پیوندهای مفید
آمار
| تعداد نشریات | 7 |
| تعداد شماره‌ها | 404 |
| تعداد مقالات | 5,423 |
| تعداد مشاهده مقاله | 5,528,038 |
| تعداد دریافت فایل اصل مقاله | 5,023,881 |
تشخیص خرابی در پل کابلی مبتنی بر ویژگیهای مود تجربی مؤثر در تبدیل موجک تجربی | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 24، دوره 53، شماره 3، خرداد 1400، صفحه 1201-1222 اصل مقاله (2.09 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2020.16849.6368 | ||
| نویسندگان | ||
| حسین باباجانیان بیشه1؛ غلامرضا قدرتی امیری* 2؛ مسعود نکویی3؛ احسان درویشان4 | ||
| 1دانشجوی دکتری مهندسی عمران، گروه عمران، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران | ||
| 2استاد، دانشکده مهندسی عمران، دانشگاه علم و صنعت ایران، تهران، ایران | ||
| 3استادیار، گروه عمران، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران | ||
| 4استادیار، گروه عمران، واحد رودهن، دانشگاه آزاد اسلامی، رودهن، ایران | ||
| چکیده | ||
| شناسایی هوشمندانه خسارت در سازههای زیربنایی از جمله پلها به منظور بهبود عملکرد پیشبینی آسیب و کاهش هزینه نگهداری ضروری است. بنابراین، توسعه روشهای کارآمد برای تشخیص به موقع آسیب در سازه و تصمیمگیری نسبت به تعمیر آن از اهمیت زیادی برخوردار است. در این مقاله، یک روش جدید مبتنی بر ویژگیهای باند فرکانسی مؤثر در تحلیل تبدیل موجک تجربی به منظور تشخیص آسیب در پل کابلی ارائه شد که شامل دو بخش است: (1) پردازش سیگنال و استخراج ویژگی، (2) تشخیص آسیب با استفاده از ترکیب ویژگیهای مؤثر. در بخش اول، دادههای پاسخ سازه با استفاده از تبدیل موجک تجربی به مودهای تجربی تجزیه گردید و مجموعهای از ویژگیها به عنوان مشخصه حساس به خرابی از طیف فرکانس مودها استخراج شد. به منظور ارزیابی نتایج از طبقهبند ماشین بردار پشتیبان استفاده گردید. در بخش دوم، برای کاهش خطای الگوریتم شناسایی آسیب، با استفاده از روشهای انتخاب ویژگی، یک زیرمجموعه بهینه حاوی اطلاعات مربوط به آسیب سازهای شامل ترکیبی از ویژگیهای مهم به عنوان شاخص خرابی تعیین گردید. برای اعتبارسنجی روش پیشنهادی از دادههای پاسخ پل کابلی یونگ استفاده شد. نتایج نشان داد که دومین و سومین مود تجربی حاصل از تحلیل موجک تجربی حاوی اطلاعات مرتبط با خرابی بوده و بکارگیری طیف فرکانسی متناظر آن در فرآیند استخراج ویژگی، عملکرد شناسایی را نسبت به روشهای متداول حدود 5 درصد بهبود میدهد. همچنین استفاده از ترکیب ویژگیهای مؤثر بجای یک ویژگی تنها، دقت شناسایی را به حدود 94 درصد افزایش میدهد. | ||
| کلیدواژه‌ها | ||
| شناسایی آسیب؛ تبدیل موجک تجربی؛ انتخاب ویژگی؛ پل کابلی؛ استخراج ویژگی | ||
| موضوعات | ||
| پایش سلامت سازه ها | ||
| عنوان مقاله [English] | ||
| Damage Assessment of a Cable-Stayed Bridge Based on Effective Empirical Mode Features using Empirical Wavelet Transform | ||
| نویسندگان [English] | ||
| Hossein Babajanian Bisheh1؛ Gholamreza Ghodrati Amiri2؛ Masoud Nekooei3؛ Ehsan Darvishan4 | ||
| 1PhD Student, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 2Professor, Center of Excellence for Fundamental Studies in Structural Engineering, School of Civil Engineering, Iran University of Science & Technology, Tehran, Iran | ||
| 3Assistant Professor, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 4Assistant Professor, Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran | ||
| چکیده [English] | ||
| Intelligent damage detection of civil infrastructures is vital to improve damage prediction performance and reduce maintenance costs. Therefore, the development of efficient techniques for detecting structural damages in an early stage is extremely important to support making decisions on structure repair. In this paper, a new damage detection method based on effective frequency band with empirical wavelet transform for a cable-stayed bridge was proposed which consists of two stages: (1) signal processing and feature extraction, (2) damage identification by combining effective features. In the first stage, structural response data was decomposed into empirical modes using empirical wavelet transform to obtain the component related to structural damage, and a set of features as damage-sensitive features were extracted from the frequency spectrum of modes. A support vector machine was applied to evaluate the results. In the second stage, by applying feature selection methods, an optimal subset of features that carries the most significant information about the structural damage was obtained as a damage index. Next, it was used in the feature extraction process. To verify the proposed damage detection method, response data obtained from a cable-stayed bridge, the Yonghe Bridge, was employed. Results showed that the second and third empirical modes obtained from the empirical wavelet analysis contain fault information and using its corresponding frequency spectrum in the feature extraction process improves detection performance by about 5% compared to conventional methods. It also increases the detection accuracy to about 94% by employing effective feature combinations rather than a single feature. | ||
