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Artykuły w czasopismach na temat „Structural Health Monitoring”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 26 lipca 2024

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1

Ghodake, Prasad, i S. R. Suryawanshi. "Structural Health Monitoring". Journal of Advances and Scholarly Researches in Allied Education 15, nr 2 (1.04.2018): 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 (31.10.2018): 771–73. http://dx.doi.org/10.31142/ijtsrd18743.

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Pines, Darryll J., i Fu-Kuo Chang. "Structural Health Monitoring". Journal of Intelligent Material Systems and Structures 9, nr 11 (listopad 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, nr 6 (1.09.2014): 2991–98. http://dx.doi.org/10.2749/222137814814069804.

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Chattopadhyay, Aditi, i Roger Ghanem. "Preface: Structural Health Monitoring". Journal of Intelligent Material Systems and Structures 24, nr 17 (16.10.2013): 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 i Gerald Merckel. "Structural Health Monitoring: Alarming System". Wireless Sensor Network 05, nr 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 i Peter K. C. Tu. "Damage Bounding Structural Health Monitoring". Journal of Intelligent Material Systems and Structures 17, nr 7 (lipiec 2006): 629–48. http://dx.doi.org/10.1177/1045389x06060148.

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Scuro, Carmelo, Paolo Francesco Sciammarella, Francesco Lamonaca, Renato Sante Olivito i Domenico Luca Carni. "IoT for structural health monitoring". IEEE Instrumentation & Measurement Magazine 21, nr 6 (grudzień 2018): 4–14. http://dx.doi.org/10.1109/mim.2018.8573586.

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

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Rivera, E., A. A. Mufti i D. J. Thomson. "Civionics for structural health monitoring". Canadian Journal of Civil Engineering 34, nr 3 (1.03.2007): 430–37. http://dx.doi.org/10.1139/l06-159.

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As the design and construction of civil structures continue to evolve, it is becoming imperative that these structures be monitored for their health. To meet this need, the discipline of civionics has emerged. It involves the application of electronics to civil structures and aims to assist engineers in realizing the full benefits of structural health monitoring (SHM). Therefore, the goal of the civionics specifications outlined in this work is to ensure that the installation and operation of fibre optic sensors are successful. This paper will discuss several lessons learned during the implementation of health monitoring systems for civil structures. The monitoring of these structures primarily motivated the writing of these specifications. Creating a standard procedure for SHM eliminated several ambiguities, such as fibre sensor specifications and the types of cables required. As a result, it is expected that these specifications will help ensure that the sensors will survive the installation process and eventually prove their value over years of structural health monitoring. The civionics fibre optic sensor specifications include the requirements for fibre sensors and their corresponding readout units. They also include specifications for the cables, conduits, junction boxes, termination, and environmental protection.Key words: civionics, structural health monitoring, fibre optic sensors, specifications.

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12

Shrive, P. L., T. G. Brown i N. G. Shrive. "Practicalities of structural health monitoring". Smart Structures and Systems 5, nr 4 (25.07.2009): 357–67. http://dx.doi.org/10.12989/sss.2009.5.4.357.

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Sumitro, S., i M. L. Wang. "Sustainable structural health monitoring system". Structural Control and Health Monitoring 12, nr 3-4 (2005): 445–67. http://dx.doi.org/10.1002/stc.79.

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14

Rezk, M. Y., N. H. Mohamed i N. M. Nagy. "Structural health monitoring with UAV". Journal of Physics: Conference Series 2616, nr 1 (1.11.2023): 012051. http://dx.doi.org/10.1088/1742-6596/2616/1/012051.

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Abstract In recent years, the use of drones to monitor various types of smart constructions has attracted more attention. Unmanned Aerial Vehicles (UAV) have a number of potential benefits over manual methods for Analyzing construction due to their permit scalable, quick, and affordable solutions to tasks that would otherwise be unsuitable for individuals who are subject to fatigue and measurement uncertainty. In order to better understand how drones can be used in dam monitoring and construction for situation assessment, early warning, and image processing, the current study is studying this topics. High resolution Ierial images that captured by UAV is used to detect the cracks in the dam body structure. Drone-shot images are analyzed by using MATLAB software in order to assess the crack in the dam body and make the correct maintenance.The finite element program Geostudio is undertaken to evaluate a complete numerical analysis of the dam to find out the cause of cracks in the dam body and give solutions to prevent these cracks from happening again depending on the outcome results. This study is applied under two cases seismic and static condition. The results of FEM model concluded that cracks starts to appear at slope change point at downstream and in upstream at dam heel during earthquake action. The current work shows that using drones in dam monitoring is a very effective and fast way to detect cracks in the dam body.

