基于物理信息引导深度学习的建筑响应实时预测方法

Ying Zhou; Shiqiao Meng; Yujie Lou; Qingzhao Kong

doi:10.1016/j.eng.2023.08.011

PDF(5079 KB)

工程（英文） ›› 2024, Vol. 35 ›› Issue (4) : 140-157. DOI: 10.1016/j.eng.2023.08.011

研究论文

Article

基于物理信息引导深度学习的建筑响应实时预测方法

作者信息 +

Physics-Informed Deep Learning-Based Real-Time Structural Response Prediction Method

Author information +

History +

Abstract

High-precision and efficient structural response prediction is essential for intelligent disaster prevention and mitigation in building structures, including post-earthquake damage assessment, structural health monitoring, and seismic resilience assessment of buildings. To improve the accuracy and efficiency of structural response prediction, this study proposes a novel physics-informed deep-learning-based real-time structural response prediction method that can predict a large number of nodes in a structure through a data-driven training method and an autoregressive training strategy. The proposed method includes a Phy-Seisformer model that incorporates the physical information of the structure into the model, thereby enabling higher-precision predictions. Experiments were conducted on a four-story masonry structure, an eleven-story reinforced concrete irregular structure, and a twenty-one-story reinforced concrete frame structure to verify the accuracy and efficiency of the proposed method. In addition, the effectiveness of the structure in the Phy-Seisformer model was verified using an ablation study. Furthermore, by conducting a comparative experiment, the impact of the range of seismic wave amplitudes on the prediction accuracy was studied. The experimental results show that the method proposed in this paper can achieve very high accuracy and at least 5000 times faster calculation speed than finite element calculations for different types of building structures.

Keywords

Structural seismic response prediction / Physics information informed / Real-time prediction / Earthquake engineering / Data-driven machine learning

引用本文

EndNote

Ris (Procite)

Bibtex

导出引用

Ying Zhou, Shiqiao Meng, Yujie Lou. . Engineering. 2024, 35(4): 140-157 https://doi.org/10.1016/j.eng.2023.08.011