| کلیدواژه‌ها [English] | ||
| Damage detection, Empirical wavelet transform, Feature selection, Cable-stayed bridge, Feature extraction | ||
| مراجع | ||
|
[1] M. Radzieński, Ł. Doliński, M. Krawczuk, M. Palacz, Damage localisation in a stiffened plate structure using a propagating wave, Mechanical Systems and Signal Processing, 39(1-2) (2013) 388-395. [2] N.T.S. Board, U.S.N.T.S. Board, Collapse of I-35W Highway Bridge, Minneapolis, Minnesota, August 1, 2007, Createspace Independent Pub, 2008. [3] M. Kunishima, Collapse of the Korea seoul seongsu bridge JST Failure Knowledge Database/100 Selected Cases, (1994). [4] T.D. Stark, R. Benekohal, L.A. Fahnestock, J.M. LaFave, J. He, C. Wittenkeller, I-5 Skagit River bridge collapse review, Journal of performance of constructed facilities, 30(6) (2016) 04016061. [5] B. Dawson, Vibration condition monitoring techniques for rotating machinery, The shock and vibration digest, 8(12) (1976) 3. [6] M.J. Whelan, M.V. Gangone, K.D. Janoyan, R. Jha, Real-time wireless vibration monitoring for operational modal analysis of an integral abutment highway bridge, Engineering Structures, 31(10) (2009) 2224-2235. [7] D. Goyal, B. Pabla, The vibration monitoring methods and signal processing techniques for structural health monitoring: a review, Archives of Computational Methods in Engineering, 23(4) (2016) 585-594. [8] J.P. Amezquita-Sanchez, H. Adeli, Signal processing techniques for vibration-based health monitoring of smart structures, Archives of Computational Methods in Engineering, 23(1) (2016) 1-15. [9] F. Kopsaftopoulos, S. Fassois, Vibration based health monitoring for a lightweight truss structure: experimental assessment of several statistical time series methods, Mechanical Systems and Signal Processing, 24(7) (2010) 1977-1997. [10] R.V. Farahani, D. Penumadu, Full‐scale bridge damage identification using time series analysis of a dense array of geophones excited by drop weight, Structural Control and Health Monitoring, 23(7) (2016) 982-997. [11] X. Zhu, M. Cao, W. Ostachowicz, W. Xu, Damage Identification in Bridges by Processing Dynamic Responses to Moving Loads: Features and Evaluation, Sensors, 19(3) (2019) 463. [12] L. Qiao, A. Esmaeily, H.G. Melhem, Signal pattern recognition for damage diagnosis in structures, Computer‐Aided Civil and Infrastructure Engineering, 27(9) (2012) 699-710. [13] P. Wang, Q. Shi, Damage Identification in Structures Based on Energy Curvature Difference of Wavelet Packet Transform, Shock and Vibration, 2018 (2018). [14] Z.-D. Xu, Z. Wu, Energy damage detection strategy based on acceleration responses for long-span bridge structures, Engineering Structures, 29(4) (2007) 609-617. [15] Y. Xin, H. Hao, J. Li, Operational modal identification of structures based on improved empirical wavelet transform, Structural Control and Health Monitoring, 26(3) (2019) e2323. [16] J.P. Amezquita-Sanchez, H.S. Park, H. Adeli, A novel methodology for modal parameters identification of large smart structures using MUSIC, empirical wavelet transform, and Hilbert transform, Engineering Structures, 147 (2017) 148-159. [17] N. Liu, J. Xi, X. Zhang, Z. Liu, Damage detection of simply supported reinforced concrete beam by S transform, in: IOP Conference Series: Earth and Environmental Science, IOP Publishing, 2017, pp. 012133. [18] J.-L. Liu, Z.-C. Wang, W.-X. Ren, X.