Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Deep neural network for prediction of time-history seismic response of bridges

  • An, Hyojoon (Department of Civil Engineering, Inha University) ;
  • Lee, Jong-Han (Department of Civil Engineering, Inha University)
  • Received : 2022.02.27
  • Accepted : 2022.07.04
  • Published : 2022.08.10
⟨ Previous Next ⟩

Abstract

The collapse of civil infrastructure due to natural disasters results in financial losses and many casualties. In particular, the recent increase in earthquake activities has highlighted on the importance of assessing the seismic performance and predicting the seismic risk of a structure. However, the nonlinear behavior of a structure and the uncertainty in ground motion complicate the accurate seismic response prediction of a structure. Artificial intelligence can overcome these limitations to reasonably predict the nonlinear behavior of structures. In this study, a deep learning-based algorithm was developed to estimate the time-history seismic response of bridge structures. The proposed deep neural network was trained using structural and ground motion parameters. The performance of the seismic response prediction algorithm showed the similar phase and magnitude to those of the time-history analysis in a single-degree-of-freedom system that exhibits nonlinear behavior as a main structural element. Then, the proposed algorithm was expanded to predict the seismic response and fragility prediction of a bridge system. The proposed deep neural network reasonably predicted the nonlinear seismic behavior of piers and bearings for approximately 93% and 87% of the test dataset, respectively. The results of the study also demonstrated that the proposed algorithm can be utilized to assess the seismic fragility of bridge components and system.

Keywords

Acknowledgement

This research was supported by the Inha University Research Grant.

