مهندسی سازه و ساخت

مهندسی سازه و ساخت

تشخیص زلزله‌های پالس‌گونه با بهره گیری از الگوریتم های یادگیری ماشین: رویکردی نوین برای بهبود دقت شناسایی و ارزیابی

نوع مقاله : علمی - پژوهشی

نویسندگان
1 کارشناس ارشد، دانشکده مهندسی عمران، آب و محیط زیست، دانشگاه شهید بهشتی، تهران، ایران
2 دانشیار، دانشکده مهندسی عمران، آب و محیط زیست، دانشگاه شهید بهشتی، تهران، ایران
چکیده
شتاب‌نگاشت‌های پالس‌گونه، با ویژگی پالس‌هایی با دامنه بزرگ و مدت‌زمان کوتاه، به‌طور چشمگیری بر پاسخ دینامیکی سازه‌ها تأثیر می‌گذارند. شبیه‌سازی دقیق این نوع زلزله‌ها و تحلیل اثرات آن‌ها بر سازه‌ها و زیرساخت‌ها، از جمله الزامات کلیدی در مهندسی زلزله به شمار می‌رود. در ارزیابی خطرات لرزه‌ای یک منطقه، شتاب‌نگاشت‌های پالس‌گونه به‌عنوان یک شاخص مهم مدنظر قرار می‌گیرند، زیرا می‌توانند به‌طور قابل‌توجهی سطح خطر لرزه‌ای را افزایش دهند. از روش‌های نوین مانند تبدیل موجک می‌توان برای شناسایی دقیق پالس‌های سرعت و استخراج ویژگی‌های کلیدی از تاریخچه زمانی زلزله بهره برد. همچنین، با پیشرفت‌های اخیر در حوزه یادگیری ماشین، امکان طبقه‌بندی دقیق‌تر شتاب‌نگاشت‌های زلزله فراهم شده است. الگوریتم‌های یادگیری ماشین، با قابلیت یادگیری مستمر و به‌روزرسانی خودکار، ابزارهای مؤثری برای تشخیص و طبقه‌بندی دقیق رویدادهای لرزه‌ای محسوب می‌شوند. در این پژوهش، به‌منظور شناسایی زلزله‌های پالس‌گونه، 60 رکورد لرزه‌ای از ایران که دارای شتاب و سرعت بالا بودند، انتخاب و تحلیل شدند. با استفاده از رویکردی که تحلیل موجک را برای استخراج ویژگی‌های کلیدی سیگنال با الگوریتم یادگیری ماشین جنگل تصادفی ترکیب می‌کند، 11 رویداد به‌عنوان زلزله‌های پالس‌گونه طبقه‌بندی شدند. این روش تلفیقی تبدیل موجک و یادگیری ماشین توانایی بالایی در تشخیص ویژگی‌های متمایز زلزله‌های پالس‌گونه از خود نشان داد. ارزیابی عملکرد الگوریتم‌های مورد استفاده حاکی از آن است که روش‌های جنگل تصادفی، تقویت طبقه‌بندی و تقویت گرادیان شدید با دقت بالای94/0 عملکرد بسیار مطلوبی در طبقه‌بندی شتاب‌نگاشت‌های پالس‌گونه دارند. این نتایج نشان‌دهنده پتانسیل بالای یادگیری ماشین در تحلیل و طبقه‌بندی داده‌های لرزه‌ای است که می‌تواند نقش مؤثری در مدیریت ریسک و طراحی مقاوم‌سازی سازه‌ها ایفا کند.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Detection of pulse-type earthquakes using machine learning algorithms: A novel approach to improve detection and accuracy

نویسندگان English

Hossein Zanganaeh 1
Hamid Saffari 2
1 M.Sc., Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
2 Associate Professor, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
چکیده English