-X. Li, Structural time-varying damage detection using synchrosqueezing wavelet transform, Smart Structures and Systems, 15(1) (2015) 119-133. [19] E. Darvishan, Low cost damage detection of cable-stayed bridges using signal processing and machine learning, Amirkabir Journal of Civil Engineering, (2018), (in Persian). [20] A. Neves, I. González, J. Leander, R. Karoumi, Structural health monitoring of bridges: a model-free ANN-based approach to damage detection, Journal of Civil Structural Health Monitoring, 7(5) (2017) 689-702. [21] M. Chandrashekhar, R. Ganguli, Structural damage detection using modal curvature and fuzzy logic, Structural Health Monitoring, 8(4) (2009) 267-282. [22] R. Ghiasi, M. Ghasemi, M. Sohrabi, Structural damage detection using frequency response function index and surrogate model based on optimized extreme learning machine algorithm, Computational Methods in Engineering, 36(1) (2017) 1-17, (in Persian). [23] P. Ghaderi and Y. Shabani, Damage Detection based on Modal Parameters and Dynamic Responses by using Enhanced Grey Wolf Optimization, Amirkabir Journal of Civil Engineering, (2019), (in Persian). [24] H. Babajanian Bisheh, G. Ghodrati Amiri, M. Nekooei, E. Darvishan, Damage detection of a cable-stayed bridge using feature extraction and selection methods, Structure and Infrastructure Engineering, 15(9) (2019) 1165-1177. [25] M. Gul, F.N. Catbas, Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering, Journal of Sound and Vibration, 330(6) (2011) 1196-1210. [26] S. Li, H. Li, Y. Liu, C. Lan, W. Zhou, J. Ou, SMC structural health monitoring benchmark problem using monitored data from an actual cable‐stayed bridge, Structural Control and Health Monitoring, 21(2) (2014) 156-172. [27] M.R. Kaloop, J.W. Hu, Stayed-cable bridge damage detection and localization based on accelerometer health monitoring measurements, Shock and Vibration, 2015 (2015). [28] ANCRiSST SHM benchmark problem, Harbin: Center of Structural Monitoring and Control of the Harbin Institute of Technology, http://smc.hit.edu.cn. [28] [29] Y.z. Lin, Z.h. Nie, H.w. Ma, Structural damage detection with automatic feature‐extraction through deep learning, Computer‐Aided Civil and Infrastructure Engineering, 32(12) (2017) 1025-1046. [30] A. Lerch, An introduction to audio content analysis: Applications in signal processing and music informatics, Wiley-IEEE Press, 2012. [31] J. Gilles, Empirical wavelet transform, IEEE transactions on signal processing, 61(16) (2013) 3999-4010. [32] S. Nezamivand Chegini, F. Zarif, A. Bagheri, M. AliTavoli, Noise Removal from the Vibration Signals of the Rotating Machinery Using the Empirical Wavelet Transform and the Conventional Thresholding Methods, Journal of Solid and Fluid Mechanics, 9(1) (2019) 111-124 (in Persian). [33] J. Toivola, J. Hollmén, Feature extraction and selection from vibration measurements for structural health monitoring, in: International Symposium on Intelligent Data Analysis, Springer, 2009, pp. 213-224. [34] Guyon, A. Elisseeff, An introduction to variable and feature selection, Journal of machine learning research, 3(Mar) (2003) 1157-1182. [35] E. Alpaydin, Introduction to machine learning, MIT press, 2009. [36] H. Vafaie, K. De Jong, Genetic algorithms as a tool for feature selection in machine learning, in: Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI'92, IEEE, 1992, pp. 200-203. [37] N.-T. Nguyen, H.-H. Lee, J.-M. Kwon, Optimal feature selection using genetic algorithm for mechanical fault detection of induction motor, Journal of Mechanical Science and Technology, 22(3) (2008) 490-496. [38] L. Breiman, Random forests, Machine learning, 45(1) (2001) 5-32. [39] H. Kawakubo, H. Yoshida, Rapid feature selection based on random forests for high-dimensional data, in: Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA), The Steering Committee of The World Congress in Computer Science, Computer …, 2012, pp. 1. [40] B.E. Boser, I.M. Guyon, V.N. Vapnik, A training algorithm for optimal margin classifiers, in: Proceedings of the fifth annual workshop on Computational learning theory, ACM, 1992, pp. 144-152. [41] S. Yin, X. Gao, H.R. Karimi, X. Zhu, Study on support vector machine-based fault detection in tennessee eastman process, in: Abstract and Applied Analysis, Hindawi, 2014. [42] H. Wang, P. Chen, A feature extraction method based on information theory for fault diagnosis of reciprocating machinery, Sensors, 9(4) (2009) 2415-2436.
| ||
|
آمار تعداد مشاهده مقاله: 750 تعداد دریافت فایل اصل مقاله: 1,109 |
||