Abstract
Fragility analysis is a powerful tool in performance-based earthquake engineering framework that facilitates in decision making and helps in communication between the stakeholders and design professionals. However, the fragility analysis requires performing a number of nonlinear dynamic analysis, which is computationally expensive and hinders design optimization. Therefore, surrogate models are developed to reduce the computational cost. In this paper, a computationally efficient machine learning based surrogate model framework is proposed for seismic fragility prediction of RC bridge piers. The framework utilizes Gaussian Process Regression to generate surrogate models that can predict the mean and standard deviation of the fragility functions as function of the influential material parameters. Despite using 100 samples for training, the surrogate model is mostly successful in predicting the fragility function parameters with error less than 20%.
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References
Applied Technology Council.: Quantification of building seismic performance factors, p. 685. ATC-63, FEMA P695 (2008)
Baker, J.W.: Efficient analytical fragility function fitting using dynamic structural analysis. Earthq. Spectra 31(1), 579–599 (2015)
Bureau of Indian Standards.: Criteria for earthquake resistant design of structures: Part 3—bridges and retaining walls. IS 1893 (2014)
Franchini, A., Sebastian, A., Ayala, D.D.: Surrogate-based fragility analysis and probabilistic optimization of cable-stayed bridges subject to seismic loads. Eng. Struct. 256, 113949 (2022)
Hamburger, R., Rojahn, C., Moehle, J., Bachman, R., Comartin, C., Whittaker, A.: The ATC-58 project: Development of next-generation performance-based earthquake engineering design criteria for buildings. In: 13th World Conference on Earthquake Engineering, pp. 1819. Vancouver, British Columbia (2004)
Kent, D.C., Park, R.: Flexural members with confined concrete. J. Struct. Div. 97, 1969–1990 (1971)
Kroetz, H.M., Beck, A.T.: Performance of surrogate modeling techniques in structural reliability. In: 12th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP12), pp. 12. Vancouver, Canada (2015)
Li, H.N., Wang, D.: Time-variant seismic performance of offshore RC bridge columns with uncertainty. Int. J. Struct. Stab. Dyn. 18(12), 1850149 (2018)
Menegotto, M, Pinto, P.E.: Method of analysis for cyclically loaded RC plane frames including changes in geometry and non-elastic behavior of elements under combined normal force and bending. In: IABSE Symposium, Lisbon (1973)
Moehle, J., Deierlein, G.: A framework methodology for performance-based earthquake engineering. In: 13th World Conference on Earthquake Engineering, pp. 679. Vancouver, British Columbia (2004)
OpenSees, Command manual, Version 2009 (2009)
OpenSees webpage. Accessed via https://opensees.berkeley.edu/wiki/index.php/Main_Page
Rachedi, M., Matallah, M., Kotronis, P.: Seismic behavior and risk assessment of an existing bridge considering soil-structure interaction using artificial neural networks. Eng. Struct. 232, 111800 (2021). Elsevier
Ramanathan, K.N.: Next generation seismic fragility curves for California bridges incorporating the evolution in seismic design philosophy. Ph.D. Dissertation, Georgia Institute of Technology (2012)
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. The MIT Press (2006)
Sheikh, M.N., Tsang, H.H., McCarthy, T.J., Lam, N.T.K.: Yield curvature for seismic design of circular reinforced concrete columns. Mag. Concr. Res. 62(10), 741–748 (2010)
Simpson, T., Dervilis, N., Chatzi, E.: Machine learning approach to model order reduction of nonlinear systems via autoencoder and LSTM networks. J. Eng. Mech. 147(10) (2010)
The MathWorks, Inc., MATLAB (2022)
Yu, Y., Yao, H., Liu, Y.: Structural dynamics simulation using a novel physics-guided machine learning method. Eng. Appl. Artif. Intell. 96, 103947 (2020)
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Narinder, Goswami, K., Bose, S., Banerjee, A., Chakraborty, S. (2023). ML-Based Surrogate Model for RC Bridge Pier Collapse Fragility Prediction. In: Shrikhande, M., Agarwal, P., Kumar, P.C.A. (eds) Proceedings of 17th Symposium on Earthquake Engineering (Vol. 2). SEE 2022. Lecture Notes in Civil Engineering, vol 330. Springer, Singapore. https://doi.org/10.1007/978-981-99-1604-7_49
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DOI: https://doi.org/10.1007/978-981-99-1604-7_49
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