Pulse liked accelerograms, characterized by large amplitude and short duration pulses, significantly affect the dynamic response of structures. Accurate simulation of these types of earthquakes and analysis of their effects on structures and infrastructure are key requirements in earthquake engineering. In assessing the seismic hazards of a region, pulsed liked accelerograms are considered an important indicator because they can significantly increase the seismic hazard level. Modern methods such as wavelet transform can be used to accurately identify velocity pulses and extract key features from the earthquake time history. Also, recent advances in the field of machine learning have enabled more accurate classification of earthquake accelerograms. Machine learning algorithms, with their continuous learning and automatic updating capabilities, are effective tools for accurate detection and classification of seismic events. In this study, in order to identify pulse-type earthquakes, 60 seismic records from Iran with high acceleration and velocity were selected and analyzed. Using an approach that combines wavelet analysis to extract key signal features with the random forest machine learning algorithm, 11 events were classified as pulse-type earthquakes. This combined wavelet transform and machine learning method showed a high ability to recognize the distinctive features of pulse-type earthquakes. Performance evaluation of the algorithms used showed that the random forest, classification boosting, and extreme gradient boosting methods have very favorable performance in classifying pulse-type accelerograms with an accuracy of 0.94. These results indicate the high potential of machine learning in analyzing and classifying seismic data, which can play an effective role in risk management and structural retrofitting design.