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15

Pozo, Francesc, Diego A. Tibaduiza i Yolanda Vidal. "Sensors for Structural Health Monitoring and Condition Monitoring". Sensors 21, nr 5 (24.02.2021): 1558. http://dx.doi.org/10.3390/s21051558.

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Structural control and health monitoring as condition monitoring are some essential areas that allow for different system parameters to be designed, supervised, controlled, and evaluated during the system’s operation in different processes, such as those used in machinery, structures, and different physical variables in mechanical, chemical, electrical, aeronautical, civil, electronics, mechatronics, and agricultural engineering applications, among others [...]

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16

Silva, Ignacio Javier González, i Raid Karoumi. "Traffic monitoring using a structural health monitoring system". Proceedings of the Institution of Civil Engineers - Bridge Engineering 168, nr 1 (marzec 2015): 13–23. http://dx.doi.org/10.1680/bren.11.00046.

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17

Nigam, Utkarsh, i Rajneesh Sharma. "Rehabilitation of Buildings for Functional Unsuitability: Need of Structural Health Monitoring". International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (30.04.2018): 152–58. http://dx.doi.org/10.31142/ijtsrd10848.

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Bhadane, Pooja, Akanksha Mali i Mukta Fulse Jayashri Patil Prof S. B. Wagh. "Structural Health Monitoring SHM of Highway bridges using Wireless Sensor Network". International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (30.06.2018): 1216–21. http://dx.doi.org/10.31142/ijtsrd14167.

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19

Ozdagli, Ali, i Xenofon Koutsoukos. "Domain Adaptation for Structural Health Monitoring". Annual Conference of the PHM Society 12, nr 1 (3.11.2020): 9. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1184.

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In recent years, machine learning (ML) algorithms gained a lot of interest within structural health monitoring (SHM) community. Many of those approaches assume the training and test data come from similar distributions. However, real-world applications, where an ML model is trained on numerical simulation data and tested on experimental data, are deemed to fail in detecting the damage, as both domain data are collected under different conditions and they don’t share the same underlying features. This paper proposes the domain adaptation approach as a solution to particular SHM problems where the classifier has access to the labeled training (source) and unlabeled test (target) domains. The proposed domain adaptation method forms a feature space to match the latent features of both source and target domains. To evaluate the performance of this approach, we present a case study where we train three neural network-based classifiers on a three-story test structure: i) Classifier A uses labeled simulation data from the numerical model of the test structure; ii) Classifier B utilizes labeled experimental data from the test structure; and iii) Classifier C implements domain adaptation by training on labeled simulation data (source) and unlabeled experimental data (target). The performance of each classifier is evaluated by computing the accuracy of the discrimination against labeled experimental data. Overall, the results demonstrate that domain adaption can be regarded as a valid approach for SHM applications where access to labeled experimental data is limited.

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20

Kim, Yail J., Evangeline Murison i Aftab Mufti. "Structural health monitoring: a Canadian perspective". Proceedings of the Institution of Civil Engineers - Civil Engineering 163, nr 4 (listopad 2010): 185–91. http://dx.doi.org/10.1680/cien.2010.163.4.185.

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21

Abdelaal, A., A. Mosallam i M. Amin. "SENSORS USED IN STRUCTURAL HEALTH MONITORING". International Conference on Civil and Architecture Engineering 9, nr 9 (1.05.2012): 1–12. http://dx.doi.org/10.21608/iccae.2012.44256.

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22

Mosalam, Khalid, Sifat Muin i Yuqing Gao. "NEW DIRECTIONS IN STRUCTURAL HEALTH MONITORING". NED University Journal of Research 2, Special Issue on First SACEE'19 (15.06.2019): 77–112. http://dx.doi.org/10.35453/nedjr-stmech-2019-0006.