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序

[1]	B.F. Spencer Jr, V. Hoskere, Y. Narazaki. Advances in computer vision-based civil infrastructure inspection and monitoring. Engineering, 5 (2) (2019), pp. 199-222.
[2]	W.K. Baek, H.S. Jung. Precise three-dimensional deformation retrieval in large and complex deformation areas via integration of offset-based unwrapping and improved multiple-aperture SAR interferometry: application to the 2016 Kumamoto earthquake. Engineering, 6 (8) (2020), pp. 927-935.
[3]	J. Takagi, A. Wada. Higher performance seismic structures for advanced cities and societies. Engineering, 5 (2) (2019), pp. 184-189.
[4]	L. Lulić, K. Ožić, T. Kišiček, I. Hafner, M. Stepinac. Post-earthquake damage assessment—case study of the educational building after the Zagreb earthquake. Sustainability, 13 (11) (2021), p. 6353.
[5]	A. Tang, A. Wen. An intelligent simulation system for earthquake disaster assessment. Comput Geosci, 35 (5) (2009), pp. 871-879.
[6]	F. Bianconi, G.P. Salachoris, F. Clementi, S. Lenci. A genetic algorithm procedure for the automatic updating of FEM based on ambient vibration tests. Sensors, 20 (11) (2020), p. 3315.
[7]	Ministry of Housing and Urban-Rural Development of the People’s Republic of China, State Administration for Market Regulation. GB/T 38591-2020: Standard for seismic resilience assessment of buildings. Chinese standard. Beijing: Standards Press of China; 2020. Chinese.
[8]	Applied Technology Council and National Earthquake Hazards Reduction Program (USA). Seismic performance assessment of buildings. Report. Washington, DC: Federal Emergency Management Agency (FEMA); 2012.
[9]	Formisano A, Chieffo N, Milo B, Fabbrocino F. The influence of local mechanisms on large scale seismic vulnerability estimation of masonry building aggregates. In: Proceedings of the International Conference of Computational Methods in Sciences and Engineering; 2016 Mar 17-20; Athens, New York City: American Institute of Physics (AIP) Publishing; 2016.
[10]	Chieffo N, Mosoarca M, Formisano A, Apostol I. Seismic vulnerability assessment and loss estimation of an urban district of Timisoara. In:Proceedings of the Institute of Physics (IOP) Conference Series: Materials Science and Engineering;2019 Apr 25-26 ; Tatranske Matliare, Slovakia. Bristol: IOP Publishing; 2019.
[11]	N. Chieffo, A. Formisano, R. Landolfo, G. Milani. A vulnerability index based-approach for the historical centre of the city of Latronico (Potenza, southern Italy). Eng Fail Anal, 136 (2022), 106207.
[12]	F. Butt, P. Omenzetter. Seismic response trends evaluation and finite element model calibration of an instrumented RC building considering soil-structure interaction and non-structural components. Eng Struct, 65 (2014), pp. 111-123.
[13]	Fu H, He C, Chen B. 9-Pflops nonlinear earthquake simulation on Sunway TaihuLight: enabling depiction of 18-Hz and 8-meter scenarios. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis; 2017 Nov 12-17; Denver, CO, USA. New York City: Association for Computing Machinery; 2017. p. 1-12.
[14]	Y. Zhang, X.K. Yan, X.D. Ren, S. Wang, D.Y. Wu, B.D. Bai. Parallel implementation and branch optimization of EBE-FEM based on CUDA platform. Appl Comput Electrom, 35 (6) (2020), pp. 595-600.
[15]	Reyes JC, Kalkan E, Sierra A. Fast nonlinear response history analysis. In:Proceedings of 16th World Conference on Earthquake Engineering;2017 Jan 9-13; Santiago, Chile. Santiago: Asociación Chilena de Sismología e Ingeniería Antisísmica (ACHISINA); 2017.
[16]	Li B, Chuang WC, Spence SM. An adaptive fast nonlinear analysis (AFNA) algorithm for rapid time history analysis. In:Proceedings of the 8th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering;2021 Jun 27-30 ; Athens, Greece. Alexandria: US National Science Foundation (NSF) Public Access; 2021.
[17]	Lin X, Wang K, Zhang L, Skalomenos KA, Li D. Development of a simulation platform to model, assess and visualize the earthquake disasters in city level. In:Proceedings of the 2nd International Conference on Natural Hazards and Infrastructure; 2019 Jun 23-26; Chania, Greece. Berlin:Springer; 2019.
[18]	M. Hori, T. Ichimura. Current state of integrated earthquake simulation for earthquake hazard and disaster. J Seismol, 12 (2) (2008), pp. 307-321.
[19]	X. Lu, H. Guan. Earthquake disaster simulation of civil infrastructures. Springer and Science Press, Beijing (2017).
[20]	K.Y. Lin, T.K. Lin, Y. Lin. Real-time seismic structural response prediction system based on support vector machine. Earthq Struct, 18 (2) (2020), pp. 163-170.
[21]	B.K. Oh, B. Glisic, S.W. Park, H.S. Park. Neural network-based seismic response prediction model for building structures using artificial earthquakes. J Sound Vibrat, 468 (2020), 115109.
[22]	M. Papadrakakis, N.D. Lagaros. Reliability-based structural optimization using neural networks and Monte Carlo simulation. Comput Methods Appl Math, 191 (32) (2002), pp. 3491-3507.
[23]	D. Yang, K. Yang. Multi-step prediction of strong earthquake ground motions and seismic responses of SDOF systems based on EMD-ELM method. Soil Dyn Earthq Eng, 85 (2016), pp. 117-129.
