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Relevant bibliographies by topics / Structural Health Monitoring

Academic literature on the topic 'Structural Health Monitoring'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2024

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Journal articles on the topic "Structural Health 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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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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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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Chattopadhyay, Aditi, and Roger Ghanem. "Preface: Structural Health Monitoring." Journal of Intelligent Material Systems and Structures 24, no. 17 (October 16, 2013): 2061–62. http://dx.doi.org/10.1177/1045389x13506146.

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INADA, Takaomi. "Development of Pressure Vessels : Needs of Structural Health Monitoring System." Proceedings of Conference of Kanto Branch 2004.10 (2004): 49–52. http://dx.doi.org/10.1299/jsmekanto.2004.10.49.

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ElSafty, Adel, Ahmed Gamal, Patrick Kreidl, and Gerald Merckel. "Structural Health Monitoring: Alarming System." Wireless Sensor Network 05, no. 05 (2013): 105–15. http://dx.doi.org/10.4236/wsn.2013.55013.

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Elwasia, Nazar, Mannur J. Sundaresan, Mark J. Schulz, Anindya Ghoshal, P. Frank Pai, and Peter K. C. Tu. "Damage Bounding Structural Health Monitoring." Journal of Intelligent Material Systems and Structures 17, no. 7 (July 2006): 629–48. http://dx.doi.org/10.1177/1045389x06060148.

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Scuro, Carmelo, Paolo Francesco Sciammarella, Francesco Lamonaca, Renato Sante Olivito, and Domenico Luca Carni. "IoT for structural health monitoring." IEEE Instrumentation & Measurement Magazine 21, no. 6 (December 2018): 4–14. http://dx.doi.org/10.1109/mim.2018.8573586.

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Yi, Ting-Hua, and Hong-Nan Li. "Innovative structural health monitoring technologies." Measurement 88 (June 2016): 343–44. http://dx.doi.org/10.1016/j.measurement.2016.05.038.

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Dissertations / Theses on the topic "Structural Health Monitoring"

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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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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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Singh, Gurjashan. "Health Monitoring of Round Objects using Multiple Structural Health Monitoring Techniques." FIU Digital Commons, 2010. http://digitalcommons.fiu.edu/etd/330.

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Structural Health Monitoring (SHM) techniques are widely used in a number of Non – destructive Evaluation (NDE) applications. There is a need to develop effective techniques for SHM, so that the safety and integrity of the structures can be improved. Two most widely used SHM methods for plates and rods use either the spectrum of the impedances or monitor the propagation of lamb waves. Piezoelectric wafer – active sensors (PWAS) were used for excitation and sensing. In this study, surface response to excitation (SuRE) and Lamb wave propagation was monitored to estimate the integrity of the round objects including the pipes, tubes and cutting tools. SuRE obtained the frequency response by applying sweep sine wave to surface. The envelope of the received signal was used to detect the arrival of lamb waves to the sensor. Both approaches detect the structural defects of the pipes and tubes and the wear of the cutting tool.

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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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Books on the topic "Structural Health Monitoring"

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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. Newport Beach, CA: ISTE, 2005.

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

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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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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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International Workshop on Structural Health Monitoring (2nd 1999 Stanford, Calif.). Structural health monitoring, 2000. Lancaster, PA: Technomic Pub. Co., 1999.

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Book chapters on the topic "Structural Health Monitoring"

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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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Weihnacht, Bianca, Uwe Lieske, Tobias Gaul, and Kilian Tschöke. "Structural Health Monitoring." In Handbook of Advanced Non-Destructive Evaluation, 1–19. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-30050-4_50-1.

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Weihnacht, Bianca, Uwe Lieske, Tobias Gaul, and Kilian Tschöke. "Structural Health Monitoring." In Handbook of Advanced Nondestructive Evaluation, 1591–608. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-26553-7_50.

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Anderson, Matthew, and David Cousins. "Structural health monitoring." In Highway Bridge Management, 133–50. London: ICE Publishing, 2022. http://dx.doi.org/10.1680/hbm.65543.133.

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Mangalgiri, Prakash D., and Kota Harinarayana. "Structural Health Monitoring." In Aerospace Materials and Material Technologies, 449–77. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2143-5_22.

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Bakht, Baidar, and Aftab Mufti. "Structural Health Monitoring." In Bridges, 307–54. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17843-1_10.

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Tennina, Stefano, Marco Tiloca, Jan-Hinrich Hauer, Melanie Bouroche, Mario Alves, Anis Koubaa, Petr Jurcik, et al. "Structural Health Monitoring." In SpringerBriefs in Electrical and Computer Engineering, 137–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37368-8_7.

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Chang, Fu-Kuo, Johannes F. C. Markmiller, Jinkyu Yang, and Yujun Kim. "Structural Health Monitoring." In System Health Management, 419–28. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119994053.ch26.

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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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Conference papers on the topic "Structural Health 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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Xu, Q. "Design of compact and portable structural health monitoring system for piezoelectric guided wave." In Structural Health Monitoring. Materials Research Forum LLC, 2023. http://dx.doi.org/10.21741/9781644902455-35.

