تعداد نشریات | 161 |
تعداد شمارهها | 6,532 |
تعداد مقالات | 70,500 |
تعداد مشاهده مقاله | 124,089,578 |
تعداد دریافت فایل اصل مقاله | 97,192,926 |
A New Damage Detection Approach Under Variable Environmental or Operational Conditions | ||
Civil Engineering Infrastructures Journal | ||
دوره 56، شماره 1، شهریور 2023، صفحه 33-49 اصل مقاله (708.35 K) | ||
نوع مقاله: Research Papers | ||
شناسه دیجیتال (DOI): 10.22059/ceij.2022.330606.1790 | ||
نویسندگان | ||
Fahimeh Jalalifar1؛ M. R. Esfahani* 2؛ Farzad Shahabian Moghadam2 | ||
1Ph.D. Candidate, Civil Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran. | ||
2Professor, Civil Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran. | ||
چکیده | ||
The basic idea of vibration-based damage identification approaches is that damage causes change in vibration response of structure. So monitoring the vibration response characteristics can be helpful in damage detection. The main limitation in such methods is that these characteristics are also affected by the Environmental and Operational Variability (EOV) that can be incorrectly known as structural damage or sometimes cover actual damages. This paper aims to propose an innovative approach to detect and locate damage considering the EOV conditions. In this regard, an Independent Component Analysis (ICA) based Blind Source Separation (BSS) approach is employed to remove the EOV influences from the time history response of the structure. The beneficial of using the ICA-based BSS method is that there is no need to measure the environmental/operational conditions. Moreover, it is able to remove EOV influences using a limited group of response data monitored during different environmental and operational conditions. Time series analysis is then performed to extract damage-sensitive features. Finally, a statistical tool is employed to damage identification and localization by using EOV independent features. Two recognized benchmark structures are employed for verifying the accuracy of the proposed approach. Results indicate that the proposed method is a time-saving tool and efficiently successful in damage assessment of structures under EOV. | ||
کلیدواژهها | ||
Bhattacharyya Measure؛ Blind Source Separation؛ Damage Detection؛ Environmental and Operational Variability؛ Time Series Analysis | ||
مراجع | ||
Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M. and Inman, D.J. (2021). "A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications", Mechanical Systems and Signal Processing, 147, (15 January), 107077, https://doi.org/10.1016/j.ymssp.2020.107077.
Bayraktar, A., Altunişik, A.C., Sevim, B. and Özşahin, T. (2014). "Environmental effects on the dynamic characteristics of the Gülburnu Highway Bridge", Civil Engineering and Environmental Systems, 31(4), 347-366, https://doi.org/10.1080/10286608.2014.916697.
Box, G.E., Jenkins, G.M., Reinsel, G.C. and Ljung, G.M. (2015). Time series analysis: Forecasting and control, John Wiley & Sons.
Cai, Y., Zhang, K., Ye, Zh. Liu, Ch., Lu, K. and Wang, L. (2021). "Influence of temperature on the natural vibration characteristics of simply Supported reinforced concrete beam", Sensors, 21, 21(12(, 4242, https://doi.org/10.3390/s21124242.
Cardoso, J.F. (1999). "High-order contrasts for independent component analysis", Neural Computation, 11(1), 157-192, https://doi.org/10.1162/089976699300016863.
Cardoso, J.F. and Souloumiac, A. (1993). "Blind beamforming for non-Gaussian signals", IEE Proceedings F (Radar and Signal Processing), 140(6), 362-370, https://doi.org/10.1049/ip-f-2.1993.0054.
Chen, S-F., Hung, T-Y and Loh, C-H. (2015). "Analysis of traffic-induced vibration and damage detection by blind source separation with application to bridge monitoring", Proceedings of SPIE 9435, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, California, United States, https://doi.org/10.1117/12.2084084.
Comanducci, G., Magalhães, F., Ubertini, F. and Cunha, Á. (2016). "On vibration-based damage detection by multivariate statistical techniques: Application to a long-span arch bridge", Structural Health Monitoring, 15(5), 505-524, https://doi.org/10.1177/1475921716650630.
Cross, E. (2012). "On structural health monitoring in changing environmental and operational conditions", PhD Thesis, University of Sheffield.