کلیدواژه‌ها English

Pulse liked accelerograms
Wavelet transform
Machine learning
Random forest algorithm
Classification boosting algorithm
Extreme gradient boosting algorithm
1.         Wani, F.M., et al., Investigating the efficiency of machine learning algorithms in classifying pulse-like ground motions. Journal of Seismology, 2023. 27(5): p. 875-899.
2.         Yazdani, A., et al., Near-field probabilistic seismic hazard analysis of metropolitan Tehran using region-specific directivity models. Pure and Applied Geophysics, 2017. 174: p. 117-132.
3.         Kohrangi, M., D. Vamvatsikos, and P. Bazzurro, Pulse‐like versus non‐pulse‐like ground motion records: spectral shape comparisons and record selection strategies. Earthquake Engineering & Structural Dynamics, 2019. 48(1): p. 46-64.
4.         Fayjaloun, R., et al., Spatial variability of the directivity pulse periods observed during an earthquake. Bulletin of the Seismological Society of America, 2017. 107(1): p. 308-318.
5.         Scala, A., G. Festa, and S. Del Gaudio, Relation between near‐fault ground motion impulsive signals and source parameters. Journal of Geophysical Research: Solid Earth, 2018. 123(9): p. 7707-7721.
6.         Hayden, C.P., J.D. Bray, and N.A. Abrahamson, Selection of near-fault pulse motions. Journal of Geotechnical and Geoenvironmental Engineering, 2014. 140(7): p. 04014030.
7.         Qifang, L., Y. Yifan, and J. Xing, Basic characteristics of near-fault ground motion. EARTHQUAKE ENGINEERING AND ENGINEERING VIBRATION-CHINESE EDITION-, 2006. 26(1): p. 1.
8.         Somerville, P.G., et al., Modification of empirical strong ground motion attenuation relations to include the amplitude and duration effects of rupture directivity. Seismological research letters, 1997. 68(1): p. 199-222.
9.         Liu, Z., X. Li, and Z. Zhang, Quantitative identification of near-fault ground motions based on ensemble empirical mode decomposition. KSCE Journal of Civil Engineering, 2020. 24(3): p. 922-930.
10.       Luo, Q., et al., Seismic performance assessment of velocity pulse-like ground motions under near-field earthquakes. Rock Mechanics and Rock Engineering, 2021. 54(8): p. 3799-3816.
11.       Erdik, M., et al. Near-fault earthquake ground motion and seismic isolation design. in World Conference on Seismic Isolation. 2022. Springer.
12.       Quaranta, G., G. Angelucci, and F. Mollaioli, Near-fault earthquakes with pulse-like horizontal and vertical seismic ground motion components: Analysis and effects on elastomeric bearings. Soil Dynamics and Earthquake Engineering, 2022. 160: p. 107361.
13.       Aoi, S., T. Kunugi, and H. Fujiwara, Trampoline effect in extreme ground motion. Science, 2008. 322(5902): p. 727-730.
14.       Peng, Y. and R. Han, A comprehensive categorization method for identifying near‐fault pulse‐like ground motions. Earthquake Engineering & Structural Dynamics, 2024. 53(14): p. 4404-4431.
15.       Shahi, S.K. and J.W. Baker, An efficient algorithm to identify strong‐velocity pulses in multicomponent ground motions. Bulletin of the Seismological Society of America, 2014. 104(5): p. 2456-2466.
16.       Yaghmaei-Sabegh, S., Detection of pulse-like ground motions based on continues wavelet transform. Journal of seismology, 2010. 14: p. 715-726.
17.       Peng, Y., et al., Stochastic simulation of velocity pulses of near-fault ground motions based on multivariate copula modeling. Probabilistic Engineering Mechanics, 2023. 72: p. 103434.
18.       Zhao, D., et al. Quantitative classification of near-fault ground motions selected by energy indicators. in Structures. 2022. Elsevier.
19.       Chang, Z., F. De Luca, and K. Goda, Automated classification of near‐fault acceleration pulses using wavelet packets. Computer‐Aided Civil and Infrastructure Engineering, 2019. 34(7): p. 569-585.
20.       Ghaffarzadeh, H., A classification method for pulse-like ground motions based on S-transform. Natural Hazards, 2016. 84: p. 335-350.
21.       Liu, Z., Quantitative Identification of Pulse‐Like Ground Motions Based on Hilbert–Huang Transform. Shock and Vibration, 2021. 2021(1): p. 9915362.
22.       Chen, X., D. Wang, and R. Zhang, Identification of pulse periods in near‐fault ground motions using the HHT method. Bulletin of the Seismological Society of America, 2019. 109(6): p. 2384-2398.
23.       Panella, D.S., M.E. Tornello, and C.D. Frau, A simple and intuitive procedure to identify pulse-like ground motions. Soil Dynamics and Earthquake Engineering, 2017. 94: p. 234-243.
24.       Zhao, G., et al., An easy-to-update pulse-like ground motion identification method based on Siamese convolutional neural networks. Journal of Earthquake Engineering, 2024. 28(1): p. 1-19.
25.       Kardoutsou, V., I. Taflampas, and I. Psycharis. A new method for the classification of ground motions as pulse-like or non pulse-like. in Proceedings of 2nd European conference on earthquake engineering and seismology, Istanbul, Turkey. 2014.
26.       Wang, Y., PCA-LSTM: An Impulsive Ground-Shaking Identification Method Based on Combined Deep Learning. CMES-Computer Modeling in Engineering & Sciences, 2024. 139(3).
27.       Alloghani, M., et al., A systematic review on supervised and unsupervised machine learning algorithms for data science. Supervised and unsupervised learning for data science, 2020: p. 3-21.
28.       Mahesh, B., Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 2020. 9(1): p. 381-386.
29.       Habib, A., I. Youssefi, and M.M. Kunt, Identification of pulse-like ground motions using artificial neural network. Earthquake Engineering and Engineering Vibration, 2022. 21(4): p. 899-912.
30.       Baker, J.W., Quantitative classification of near-fault ground motions using wavelet analysis. Bulletin of the seismological society of America, 2007. 97(5): p. 1486-1501.
31.       Baker, J.W., Identification of near-fault velocity pulses and prediction of resulting response spectra, in Geotechnical earthquake engineering and soil dynamics IV. 2008. p. 1-10.
32.       Zhai, C., et al., Quantitative identification of near‐fault pulse‐like ground motions based on energy. Bulletin of the Seismological Society of America, 2013. 103(5): p. 2591-2603.
33.       De Roeck, G., et al., Application of wavelet transform for identification of pulse-like ground motions effects on response spectra.
34.       Pearson, K., Notes on the history of correlation. Biometrika, 1920. 13(1): p. 25-45.
35.       Asgarkhani, N., et al., Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods. Engineering Applications of Artificial Intelligence, 2024. 128: p. 107388.
36.       Angelucci, G., et al., Interpretable machine learning models for displacement demand prediction in reinforced concrete buildings under pulse-like earthquakes. Journal of Building Engineering, 2024. 95: p. 110124.
37.       Mosca, E., et al. SHAP-based explanation methods: a review for NLP interpretability. in Proceedings of the 29th international conference on computational linguistics. 2022.
38.       Barreñada, L., et al., Understanding overfitting in random forest for probability estimation: a visualization and simulation study. Diagnostic and Prognostic Research, 2024. 8(1): p. 14.
39.       Wyner, A.J., et al., Explaining the success of adaboost and random forests as interpolating classifiers. Journal of Machine Learning Research, 2017. 18(48): p. 1-33.

  • تاریخ دریافت 10 اسفند 1403
  • تاریخ بازنگری 21 خرداد 1404
  • تاریخ پذیرش 18 تیر 1404