References

  1. Adeli, H. (2001), "Neural networks in civil engineering: 1989- 2000", Comput.-Aid. Civil Infrastr. Eng., 16(2), 126-142. https://doi.org/10.1111/0885-9507.00219.
  2. Adeli, H. and Panakkat, A. (2009), "A probabilistic neural network for earthquake magnitude prediction", Neur. Network., 22(7), 1018-1024. https://doi.org/10.1016/j.neunet.2009.05.003.
  3. Alam, J., Kim, D. and Choi, B. (2019), "Seismic risk assessment of intake tower in Korea using updated fragility by Bayesian inference", Struct. Eng. Mech., 69(3), 317-326. https://doi.org/10.12989/sem.2019.69.3.317.
  4. An, H., Lee, J.H. and Shin, S. (2020), "Dynamic response evaluation of bridges considering aspect ratio of pier in near-fault and far-fault ground motions", Appl. Sci., 10(17), 6098. https://doi.org/10.3390/app10176098.
  5. Ansari, M., Safiey, A. and Abbasi, M. (2020), "Fragility based performance evaluation of mid-rise reinforced concrete frames in near field and far field earthquakes", Struct. Eng. Mech., 76(6), 751-763. https://doi.org/10.12989/sem.2020.76.6.751.
  6. ASCE. (2013), Seismic Evaluation and Rehabilitation of Existing Buildings, ASCE/SEI 41-13 (Public Comment Draft), American Society of Civil Engineers, Reston, VA.
  7. ATC (1997), Seismic Evaluation and Retrofit of Concrete Buildings, Report No. ATC-40, Applied Technology Council, Redwood City, CA.
  8. Bahmani, Z., Ghasemi, M.R., Mousaviamjad, S.S. and Gharehbaghi, S. (2019), "Prediction of performance point of semi-rigid steel frames using artificial neural networks", Int. J. Intel. Syst. Appl., 11(10), 42-53. https://doi.org/10.5815/ijisa.2019.10.05.
  9. Barcley, L. and Kowalsky, M. (2020), "Seismic performance of circular concrete columns reinforced with high-strength steel", J. Struct. Eng., 146(2), 04019198. https://doi.org/10.1061/(asce)st.1943-541x.0002452.
  10. Behmanesh, I., Yousefianmoghadam, S., Nozari, A., Moaveni, B. and Stavridis, A. (2018), "Uncertainty quantification and propagation in dynamic models using ambient vibration measurements, application to a 10-story building", Mech. Syst. Signal Pr., 107, 502-514. https://doi.org/10.1016/j.ymssp.2018.01.033.
  11. Casarotti, Ch. and Pinho, R. (2006), "Seismic response of continuous span bridges through fiber based finite element analysis", Earthq. Eng. Eng. Vib., 5, 119-131. https://doi.org/10.1007/s11803-006-0631-0.
  12. Celik, O.C. and Ellingwood, B.R. (2010), "Seismic fragilities for non-ductile reinforced concrete frames-Role of aleatoric and epistemic uncertainties", Struct. Saf., 32(1), 1-12. https://doi.org/10.1016/j.strusafe.2009.04.003.
  13. Cha, Y.J., Choi, W. and Buyukozturk, O. (2017), "Deep learning-based crack damage detection using convolutional neural networks", Comput.-Aid. Civil Infrastr. Eng., 32(5), 361-378. https://doi.org/10.1111/mice.12263.
  14. Chunbo, X. and Yuxia, L. (2009), "Hysteresis response neural network and its applications", 2009 Second ISECS International Colloquium on Computing, Communication, Control, and Management, CCCM 2009, 1, 361-364. https://doi.org/10.1109/CCCM.2009.5268105.
  15. Chungui, Z., Xinong, Z., Shilin, X., Tong, Z. and Changchun, Z. (2009), "Hybrid modeling of wire cable vibration isolation system through neural network", Math. Comput. Simul., 79(10), 3160-3173. https://doi.org/10.1016/j.matcom.2009.03.007.
  16. Clevert, D.A., Unterthiner, T. and Hochreiter, S. (2015), "Fast and accurate deep network learning by Exponential Linear Units (ELUs)", 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings.
  17. Cornell, C.A., Jalayer, F., Hamburger, R.O. and Foutch, D.A. (2002), "Probabilistic basis for 2000 SAC federal emergency management agency steel moment frame guidelines", J. Struct. Eng., 128(4), 526-533. https://doi.org/10.1061/(ASCE)0733-9445(2002)128:4(526).
  18. Dibi, Z. and Lamine Hafiane, M. (2007), "Artificial neural network-based hysteresis estimation of capacitive pressure sensor", Physica Status Solidi (b), 244(1), 468-473. https://doi.org/10.1002/pssb.200672579.
  19. Ediger, V.S. and Akar, S. (2007), "ARIMA forecasting of primary energy demand by fuel in Turkey", Energy Policy, 35(3), 1701-1708. https://doi.org/10.1016/j.enpol.2006.05.009.
  20. Ellsworth, W.L., Llenos, A.L., McGarr, A.F., Michael, A.J., Rubinstein, J.L., Mueller, C.S., Petersen, M.D. and Calais, E. (2015), "Increasing seismicity in the U. S. midcontinent: Implications for earthquake hazard", Lead. Edge, 34(6), 618-626. https://doi.org/10.1190/tle34060618.1.
  21. Federal Emergency Management Agency (FEMA) (2020), Hazus Earthquake Model Technical Manual-Hazus 4.2 SP3, National Institute of Building Sciences and Federal Emergency Management Agency (NIBS and FEMA).
  22. FEMA (2005), "Improvement of nonlinear static seismic analysis procedures", Report No. FEMA 440, Washington, DC.
  23. Fishwick, P.A. (1989), "Neural network models in simulation: A comparison with traditional modeling approaches", Winter Simulation Conference Proceedings, 702-710.
  24. Garbin, C., Zhu, X. and Marques, O. (2020), "Dropout vs. batch normalization: an empirical study of their impact to deep learning", Multimedia Tool. Appl., 79(19-20), 12777-12815. https://doi.org/10.1007/s11042-019-08453-9.
  25. Habibi, A.R., Vahed, M. and Asadi, K. (2018), "Evaluation of Seismic performance of RC setback frames", Struct. Eng. Mech., 66(5), 609-619. https://doi.org/10.12989/sem.2018.66.5.609.
  26. Home | Pacific Earthquake Engineering Research Center n.d. https://peer.berkeley.edu/ (accessed April 30, 2021).
  27. Jalali, R.S., Bahari Jokandan, M. and Trifunac, M.D. (2012), "Earthquake response of a three-span, simply supported bridge to near-field pulse and permanent-displacement step", Soil Dyn. Earthq. Eng., 43, 380-397. https://doi.org/10.1016/j.soildyn.2012.08.004.
