly used strain sensors. Finally, we will present an overview of the applications of strain sensors for structural health monitoring in the aeronautics field.
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Lu, George, and Y. J. Yang. "STRUCTURAL HEALTH MONITORING." In Internet of Things and Data Analytics Handbook, 665–74. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119173601.ch40.

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Conference papers on the topic "Structural monitoring"

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"Structural Health Monitoring (SHM) of Space Structures." In Structural Health Monitoring. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901311-42.

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Abstract. Recent years have seen an increased interest in exploring outer space for space tourism or for unmanned or manned planetary explorations. The captivated interests among various stakeholders to employ advanced technologies to meet the requirements of these missions have necessitated the use of newly developed asset monitoring systems to ensure robustness and mission reliability. Although, Non-Destructive Testing (NDT) methods provide sufficient information about the state of the structure at the time of inspection, the need for continuously monitoring the health of the structure throughout the mission has asserted the use of Structure Health Monitoring (SHM) technologies to increase the levels of safety and thereby, reducing the overall mission costs. However, since the implementation of SHM technologies for space missions can be affected by several factors including, environmental conditions, measurement reliability and unavailability of adequate standards, additional considerations on its employability must be reconsidered. This article demonstrates a structured approach to compare the capabilities of some of the most promising SHM technologies in consideration of these influential factors. Additionally, remarks on the feasibility of employing these SHM technologies and the role they could play in such critical missions would be elaborated.
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HADJRIA, RAFIK, and OSCAR D’ALMEIDA. "Structural Health Monitoring for Aerospace Composite Structures." In Structural Health Monitoring 2019. Lancaster, PA: DEStech Publications, Inc., 2019. http://dx.doi.org/10.12783/shm2019/32280.

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SHARIF-KHODAEI, ZAHRA, MARCO THIENE, and M. H. ALIABADI. "Structural Health Monitoring Platform for Sensorised Composite Structures." In Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/247.

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LITTLE, W. THOMAS, TRYSTON B. GILBERT, DARIAN D. WOOD, MICHELE K. PLATT, and JEAN P. VREULS, JR. "Optimized Structural Health Monitoring System Design for Aviation Structures." In Structural Health Monitoring 2017. Lancaster, PA: DEStech Publications, Inc., 2017. http://dx.doi.org/10.12783/shm2017/13992.

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LOUBET, GAËL, ALASSANE SIDIBE, ALEXANDRU TAKACS, JEAN-PAUL BALAYSSAC, and DANIELA DRAGOMIRESCU. "BATTERY-FREE STRUCTURAL HEALTH MONITORING SYSTEM FOR CONCRETE STRUCTURES." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36246.

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This paper presents a cyber-physical system based on a wireless sensor network dedicated to structural health monitoring of reinforced concretes throughout their lifetime. This cyber-physical system is intended to implement a communicating reinforced concrete. Two types of nodes compose this WSN. The sensing node is fully wireless, can measure various parameters (such as temperature, relative humidity, mechanical strain, or resistivity), is battery-free, and is wirelessly and remotely powered and controlled via a radiative electromagnetic power transfer system by the second type of nodes, the communicating node. The communicating node connect the WSN to the digital world.
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QING, XINLIN, HU SUN, and MINGYU LU. "Integrated Structural Health Monitoring System for Civil Aircraft Structures." In Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/13.

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SCHORN, STEPHEN, and NICOLAS CORTES. "Structural Health Monitoring of Unique Structures: Normandy and Tancarville." In Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/204.

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"A 3D Printed, Constriction-Resistive Sensor for the Detection of Ultrasonic Waves." In Structural Health Monitoring. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901311-33.

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Abstract. Ultrasonic waves, either bulk waves or guided waves, are commonly used for non-destructive evaluation, for example in structural health monitoring. Traditional sensors for detecting ultrasonic waves include metallic strain gauges and piezoelectric ceramics. Recently piezoresistive nanocomposites have emerged as a promising sensor with high sensing range. In this paper, a constriction-resistive based sensor made from a graphene reinforced PLA filament is developed using a fused deposition modelling 3D printing approach as a novel type of ultrasonic sensor for structural health monitoring purposes. The sensor is made of very low-cost and recyclable thermoplastic material, which is lightweight and can be either directly printed onto the surface of various engineering structures, or embedded into the interior of a structure via fused filament fabrication 3D printing. These characteristics make this sensor a promising candidate compared to the traditional sensors in detecting ultrasonic waves for structural health monitoring. The printed sensors can detect ultrasonic signals with frequencies around 200 kHz, with good signal-to-noise ratio and sensitivity. When deployed between two adjacent printed tracks , and exploiting a novel kissing-bond mechanism, the sensor is capable of detecting ultrasonic waves. Several confirmatory experiments were carried out on this printed sensor to validate the capability of the printed sensor for structural health monitoring.
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"Computational Study of Scattering Elastic Waves Due to a Teredo Marine Borer-Like Cylindrical Defect Embedded in an Isotropic Solid Cylinder." In Structural Health Monitoring. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901311-13.