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This paper presents two on-going efforts of the Pacific Earthquake Engineering Research (PEER) center in the area of structural health monitoring. The first is data-driven damage assessment, which focuses on using data from instrumented buildings to compute the values of damage features. Using machine learning algorithms, these damage features are used for rapid identification of the level and location of damage after earthquakes. One of the damage features identified to be highly efficient is the cumulative absolute velocity. The second is vision-based automated damage identification and assessment from images. Deep learning techniques are used to conduct several identification tasks from images, examples of which are the structural component type, and level and type of damage. The objective is to use crowdsourcing, allowing the general public to take photographs of damage and upload them to a server where damage is automatically identified using deep learning algorithms. The paper also introduces PEER.s effort and preliminary results in engaging the engineering and computer science communities in such developments through the PEER Hub Image-Net (F-Net) challenge.

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23

Holford, Karen M. "Acoustic Emission in Structural Health Monitoring". Key Engineering Materials 413-414 (czerwiec 2009): 15–28. http://dx.doi.org/10.4028/www.scientific.net/kem.413-414.15.

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Structural Health Monitoring (SHM) is of paramount importance in an increasing number of applications, not only to ensure safety and reliability, but also to reduce NDT costs and to ensure timely maintenance of critical components. This paper overviews the modern applications of acoustic emission (AE), which has become established as a very powerful technique for monitoring damage in a variety of structures, and the new approaches that have enabled the successful application of the technique, leading to automated crack detection. Examples are drawn from a variety of industries to provide an insight into the current role of AE in structural health monitoring.

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24

YUAN, Shenfang, Jian WU, Weisong YE, Xia ZHAO i Xin XU. "On distributed structural health monitoring system". Journal of Advanced Science 18, nr 1/2 (2006): 131–39. http://dx.doi.org/10.2978/jsas.18.131.

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25

Wang, Xin, i Wei Bing Hu. "Structural Health Monitoring for Steel Structures". Applied Mechanics and Materials 351-352 (sierpień 2013): 1088–91. http://dx.doi.org/10.4028/www.scientific.net/amm.351-352.1088.

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The process of implementing a damage identification strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring. Many different types and degrees accidents take place, especially some important collapse accidents, the significance of steel structural health monitoring has been recognized. The introduction begins with a brief research status of steel structural health monitoring in china and the world. The paper analyzes the projects and contents of steel structures monitoring from nine aspects and summarizes the diagnosis methods of steel structural damages which include power fingerprint analysis, the methods of model correction and system identification, neural network methods, genetic algorithm and wavelet analysis, it provides us theoretical guidence. In conclusion, structural health monitoring for steel structures could reduce the impact of such disasters immediately after natural hazards and man-made disasters both economically and socially, thus it is becoming increasingly important.

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26

Friswell, Michael I., i John E. Mottershead. "Inverse Methods in Structural Health Monitoring". Key Engineering Materials 204-205 (kwiecień 2001): 201–10. http://dx.doi.org/10.4028/www.scientific.net/kem.204-205.201.

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27

Karuskevich, M., T. Maslak, Ie Gavrylov, Ł. Pejkowski i J. Seyda. "Structural health monitoring for light aircraft". Procedia Structural Integrity 36 (2022): 92–99. http://dx.doi.org/10.1016/j.prostr.2022.01.008.

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Hudec, Robert, Ladislav Janousek, Miroslav Benco, Pavol Makys, Vladimir Wieser, Martina Zachariasova, Matej Pacha, Vladimir Vavrus i Martin Vestenicky. "Structural Health Monitoring of Helicopter Fuselage". Communications - Scientific letters of the University of Zilina 15, nr 2 (30.06.2013): 95–101. http://dx.doi.org/10.26552/com.c.2013.2.95-101.

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Kusunoki, K. "Structural Health Monitoring for Building Structures". Concrete Journal 58, nr 9 (2020): 761–66. http://dx.doi.org/10.3151/coj.58.9_761.

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NAGAI, Nozomu, Akira MITA, Takahiro YAKOH i Tadanobu SATO. "Wireless Sensor for Structural Health Monitoring". Journal of JAEE 3, nr 4 (2003): 1–13. http://dx.doi.org/10.5610/jaee.3.4_1.

