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Journal articles on the topic 'Structural monitoring'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 March 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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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12

Goulet, James-A., Prakash Kripakaran, and Ian F. C. Smith. "Multimodel Structural Performance Monitoring." Journal of Structural Engineering 136, no. 10 (October 2010): 1309–18. http://dx.doi.org/10.1061/(asce)st.1943-541x.0000232.

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13

Chung, D. D. L. "Self-monitoring structural materials." Materials Science and Engineering: R: Reports 22, no. 2 (March 1998): 57–78. http://dx.doi.org/10.1016/s0927-796x(97)00021-1.

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14

Pozo, Francesc, Diego A. Tibaduiza, and Yolanda Vidal. "Sensors for Structural Health Monitoring and Condition Monitoring." Sensors 21, no. 5 (February 24, 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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15

Silva, Ignacio Javier González, and Raid Karoumi. "Traffic monitoring using a structural health monitoring system." Proceedings of the Institution of Civil Engineers - Bridge Engineering 168, no. 1 (March 2015): 13–23. http://dx.doi.org/10.1680/bren.11.00046.

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16

de Leeuw, B., and F. P. Brennan. "Structural integrity monitoring index (SIMdex): A methodology for assessing structural health monitoring technologies." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 223, no. 5 (May 1, 2009): 515–23. http://dx.doi.org/10.1243/09544100jaero392.

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Recent years have seen enormous activity in the development of structural integrity monitoring/structural health monitoring equipment and systems. Systems based on technology that could in the past have only been used under laboratory conditions are now frequently deployed in the field very often claiming accuracy and reliability commensurate with laboratory measurements. Monitoring is certainly an exciting prospect and has many advantages over traditional NDT; there are, however, some very fundamental issues that must be resolved to benefit fully from these new technologies. Not least of these is the development of objective measures to quantitatively assess the performance characteristics of monitoring technologies. This article presents the background and development of such a measure of performance based on fatigue and fracture mechanics failure models of the host structure. This new measure, the structural integrity monitoring index or SIMdex, can be similarly applied using any failure model and criterion and means that structural integrity monitoring technologies can be objectively judged solely on their suitability for specific applications.

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17

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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18

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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19

Petersen, DR, and YL Mo. "Static Structural Integrity Monitoring System." Journal of Testing and Evaluation 21, no. 5 (1993): 453. http://dx.doi.org/10.1520/jte11790j.

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20

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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21

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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22

Rivera, E., A. A. Mufti, and D. J. Thomson. "Civionics for structural health monitoring." Canadian Journal of Civil Engineering 34, no. 3 (March 1, 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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23

Arangio, S., F. Bontempi, and M. Ciampoli. "Structural integrity monitoring for dependability." Structure and Infrastructure Engineering 7, no. 1-2 (January 2011): 75–86. http://dx.doi.org/10.1080/15732471003588387.

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24

Cobianu, Cornel. "SYSTEM FOR MONITORING STRUCTURAL ASSETS." Journal of the Acoustical Society of America 135, no. 1 (2014): 570. http://dx.doi.org/10.1121/1.4861495.

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25

Shrive, P. L., T. G. Brown, and N. G. Shrive. "Practicalities of structural health monitoring." Smart Structures and Systems 5, no. 4 (July 25, 2009): 357–67. http://dx.doi.org/10.12989/sss.2009.5.4.357.

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26

Sumitro, S., and M. L. Wang. "Sustainable structural health monitoring system." Structural Control and Health Monitoring 12, no. 3-4 (2005): 445–67. http://dx.doi.org/10.1002/stc.79.

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27

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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28

Giordano, Pier Francesco, Said Quqa, and Maria Pina Limongelli. "The value of monitoring a structural health monitoring system." Structural Safety 100 (January 2023): 102280. http://dx.doi.org/10.1016/j.strusafe.2022.102280.