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Abstract. This paper showcases a quantitative investigation of scattering of ultrasonic waves experiences when impinging on a cylindrical defect inside a solid cylinder. Such cylindrical bores reduce the structural capacity of the cylinder, these defects constitute an even greater risk as they cannot be observed from the surface. The focal point investigated herein is to develop a better understanding of the wave’s scattering when interacting with defects of cylindrical bore, mimicking the Teredo marine borer, within the solid cylinder. Two-dimensional Finite Element simulations are carried out using ABAQUS software. A 200 kHz 5.5 cycle Hann windowed excitation on an isotropic cylinder is simulated a point source excitation at the circumference of the cylinder is used. The scattering wave fields from a range of defect diameters through the solid cylinder are presented. Using Two-Dimensional Fast Fourier Transform, the wave mode and velocity of the scattered wavefield along various directions was identified in cylindrical coordinates, to decouple the wave modes. Computational results are presented for the scattering pattern as a function of cylindrical bore diameter size relative to wavelength. This study serves as an efficient approach when choosing an input for ultrasonic imaging, with the aim to obtain high fidelity imaging resolution for structural health monitoring applications.
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10

"Gaussian Mixture Model Based Damage Evaluation for Aircraft Structures." In Structural Health Monitoring. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901311-18.

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Abstract. The Guided Wave (GW) based Structural Health Monitoring (SHM) method is of significant research interest because of its wide monitoring range and high sensitivity. However, there are still many challenges in real engineering applications due to complex time-varying conditions, such as changes in temperature and humidity, random dynamic loads, and structural boundary conditions. In this paper, a Gaussian Mixture Model (GMM) is adopted to deal with these problems. Multi-dimensional GMM (MDGMM) is proposed to model the probability distribution of GW features under time-varying conditions. Furthermore, to measure the migration degree of MDGMM to reveal the crack propagation, research on migration indexes of the probability model is carried out. Finally, the validation in an aircraft fatigue test shows a good performance of the MDGMM.
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Reports on the topic "Structural monitoring"

1

Roach, Dennis P., Raymond Bond, and Doug Adams. Structural Health Monitoring for Impact Damage in Composite Structures. Office of Scientific and Technical Information (OSTI), August 2014. http://dx.doi.org/10.2172/1154712.

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2

Chiu, Wing K. Structural Health Monitoring Pertaining to Critical Aircraft Structural Components. Fort Belvoir, VA: Defense Technical Information Center, March 2010. http://dx.doi.org/10.21236/ada515997.

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3

Avioli, Michael J., Fei Yan, and Joseph L. Rose. Dynamics-based Nondestructive Structural Monitoring Teclrniques. Fort Belvoir, VA: Defense Technical Information Center, May 2012. http://dx.doi.org/10.21236/ada569293.

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Avioli, Michael J., and Fei Yan. Dynamics-based Nondestructive Structural Monitoring Techniques. Fort Belvoir, VA: Defense Technical Information Center, June 2012. http://dx.doi.org/10.21236/ada580525.

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5

Chattopadhyay, Aditi. Structural Health Monitoring for Heterogeneous Systems. Fort Belvoir, VA: Defense Technical Information Center, June 2006. http://dx.doi.org/10.21236/ada465429.

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Flynn, Eric B. Design Optimization of Structural Health Monitoring Systems. Office of Scientific and Technical Information (OSTI), March 2014. http://dx.doi.org/10.2172/1122908.

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7

Chang, Fu-Kuo. Structural Health Monitoring: A Summary Report on the First Stanford Workshop on Structural Health Monitoring, September 18-20, 1997. Fort Belvoir, VA: Defense Technical Information Center, May 1998. http://dx.doi.org/10.21236/ada350933.

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8

Park, G., C. R. Farrar, M. D. Todd, T. Hodgkiss, and T. Rosing. Energy Harvesting for Structural Health Monitoring Sensor Networks. Office of Scientific and Technical Information (OSTI), February 2007. http://dx.doi.org/10.2172/902464.

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9

DOEBLING, S. W., and F. M. HEMEZ. OVERVIEW OF UNCERTAINTY ASSESSMENT FOR STRUCTURAL HEALTH MONITORING. Office of Scientific and Technical Information (OSTI), August 2001. http://dx.doi.org/10.2172/783378.

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Rickard, Lee J. High-Resolution Structural Monitoring of Ionospheric Absorption Events. Fort Belvoir, VA: Defense Technical Information Center, July 2013. http://dx.doi.org/10.21236/ada581050.

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