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31

Gamal, Ahmed, Adel ElSafty i Gerald Merckel. "New System of Structural Health Monitoring". Open Journal of Civil Engineering 03, nr 01 (2013): 19–28. http://dx.doi.org/10.4236/ojce.2013.31004.

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Maalej, Mohamed, Anestis Karasaridis, Stavroula Pantazopoulou i Dimitrios Hatzinakos. "Structural health monitoring of smart structures". Smart Materials and Structures 11, nr 4 (23.07.2002): 581–89. http://dx.doi.org/10.1088/0964-1726/11/4/314.

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Karale, Prof Ankita. "Iot based Structural Health Monitoring System". International Journal for Research in Applied Science and Engineering Technology 8, nr 5 (31.05.2020): 43–48. http://dx.doi.org/10.22214/ijraset.2020.5009.

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Cha, Young-Jin, Yeesock Kim i Taesun You. "Advanced Sensing and Structural Health Monitoring". Journal of Sensors 2018 (2018): 1–3. http://dx.doi.org/10.1155/2018/7286069.

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Dharap, Prasad, Bong-Hwan Koh i Satish Nagarajaiah. "Structural Health Monitoring using ARMarkov Observers". Journal of Intelligent Material Systems and Structures 17, nr 6 (czerwiec 2006): 469–81. http://dx.doi.org/10.1177/1045389x06058793.

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Joshi, Shiv, i Kara Peters. "SMASIS Symposium on Structural Health Monitoring". Journal of Intelligent Material Systems and Structures 21, nr 3 (luty 2010): 223. http://dx.doi.org/10.1177/1045389x09359109.

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Stoilov, G., D. Pashkouleva i V. Kavardzhikov. "Smartphone application for structural health monitoring". IOP Conference Series: Materials Science and Engineering 951 (3.11.2020): 012026. http://dx.doi.org/10.1088/1757-899x/951/1/012026.

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Krüger, Markus, Christian U. Grosse i Pedro José Marrón. "Wireless Structural Health Monitoring Using MEMS". Key Engineering Materials 293-294 (wrzesień 2005): 625–34. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.625.

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So far, the inspection of building structures and especially of bridges is mainly done visually. Therefore, the condition of the structure is examined from the surface and the interpretation and assessment is based on the experience of the expert. However, the main purpose of monitoring civil structures is not to substitute visual inspection. Continuous structural health monitoring should provide data from the inside of a structure to better understand its structural performance and to predict its durability and remaining life time. Monitoring should render objective data and observable alterations in the structure continuously, which cannot be done by visual inspection. More detailed information is needed with respect to different exposure due to dynamic and static loads and also temperature and moisture. Today mainly wired monitoring systems are used to monitor structures, which are relatively expensive and time consuming to install. In this paper the basic principle of a wireless monitoring system equipped with MEMS sensors is presented, which can be easily installed at different structures. Microelectromechanical systems (MEMS) are small integrated devices or systems that combine electrical and mechanical components. A wireless monitoring sensor network equipped with such MEMS could be produced with a very low budget and becomes very efficient. This permits a wide area of applications not only in civil engineering. With respect to different applications relevant properties of a wireless monitoring system are described. In detail network configuration, power consumption, data acquisition and data aggregation, signal analysis and data reduction as well as reliability and robustness are discussed.

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39

Brownjohn, J. M. W. "Structural health monitoring of civil infrastructure". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365, nr 1851 (13.12.2006): 589–622. http://dx.doi.org/10.1098/rsta.2006.1925.

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Structural health monitoring (SHM) is a term increasingly used in the last decade to describe a range of systems implemented on full-scale civil infrastructures and whose purposes are to assist and inform operators about continued ‘fitness for purpose’ of structures under gradual or sudden changes to their state, to learn about either or both of the load and response mechanisms. Arguably, various forms of SHM have been employed in civil infrastructure for at least half a century, but it is only in the last decade or two that computer-based systems are being designed for the purpose of assisting owners/operators of ageing infrastructure with timely information for their continued safe and economic operation. This paper describes the motivations for and recent history of SHM applications to various forms of civil infrastructure and provides case studies on specific types of structure. It ends with a discussion of the present state-of-the-art and future developments in terms of instrumentation, data acquisition, communication systems and data mining and presentation procedures for diagnosis of infrastructural ‘health’.