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29

GOLOB, Tea, and Matej MAKAROVIČ. "WORK INTEGRATION SOCIAL ENTREPRENEURSHIP IN EAST-CENTRAL EUROPE THROUGH STRUCTURAL AND SEMIOTIC TRANSFORMATIONS." Monitoring of public opinion economic&social changes, no. 5 (November 10, 2018): 0. http://dx.doi.org/10.14515/monitoring.2018.5.18.

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The central question addressed is how the structural and semiotic contexts, seen from the perspective of the cultural political economy, of the selected post-communist societies of East-Central Europe (Croatia, Czech Republic, Slovenia, and Poland) have affected the historical development and contemporary situation of social entrepreneurship focused on the integration of the disadvantaged social groups in the labour market (work integration social enterprises (WISEs)). Based on secondary data, surveys, semi-structured interviews and focus groups with the stakeholders from the transnational project INNO WISEs, we identify both the communist and post-communist transformations as mostly unfavourable for WISE, while the crucial factor contributing to their selection as a viable option after 2004 has been the external impact of the European Union-related structures and discourses.

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30

Xia, Zhiming, Pengjiang Guo, and Wenzhi Zhao. "CUSUM Methods for Monitoring Structural Changes in Structural Equations." Communications in Statistics - Theory and Methods 40, no. 6 (January 21, 2011): 1109–23. http://dx.doi.org/10.1080/03610920903537285.

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31

Nigam, Utkarsh, and 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 (April 30, 2018): 152–58. http://dx.doi.org/10.31142/ijtsrd10848.

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32

Bhadane, Pooja, Akanksha Mali, and 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 (June 30, 2018): 1216–21. http://dx.doi.org/10.31142/ijtsrd14167.

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33

Vanniamparambil, Prashanth Abraham, Ivan Bartoli, Kavan Hazeli, Jefferson Cuadra, Eric Schwartz, Raghavendra Saralaya, and Antonios Kontsos. "An integrated structural health monitoring approach for crack growth monitoring." Journal of Intelligent Material Systems and Structures 23, no. 14 (June 2012): 1563–73. http://dx.doi.org/10.1177/1045389x12447987.

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34

Ozdagli, Ali, and Xenofon Koutsoukos. "Domain Adaptation for Structural Health Monitoring." Annual Conference of the PHM Society 12, no. 1 (November 3, 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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35

Kim, Yail J., Evangeline Murison, and Aftab Mufti. "Structural health monitoring: a Canadian perspective." Proceedings of the Institution of Civil Engineers - Civil Engineering 163, no. 4 (November 2010): 185–91. http://dx.doi.org/10.1680/cien.2010.163.4.185.

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36

Abdelaal, A., A. Mosallam, and M. Amin. "SENSORS USED IN STRUCTURAL HEALTH MONITORING." International Conference on Civil and Architecture Engineering 9, no. 9 (May 1, 2012): 1–12. http://dx.doi.org/10.21608/iccae.2012.44256.

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37

Constantin, Nicolae, Alexandrina Mihai, Mircea Găvan, Ştefan Sorohan, Constantin Dumitraşcu, and Viorel Anghel. "Structural Integrity Monitoring of Composite Pipes." Key Engineering Materials 347 (September 2007): 615–20. http://dx.doi.org/10.4028/www.scientific.net/kem.347.615.

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Composite pipes enjoy increasing interest in the sector of petroleum and gas transportation, due to a number of qualities, concerning especially the corrosion resistance and light weight, face to the traditional steel pipes. As composite materials are prone to a various range of defects and damages which can seriously affect their service ability, reliable inspection methods have to be tested in order to assure the required in service reliability. The paper presents progress made in applying complementary global/local non-destructive inspection (NDI) methods, such as Lamb wave method and infrared thermography (IRT) method, to effective structural health (SHM) monitoring of composite pipes.

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38

Mosalam, Khalid, Sifat Muin, and Yuqing Gao. "NEW DIRECTIONS IN STRUCTURAL HEALTH MONITORING." NED University Journal of Research 2, Special Issue on First SACEE'19 (June 15, 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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39

Holford, Karen M. "Acoustic Emission in Structural Health Monitoring." Key Engineering Materials 413-414 (June 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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40

YUAN, Shenfang, Jian WU, Weisong YE, Xia ZHAO, and Xin XU. "On distributed structural health monitoring system." Journal of Advanced Science 18, no. 1/2 (2006): 131–39. http://dx.doi.org/10.2978/jsas.18.131.