  28. Jalikhani, M. and Manafpour, A.R. (2018), "Evaluation of seismic collapse capacity of regular RC frames using nonlinear static procedure", Struct. Eng. Mech., 68(6), 647-660. https://doi.org/10.12989/sem.2018.68.6.647.
  29. Kabir, M.R., Billah, A.H.M.M. and Alam, M.S. (2019), "Seismic fragility assessment of a multi-span RC bridge in Bangladesh considering near-fault, far-field and long duration ground motions", Struct., 19, 333-348. https://doi.org/10.1016/j.istruc.2019.01.021.
  30. Kim, T., Kwon, O.S. and Song, J. (2019), "Response prediction of nonlinear hysteretic systems by deep neural networks", Neur. Network., 111, 1-10. https://doi.org/10.1016/j.neunet.2018.12.005.
  31. Kim, T., Song, J. and Kwon, O.S. (2020), "Probabilistic evaluation of seismic responses using deep learning method", Struct. Saf., 84, 101913. https://doi.org/10.1016/j.strusafe.2019.101913.
  32. Kostinakis, K.G. and Morfidis, K.E. (2020), "Optimization of the seismic performance of masonry infilled R/C buildings at the stage of design using artificial neural networks", Struct. Eng. Mech., 75(3), 295-309. https://doi.org/10.12989/sem.2020.75.3.295.
  33. Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2017), "ImageNet classification with deep convolutional neural networks", Commun. ACM, 60(6), 84-90. https://doi.org/10.1145/3065386.
  34. Kwon, O.S. and Elnashai, A. (2006), "The effect of material and ground motion uncertainty on the seismic vulnerability curves of RC structure", Eng. Struct., 28(2), 289-303. https://doi.org/10.1016/j.engstruct.2005.07.010.
  35. Lawrence, S., Giles, C.L., Tsoi, A.C. and Back, A.D. (1997), "Face recognition: A convolutional neural-network approach", IEEE Trans. Neur. Network., 8(1), 98-113. https://doi.org/10.1109/72.554195.
  36. Lecun, Y., Bengio, Y. and Hinton, G. (2015), "Deep learning", Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539.
  37. Li, L.X., Li, H.N. and Li, C. (2018), "Seismic fragility assessment of self-centering RC frame structures considering maximum and residual deformations", Struct. Eng. Mech., 68(6), 677-689. https://doi.org/10.12989/sem.2018.68.6.677.
  38. Liu, P., Zhu, H.X., Fan, P.P. and Yang, W.G. (2021), "A reliability based fragility assessment method for seismic pounding between nonlinear buildings", Struct. Eng. Mech., 77(1), 19-35. https://doi.org/10.12989/sem.2021.77.1.019.
  39. McKenna, F. and Fenves, G.L. (2006), Open System for Earthquake Engineering Simulation User Manual, Open System for Earthquake Engineering Simulation (OpenSees).
  40. Menegotto, M. and Pinto, P.E. (1973), "Method of analysis for cyclically loaded R.C. plane frames including changes in geometry and non-elastic behavior of elements under combined normal force and bending", Symposium on the Resistance and Ultimate Deformability of structures Acted on by Well Defined Repeated Loads, International Association for Bridge and Structural Engineering, Zurich, Switzerland, September.
  41. Najam, F.A. (2018), "Nonlinear static analysis procedures for seismic performance evaluation of existing buildingsEvolution and issues", Sustain. Civil Infrastr.: Innov. Infrastr. Geotech., 180-198. https://doi.org/10.1007/978-3-319-61914-9_15.
  42. Nassar, A. and Krawinkler, H. (1991), "Seismic demands for SDOF and MDOF systems", John A. Blume Earthquake Engineering Center Report, Stanford University.
  43. Nielson, B.G. and DesRoches, R. (2007), "Seismic fragility methodology for highway bridges using a component level approach", Earthq. Eng. Struct. Dyn., 36(6), 823-839. https://doi.org/10.1002/eqe.655.
  44. Noureldin, M., Adane, M. and Kim, J. (2021), "Seismic fragility of structures with energy dissipation devices for mainshock-aftershock events", Earthq. Struct., 21(3), 219-230. https://doi.org/10.12989/eas.2021.21.3.219.
  45. Stefanidou, S.P. and Kappos, A.J. (2017), "Methodology for the development of bridge-specific fragility curves", Earthq. Eng. Struct. Dyn., 46(1), 73-93. https://doi.org/10.1002/eqe.2774.
  46. Stone, W.C. and Cheok, G.S. (1989), "Inelastic behavior of full-scale bridge columns subjected to cyclic loading in national institute of standards and technology", No. NIST BSS 166.
  47. Surana, M. (2020), "Seismic fragility curves using pulse-like and spectrally equivalent ground-motion records", Earthq. Struct., 19(2), 79-90. https://doi.org/10.12989/eas.2020.19.2.079.
  48. Tomar, A. and Burton, H.V. (2021), "Active learning method for risk assessment of distributed infrastructure systems", Comput.-Aid. Civil Infrastr. Eng., 36(4), 438-452. https://doi.org/10.1111/mice.12665.
  49. Vamvatsikos, D. and Fragiadakis, M. (2009), "Incremental dynamic analysis for estimating seismic performance sensitivity and uncertainty", Earthq. Eng. Struct. Dyn., 39(2), 70-81. https://doi.org/10.1002/eqe.935.
  50. Vrigazova, B. (2021), "The proportion for splitting data into training and test set for the bootstrap in classification problems", Business Syst. Res., 12(1), 228-242. https://doi.org/10.2478/bsrj-2021-0015.
  51. Xie, S.L., Zhang, Y.H., Chen, C.H. and Zhang, X.N. (2013), "Identification of nonlinear hysteretic systems by artificial neural network", Mech. Syst. Signal. Pr., 34(1-2), 76-87. https://doi.org/10.1016/j.ymssp.2012.07.015.
  52. Yinfeng, D., Yingmin, L., Ming, L. and Mingkui, X. (2008), "Nonlinear structural response prediction based on support vector machines", J. Sound Vib., 311(3-5), 886-897. https://doi.org/10.1016/j.jsv.2007.09.054.
  53. Zhang, J., Sato, T. and Iai, S. (2007), "Novel support vector regression for structural system identification", Struct. Control Hlth. Monit., 14(4), 609-626. https://doi.org/10.1002/stc.175.
  54. Zhang, R., Liu, Y. and Sun, H. (2020), "Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling", Eng. Struct., 215, 110704. https://doi.org/10.1016/j.engstruct.2020.110704.