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Farrar, Charles R., i Keith Worden. "An introduction to structural health monitoring". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365, nr 1851 (12.12.2006): 303–15. http://dx.doi.org/10.1098/rsta.2006.1928.

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The process of implementing a damage identification strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). Here, damage is defined as changes to the material and/or geometric properties of these systems, including changes to the boundary conditions and system connectivity, which adversely affect the system's performance. A wide variety of highly effective local non-destructive evaluation tools are available for such monitoring. However, the majority of SHM research conducted over the last 30 years has attempted to identify damage in structures on a more global basis. The past 10 years have seen a rapid increase in the amount of research related to SHM as quantified by the significant escalation in papers published on this subject. The increased interest in SHM and its associated potential for significant life-safety and economic benefits has motivated the need for this theme issue. This introduction begins with a brief history of SHM technology development. Recent research has begun to recognize that the SHM problem is fundamentally one of the statistical pattern recognition (SPR) and a paradigm to address such a problem is described in detail herein as it forms the basis for organization of this theme issue. In the process of providing the historical overview and summarizing the SPR paradigm, the subsequent articles in this theme issue are cited in an effort to show how they fit into this overview of SHM. In conclusion, technical challenges that must be addressed if SHM is to gain wider application are discussed in a general manner.

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Klikowicz, Piotr, Marek Salamak i Grzegorz Poprawa. "Structural Health Monitoring of Urban Structures". Procedia Engineering 161 (2016): 958–62. http://dx.doi.org/10.1016/j.proeng.2016.08.833.

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Trifunac, M. D., i M. Ebrahimian. "Detection thresholds in structural health monitoring". Soil Dynamics and Earthquake Engineering 66 (listopad 2014): 319–38. http://dx.doi.org/10.1016/j.soildyn.2014.07.014.

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Alampalli, Sreenivas, Mohammed M. Ettouney i Anil K. Agrawal. "Structural health monitoring for bridge maintenance". Bridge Structures 1, nr 3 (wrzesień 2005): 345–54. http://dx.doi.org/10.1080/15732480500252751.

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TODOROKI, Akira. "Structural Health Monitoring for Practical Use". Reference Collection of Annual Meeting VIII.03.1 (2003): 124–25. http://dx.doi.org/10.1299/jsmemecjsm.viii.03.1.0_124.

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Crivelli, Davide, i Stewart Bland. "Structural health monitoring via acoustic emission". Reinforced Plastics 60, nr 6 (listopad 2016): 390–92. http://dx.doi.org/10.1016/j.repl.2015.05.004.

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Forrest, Chad, Clint Forrest i Doug Wiser. "Landing Gear Structural Health Monitoring (SHM)". Procedia Structural Integrity 5 (2017): 1153–59. http://dx.doi.org/10.1016/j.prostr.2017.07.025.

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Friswell, M. I., i S. Adhikari. "Structural health monitoring using shaped sensors". Mechanical Systems and Signal Processing 24, nr 3 (kwiecień 2010): 623–35. http://dx.doi.org/10.1016/j.ymssp.2009.10.009.

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Comisu, Cristian-Claudiu, Nicolae Taranu, Gheorghita Boaca i Maria-Cristina Scutaru. "Structural health monitoring system of bridges". Procedia Engineering 199 (2017): 2054–59. http://dx.doi.org/10.1016/j.proeng.2017.09.472.

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Moreno-Gomez, Alejandro, Carlos A. Perez-Ramirez, Aurelio Dominguez-Gonzalez, Martin Valtierra-Rodriguez, Omar Chavez-Alegria i Juan P. Amezquita-Sanchez. "Sensors Used in Structural Health Monitoring". Archives of Computational Methods in Engineering 25, nr 4 (17.03.2017): 901–18. http://dx.doi.org/10.1007/s11831-017-9217-4.

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Qi, G. Z., Guo Xun, Qi Xiaozhai, W. Dong i P. Chang. "Local measurement for structural health monitoring". Earthquake Engineering and Engineering Vibration 4, nr 1 (czerwiec 2005): 165–72. http://dx.doi.org/10.1007/s11803-005-0034-7.

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