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41

Wang, Xin, and Wei Bing Hu. "Structural Health Monitoring for Steel Structures." Applied Mechanics and Materials 351-352 (August 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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42

MENZIES, J. "BRIEFING. STRUCTURAL SAFETY - A MONITORING PROCESS." Proceedings of the Institution of Civil Engineers - Civil Engineering 92, no. 4 (November 1992): 150–52. http://dx.doi.org/10.1680/icien.1992.21495.

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43

Cammarata, M., D. Dutta, Hoon Sohn, P. Rizzo, and Kent A. Harries. "Advanced Ultrasonic Structural Monitoring of Waveguides." Advances in Science and Technology 56 (September 2008): 477–82. http://dx.doi.org/10.4028/www.scientific.net/ast.56.477.

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Ultrasonic Guided Waves (UGWs) are a useful tool in those structural health monitoring applications that can benefit from built-in transduction, moderately large inspection ranges and high sensitivity to small flaws. This paper describes two methods, based on linear and nonlinear acoustics for structural damage detection based on UGWs. The linear method combine the advantages of UGW inspection with the outcomes of the Discrete Wavelet Transform (DWT) that is used for extracting defect-sensitive features that can be combined to perform a multivariate diagnosis of damage. In particular, the DWT is exploited to generate a set of relevant wavelet coefficients to construct a uni-dimensional or multi-dimensional damage index that, in turn is fed to an outlier algorithm to detect anomalous structural states. The nonlinear acoustics method exploits the circumstance that a cracked medium exhibits high acoustic nonlinearity which is manifested as harmonics in the power spectrum of the received signal. Experimental results also indicate that the harmonic components increase non-linearly in magnitude with increasing amplitude of the input signal. The proposed nonlinear technique identifies the presence of cracks by looking at the harmonics and their nonlinear relationship to the input amplitude. The general framework presented in this paper is applied to the detection of fatigue cracks in an I-shaped steel beam. The probing hardware consists of Lead Zirconate Titanate (PZT) materials used for both ultrasound generation and detection at chosen frequency. The effectiveness of the proposed methods for the structural diagnosis of defects that are small compared to the waveguide cross-sectional area is discussed.

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44

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

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45

Karuskevich, M., T. Maslak, Ie Gavrylov, Ł. Pejkowski, and 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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46

Hudec, Robert, Ladislav Janousek, Miroslav Benco, Pavol Makys, Vladimir Wieser, Martina Zachariasova, Matej Pacha, Vladimir Vavrus, and Martin Vestenicky. "Structural Health Monitoring of Helicopter Fuselage." Communications - Scientific letters of the University of Zilina 15, no. 2 (June 30, 2013): 95–101. http://dx.doi.org/10.26552/com.c.2013.2.95-101.

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47

Kusunoki, K. "Structural Health Monitoring for Building Structures." Concrete Journal 58, no. 9 (2020): 761–66. http://dx.doi.org/10.3151/coj.58.9_761.

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48

NAGAI, Nozomu, Akira MITA, Takahiro YAKOH, and Tadanobu SATO. "Wireless Sensor for Structural Health Monitoring." Journal of JAEE 3, no. 4 (2003): 1–13. http://dx.doi.org/10.5610/jaee.3.4_1.

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49

MURAYAMA, Hideaki. "Structural Monitoring Using Fiber-Optic Sensors." Journal of the Japan Welding Society 74, no. 4 (2005): 185–88. http://dx.doi.org/10.2207/qjjws1943.74.185.

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50

Gamal, Ahmed, Adel ElSafty, and Gerald Merckel. "New System of Structural Health Monitoring." Open Journal of Civil Engineering 03, no. 01 (2013): 19–28. http://dx.doi.org/10.4236/ojce.2013.31004.

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