Cross, E.J., Koo, K.Y., Brownjohn, J.M.W. and Worden, K. (2013). "Long-term monitoring and data analysis of the Tamar Bridge", Mechanical Systems and Signal Processing, 35(1-2), 16-34, https://doi.org/10.1016/j.ymssp.2012.08.026.
Cunha, A., Caetano, E., Moutinho, C. and Magalhães, F. (2019). "Continuous dynamic monitoring program of large civil infrastructures", Proceedings of the 7th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COMPDYN 2015), 13-47, Crete Island, Greece, https://doi.org/10.7712/120119.6901.20156.
Datteo, A, and Lucà, F. (2017). "Statistical pattern recognition approach for long-time monitoring of the G. Meazza stadium by means of AR models and PCA", Engineering Structures, 153, (15 December), 317-333, https://doi.org/10.1016/j.engstruct.2017.10.022..
Daneshvar, M.H, Gharighoran, A., Zareei, S.A. and Karamodin, A. (2021). "Structural health monitoring using high-dimensional features from time series modeling by innovative hybrid distance-based methods", Journal of Civil Structural Health Monitoring, 11(2), 537-557, https://doi.org/10.1007/s13349-020-00466-5.
Dervilis, N., Worden, K. and Cross, E. (2015). "On robust regression analysis as a means of exploring environmental and operational conditions for SHM data", Journal of Sound and Vibration, 347(7 July), 279-96, https://doi.org/10.1016/j.jsv.2015.02.039.
Entezami A. (2021), Structural health monitoring by time series analysis and statistical distance measures, Cham: Springer International Publishing, https://doi.org/10.1007/978-3-030-66259-2.
Entezami, A. and Shariatmadar, H. (2019). "Structural health monitoring by a new hybrid feature extraction and dynamic time warping methods under ambient vibration and non-stationary signal", Measurement, 134(February), 548-568, https://doi.org/10.1016/j.measurement.2018.10.095.
Entezami, A., Shariatmadar, H. and Karamodin, A. (2019). "Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods", Structural Health Monitoring, 18(5-6), 1416-1443, https://doi.org/10.1177/1475921718800306.
Guo, Y., and Kareem, A., (2016). "System identification through nonstationary data using time-frequency blind source separation", Journal of Sound and Vibration, 371(February), 110-131, https://doi.org/10.1016/j.jsv.2016.02.011.
Hu, W-H., Tang, D-H., Teng, J., Said, S. and Rohrmann, R.G. (2018). "Structural health monitoring of a prestressed concrete bridge based on statistical pattern recognition of continuous dynamic measurements over 14 years", Sensors, 18(12), 4117, https://doi.org/10.3390/s18124117.
Jain, S.N. and Rai, C. (2012). "Blind source separation and ICA techniques: A review", International Journal of Engineering Science and Technology, 4(4), 1490-1503, https://doi.org/10.1109/CISES54857.2022.9844373.
Jiang, S., Lin, P., Chen, Y., Tian, Ch. and Li, Y. (2019). "Mixed-signal extraction and recognition of wind turbine blade multiple-area damage based on improved Fast-ICA", Optik, 179(February), 1152-1159, https://doi.org/10.1016/j.ijleo.2018.10.137.
Kordi, A., and Mahmoudi, M. (2022). "Damage detection in truss bridges under moving load using time history response and members influence line curves", Civil Engineering Infrastructures Journal, 55(1), 183-194, https://doi.org/10.22059/ceij.2021.314109.1723.
Kullaa, J. (2011). "Distinguishing between sensor fault, structural damage, and environmental or operational effects in structural health monitoring", Mechanical Systems and Signal Processing, 25(8), 2976-2989, https://doi.org/10.1016/j.ymssp.2011.05.017.
Kullaa, J. (2014). "Benchmark data for structural health monitoring", Proceedings of the EWSHM, 7th European Workshop on Structural Health Monitoring, Nantes, France, https://inria.hal.science/hal-01021056.
Kullaa, J. (2020). "Robust damage detection in the time domain using Bayesian virtual sensing with noise reduction and environmental effect elimination capabilities", Journal of Sound and Vibration, 473, (12 May), 115232, https://doi.org/10.1016/j.jsv.2020.115232.