[24]	D.M. Sahoo, S. Chakraverty. Functional link neural network learning for response prediction of tall shear buildings with respect to earthquake data. IEEE T Syst Man Cy-S, 48 (1) (2017), pp. 1-10.
[25]	J. Tezcan, C.C. Marin-Artieda. Least-square-support-vector-machine-based approach to obtain displacement from measured acceleration. Adv Eng Softw, 115 (2018), pp. 357-362.
[26]	A. Krizhevsky, I. Sutskever, G.E. Hinton. Imagenet classification with deep convolutional neural networks. Commun ACM, 60 (6) (2017), pp. 84-90.
[27]	Zaremba W, Sutskever I, Vinyals O. Recurrent neural network regularization. 2014. arXiv:1409.2329.
[28]	S. Hochreiter, J. Schmidhuber. Long short-term memory. Neural Comput, 9 (8) (1997), pp. 1735-1780.
[29]	A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, et al. Attention is all you need. I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan (Eds.), Advances in neural information processing systems, Curran Associates, Red Hook, 2017).
[30]	T. Kim, O.S. Kwon, J. Song. Response prediction of nonlinear hysteretic systems by deep neural networks. Neural Netw, 111 (2019), pp. 1-10.
[31]	T. Kim, J. Song, O.S. Kwon. Probabilistic evaluation of seismic responses using deep learning method. Struct Saf, 84 (2020), 101913.
[32]	R.T. Wu, M.R. Jahanshahi. Deep convolutional neural network for structural dynamic response estimation and system identification. J Eng Mech, 145 (1) (2019), p. 04018125.
[33]	J. Gao, C. Zhang. Structural seismic response prediction based on long short-term memory network. Earthq Resist Eng Retrofit, 42 (3) (2020), pp. 130-136 Chinese.
[34]	R. Zhang, Z. Chen, S. Chen, J. Zheng, O. Büyüköztürk, H. Sun. Deep long short-term memory networks for nonlinear structural seismic response prediction. Comput Struc, 220 (2019), pp. 55-68.
[35]	R. Zhang, Y. Liu, H. Sun. Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling. Eng Struct, 215 (2020), 110704.
[36]	C.A. Perez-Ramirez, J.P. Amezquita-Sanchez, M. Valtierra-Rodriguez, H. Adeli, A. Dominguez-Gonzalez, R.J. Romero-Troncoso. Recurrent neural network model with Bayesian training and mutual information for response prediction of large buildings. Eng Struct, 178 (2019), pp. 603-615.
[37]	H. Peng, J. Yan, Y. Yu, Y. Luo. Time series estimation based on deep Learning for structural dynamic nonlinear prediction. Structures, 29 (2021), pp. 1016-1031.
[38]	S.S. Eshkevari, M. Takáč, S.N. Pakzad, M. Jahani. DynNet: physics-based neural architecture design for nonlinear structural response modeling and prediction. Eng Struct, 229 (2021), 111582.
[39]	Ministry of Housing and Urban-Rural Development of the People’s Republic of China, China Association for Engineering Construction Standardization. GB 50009-2012: Load code for the design of building structures. Chinese standard. Beijing: China Architecture and Building Press; 2012. Chinese.
[40]	S.H. Bao. Time-history analysis method for seismic response of high-rise building structures. S.H. Bao (Ed.), New high-rise building structure, China Water and Power Press, Beijing (2013), pp. 417-419.
[41]	H. Hashamdar, Z. Ibrahim, M. Jameel. Finite element analysis of nonlinear structures with Newmark method. Int J Phys Sci, 6 (6) (2011), pp. 1395-1403.
[42]	X.J. Liu, X.L. Du. MDOF system. X.J. Liu, X.L. Du (Eds.), Dynamic of structure, China Machine Press, Beijing (2005), pp. 119-122.
[43]	M. Zhou, N. Duan, S. Liu, H.Y. Shum. Progress in neural NLP: modeling, learning, and reasoning. Engineering, 6 (3) (2020), pp. 275-290.
[44]	Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. In: In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics; 2011 Apr 11-13; Ft. Lauderdale, FL, USA. New York City: Association for Computing Machinery; 2013. p. 315-23.
[45]	Shanghai Urban and Rural Construction and Transportation Commission. Rural Construction and Transportation Commission. DGJ08-9-2013: Code for seismic design of buildings. Chinese standard. Beijing: China Architecture and Building Press; 2013. Chinese.
[46]	Kingma DP, Ba J. Adam: a method for stochastic optimization. 2014. arXiv:1412.6980.
[47]	A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan. Pytorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst, 32 (2019), pp. 8026-8037.
[48]	Ministry of Housing and Urban-Rural Development of the People’s Republic of China, State Administration for Market Regulation. GB 50010-2010: Code for design of concrete structures. Chinese standard. Beijing: China Architecture and Building Press; 2015. Chinese.
[49]	F. Clementi. Failure analysis of Apennine masonry churches severely damaged during the 2016 central Italy seismic sequence. Buildings, 11 (2) (2021), p. 58.
[50]	Ministry of Housing and Urban-Rural Development of the People’s Republic of China, State Administration for Market Regulation. GB 50011-2010: Code for seismic design of buildings. Chinese standard. Beijing: China Architecture and Building Press; 2016. Chinese.
[51]	S. Meng, Y. Zhou, X. Zhang, Y. Lu. Research on real-time prediction algorithm of seismic time history response of building structures. J Build Struct, 43 (Suppl 1) (2022), pp. 334-344.Chinese.