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Abstract. Structural health monitoring (SHM) is an important technology to realize structural reliability evaluation, which can increase the safety and reduce the maintenance costs of engineering structures. Piezoelectric guided wave SHM technology has broad application prospects because it is sensitive to small damage and can realize multi parameter monitoring such as damage and impact. However, the reported piezoelectric guided wave SHM system is large, which is not conducive to engineering applications. In this paper, aiming at the ground rapid monitoring application of aircraft structure, a compact and portable SHM system for piezoelectric guided wave is developed. Firstly, an overall architecture of hierarchical design is proposed, and then the software and hardware design of the system is studied. The volume of the system is only 273×184×59mm3, the mass is less than 3kg, it can support 32 sensor channels, the excitation voltage amplitude can reach 140Vpp, and the maximum sampling rate can reach 60MS/s. Finally, a verification experiment is carried out to realize the accurate location of the damage of carbon fiber composite structure. The results show that the system is a high-performance portable system suitable for aircraft ground applications.

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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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"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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"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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"Damage Identification of High-speed Maglev Guideway Girder Based on Modal Identification." In Structural Health Monitoring. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901311-34.

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Abstract. As a modern high-tech rail vehicle, the maglev train realizes the non-contact suspension and guidance between the train and the guideway, which greatly reduces the resistance of the system. Due to the high-speed operation characteristics of maglev trains, the structural health monitoring of guideway girders is particularly important for the safety and stability of maglev train operation. This paper takes the maglev train guideway girder as the monitoring target, and the finite element model of the maglev vehicle-guideway is established to simulate the running state of the train passing through the guideway girder. The dynamic response data of the guideway girder is obtained in the finite element model, considering healthy states and different damage states of the guideway girder. Then, a modal-based damage identification method is proposed, which obtains the guideway girder damage sensitive characteristics by decomposing the guideway girder acceleration response signal. Finally, based on the measured guideway girder acceleration data, this paper verifies the effectiveness of the damage identification method in guideway girder structure health monitoring, which provides reference and guidance for the future maintenance of the maglev guideway girder.

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"Extraction of Parameters for 90-degree Turn Prediction Using the IMU-based Motion Capture System." In Structural Health Monitoring. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901311-29.

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Abstract. Against the increasing number of single households, we have been proposing the “Biofied Building” that provides a safe, secure, and comfortable living space for a resident using a small home robot. The robot can be used for real-time sensing of the resident’s position and behavior. On the other hand, for further use of the robot, such as choosing a path that does not disturb the resident, a phase to predict the resident’s behavior is necessary. Walking, which is one of the most basic activities of daily living, is often targeted in studies of motion prediction. However, most of them deal with steady walking, even though walking in daily life includes unsteady walking such as the turning motion. Therefore, the purpose of this study was to extract the prediction parameters to construct a prediction method for the unsteady 90-degree turn. In this study, we explored the effective prediction parameters for 90-degree turns based on the measured data using the inertial measurement unit (IMU) based motion capture system aiming to introduce the prediction of unsteady walking to the “Biofied Building”.

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"Comparative Assessment of Distributed Strain Measurement Technologies." In Structural Health Monitoring. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901311-3.

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Abstract. Fibre optic (FO) distributed strain sensing technology has introduced a significant new capability for structural health monitoring (SHM). FO sensing (FOS) offers a simpler installation process with improved resistance to corrosion and electromagnetic interference compared to traditional electrical resistance foil strain gauges (FSGs) which unlike FOS is limited to single point measurements. Previous FO distributed strain measurement studies at the Defence Science and Technology Group showed good correlation between strain measurements derived from a proprietary continuous fibre grating system and FSGs. This paper compares a commercially available, non-proprietary FO sensing system and digital image correlation (DIC) against industry standard FSGs and finite element analysis (FEA) predictions.

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"Estimation Method of Maximum Inter-Story Drift Angle of Wood-Frame House using Two Accelerometers." In Structural Health Monitoring. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901311-21.

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Abstract. In April 2016, Kumamoto earthquake occurred in Japan and many wooden houses collapsed and many lives were lost because of the second and larger main shock. As a result, the need for Structural Health Monitoring (SHM) for wooden houses is receiving increased attention. In the SHM system, maximum inter-story drift angle is considered as the damage index. We assume that the first story of a wooden house will be damaged so that we need only to focus on the response of this first story. Hence, we install accelerometers on the ground floor and the second floor. In order to estimate the inter-story drift angle, we need to integrate the acceleration records twice. The simple double integration will result in erroneous results. Thus, in this paper, we propose the most appropriate integration method to estimate the maximum story drift angle with high accuracy using two accelerometers.

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Reports on the topic "Structural Health Monitoring"

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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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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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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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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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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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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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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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Masri, Sami F. Analytical and Experimental Studies into Structural Health Monitoring. Fort Belvoir, VA: Defense Technical Information Center, December 2000. http://dx.doi.org/10.21236/ada387071.

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Bubacz, Jacob A., Hana T. Chmielewski, Alexander E. Pape, Andrew J. Depersio, Lee M. Hively, Robert K. Abercrombie, and Shane Boone. Phase Space Dissimilarity Measures for Structural Health Monitoring. Office of Scientific and Technical Information (OSTI), November 2011. http://dx.doi.org/10.2172/1029952.

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Yalisove. Femtosecond Laser Assisted Health Monitoring of Critical Structural Components. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada435785.

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