Limongelli, M.P., Manoach, E., Quqa, S., Giordano, P.F., Bhowmik, B., Pakrashi, V. and Cigada, F. (2021). "Vibration response-based damage detection", In: Sause, M.G.R., Jasiūnienė, E. (eds), Structural Health Monitoring Damage Detection Systems for Aerospace, Springer Aerospace Technology, Springer, Cham, https://doi.org/10.1007/978-3-030-72192-3_6.
Ljung, L. (1999). System identification: Theory for the user, PTR Prentice Hall, Upper Saddle River.
Nguyen, V., Mahowald, J., Schommer, S., Maas, S. and Zuerbes, A. (2017). "A study of temperature and aging effects on Eigenfrequencies of concrete bridges for health monitoring", Engineering, 9(5), 396-411, https://doi.org/10.4236/eng.2017.95023.
Rainieri, C., Magalhaes, F., Gargaro, D., Fabbrocino, G. and Cunha, A. (2019). "Predicting the variability of natural frequencies and its causes by Second-Order Blind Identification", Structural Health Monitoring, 18(2), 486-507, https://doi.org/10.1177/1475921718758629.
Razavi, B.S., Mahmoudkelayeh, M.R. and Razavi, S.S. (2021). "Damage identification under ambient vibration and unpredictable signal nature", Journal of Civil Structural Health Monitoring, 11(5), 1253-1273, https://doi.org/10.1007/s13349-021-00503-x.
Roy, K., Bhattacharya, B. and Ray-Chaudhuri, S. (2015). "ARX model-based damage sensitive features for structural damage localization using output-only measurements", Journal of Sound and Vibration, 349(4 August), 99-122, https://doi.org/10.1016/j.jsv.2015.03.038..
Sadhu, A. and Hazra, B. (2013). “A novel damage detection algorithm using time-series analysis-based blind source separation” Shock and Vibration, 20(3), 423-438, https://doi.org/10.3233/SAV-120759.
Sadhu, A., Narasimhan, S. and Antoni, J. (2017). "A review of output-only structural mode identification literature employing blind source separation methods", Mechanical Systems and Signal Processing, 94(15 September), 415-431, https://doi.org/10.1016/j.ymssp.2017.03.001.
Shan, W., Wang, X. and Jiao, Y. (2018). "Modeling of temperature effect on modal frequency of concrete beam based on field monitoring data", Shock and Vibration, 2018, Article ID. 8072843, 1-13, https://doi.org/10.1155/2018/8072843.
Sohn, H. and Farrar, C.R. (2001). "Damage diagnosis using time series analysis of vibration signals", Smart Materials and Structures, 10(3), 446, https://doi.org/10.1088/0964-1726/10/3/304.
Spiridonakos, M.D, Chatzi, E.N. and Sudret, B. (2016). "Polynomial Chaos Expansion models for the monitoring of structures under operational variability", ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 2(3), B4016003, https://doi.org/10.1061/AJRUA6.0000872.
Vamvoudakis-Stefanou, K., Sakellariou, J. and Fassois, S. (2018). "Vibration-based damage detection for a population of nominally identical structures: Unsupervised Multiple Model (MM) statistical time series type methods", Mechanical Systems and Signal Processing, 111(October), 149-171, https://doi.org/10.1016/j.ymssp.2018.03.054.
Wang, K., Hao, Q., Zhang, X., Tang, Z., Wang, Y. and Shen, Y. (2020). "Blind source extraction of acoustic emission signals for rail cracks based on ensemble empirical mode decomposition and constrained independent component analysis", Measurement, 157(June), 107653, https://doi.org/10.1016/j.measurement.2020.107653.
Yu, G. (2019). "An underdetermined blind source separation method with application to modal identification", Shock and Vibration, 2019, Article ID. 1637163, https://doi.org/10.1155/2019/1637163.
Zhang, W., Li, C., Peng, G., Chen, Y. and Zhang, Z. (2018). "A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load", Mechanical System and Signal Processing, 100(1 February), 439-453, https://doi.org/10.1016/j.ymssp.2017.06.022.
Zhang, X., Li, D. and Song, G. (2018). “Structure damage identification based on regularized ARMA time series model under environmental excitation”, Vibration, 1(1), 138-156, https://doi.org/10.3390/vibration1010011. | ||
آمار تعداد مشاهده مقاله: 269 تعداد دریافت فایل اصل مقاله: 710 |