[1] |
聂建国, 王宇航. ABAQUS中混凝土本构模型用于模拟结构静力行为的比较研究[J]. 工程力学, 2013, 30(4): 59 − 67, 82. doi: 10.6052/j.issn.1000-4750.2011.07.0420NIE Jianguo, WANG Yuhang. Comparison study of constitutive model of concrete in ABAQUS for static analysis of structures [J]. Engineering Mechanics, 2013, 30(4): 59 − 67, 82. (in Chinese) doi: 10.6052/j.issn.1000-4750.2011.07.0420
|
[2] |
许立言. 低屈服点钢剪切型阻尼器的力学性能及理论模型研究[D]. 北京: 清华大学, 2017.XU Liyan. Research on mechanical behavior and theoretical model of low-yield-point steel shear panel dampers [D]. Beijing: Tsinghua University, 2017. (in Chinese)
|
[3] |
骆晶, 施刚, 毛灵涛, 等. 双相型不锈钢S22053循环本构关系研究[J]. 工程力学, 2021, 38(9): 171 − 181. doi: 10.6052/j.issn.1000-4750.2020.09.0659LUO Jing, SHI Gang, MAO Lingtao, et al. Constitutive relation of duplex stainless steel S22053 under cyclic loading [J]. Engineering Mechanics, 2021, 38(9): 171 − 181. (in Chinese) doi: 10.6052/j.issn.1000-4750.2020.09.0659
|
[4] |
班慧勇, 梅镱潇, 石永久. 不锈钢复合钢材钢结构研究进展[J]. 工程力学, 2021, 38(6): 1 − 23. doi: 10.6052/j.issn.1000-4750.2020.04.ST01BAN Huiyong, MEI Yixiao, SHI Yongjiu. Research advances of stainless-clad bimetallic steel structures [J]. Engineering Mechanics, 2021, 38(6): 1 − 23. (in Chinese) doi: 10.6052/j.issn.1000-4750.2020.04.ST01
|
[5] |
刘晓刚, 樊健生, 聂建国, 等. 剪切型消能连梁的塑性强化特性研究[J]. 土木工程学报, 2017, 50(3): 1 − 11. doi: 10.15951/j.tmgcxb.2017.03.001LIU Xiaogang, FAN Jiansheng, NIE Jianguo, et al. Research on plastic overstrength of energy-dissipation shear links [J]. China Civil Engineering Journal, 2017, 50(3): 1 − 11. (in Chinese) doi: 10.15951/j.tmgcxb.2017.03.001
|
[6] |
侯帅, 朱有利, 王燕礼, 等. 基于多晶体塑性与唯象学本构模型的纯铝与单晶铝的有限变形分析与对比[J]. 稀有金属材料与工程, 2017, 46(12): 3760 − 3766.HOU Shuai, ZHU Youli, WANG Yanli, et al. Analysis and comparison of finite deformation of pure aluminum and single crystal aluminum based on polycrystal plasticity and phenomenological constitutive models [J]. Rare Metal Materials and Engineering, 2017, 46(12): 3760 − 3766. (in Chinese)
|
[7] |
王元清, 关阳, 刘明, 等. 建筑结构钢材及其焊缝循环微观损伤模型的韧性参数校正分析[J]. 工程力学, 2020, 37(增刊): 20 − 31. doi: 10.6052/j.issn.1000-4750.2019.04.S019WANG Yuanqing, GUAN Yang, LIU Ming, et al. Correction analysis of toughness parameters of cyclic microscopic damage model for building structural steel and its welds [J]. Engineering Mechanics, 2020, 37(Suppl): 20 − 31. (in Chinese) doi: 10.6052/j.issn.1000-4750.2019.04.S019
|
[8] |
WANG C, XU L, FAN J. Cyclic softening behavior of structural steel with strain range dependence [J]. Journal of Constructional Steel Research, 2021, 181: 106658. doi: 10.1016/j.jcsr.2021.106658
|
[9] |
TANG M, LIU Y, DURLOFSKY L J. A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems [J]. Journal of Computational Physics, 2020, 413: 109456. doi: 10.1016/j.jcp.2020.109456
|
[10] |
WANG C, XU L, FAN J. A general deep learning framework for history-dependent response prediction based on UA-Seq2Seq model [J]. Computer Methods in Applied Mechanics and Engineering, 2020, 372: 113357. doi: 10.1016/j.cma.2020.113357
|
[11] |
HORNIK K, STINCHCOMBE M, WHITE H. Multilayer feedforward networks are universal approximators [J]. Neural Networks, 1989, 2(5): 359 − 366. doi: 10.1016/0893-6080(89)90020-8
|
[12] |
LEFIK M, SCHREFLER B A. Artificial neural network as an incremental non-linear constitutive model for a finite element code [J]. Computer Methods in Applied Mechanics and Engineering, 2003, 192(28/29/30): 3265 − 3283. doi: 10.1016/S0045-7825(03)00350-5
|
[13] |
GHAVAMIAN F, SIMONE A. Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network [J]. Computer Methods in Applied Mechanics and Engineering, 2019, 357: 112594. doi: 10.1016/j.cma.2019.112594
|
[14] |
王琛, 樊健生. 具有历史依赖效应的材料及结构响应预测通用深度学习模型MechPerformer[J]. 建筑结构学报, 2022, 43(8): 209 − 219. doi: 10.14006/j.jzjgxb.2021.0115WANG Chen, FAN Jiansheng. A general deep learning model MechPerformer for history-dependent response prediction in structural engineering [J]. Journal of Building Structures, 2022, 43(8): 209 − 219. (in Chinese) doi: 10.14006/j.jzjgxb.2021.0115
|
[15] |
MOZAFFAR M, BOSTANABAD R, CHEN W, et al. Deep learning predicts path-dependent plasticity [J]. Proceedings of the National Academy of Sciences, 2019, 116(52): 26414 − 26420. doi: 10.1073/pnas.1911815116
|
[16] |
WANG J J, WANG C, FAN J S, et al. A deep learning framework for constitutive modeling based on temporal convolutional network [J]. Journal of Computational Physics, 2022, 449: 110784. doi: 10.1016/j.jcp.2021.110784
|
[17] |
RAISSI M, PERDIKARIS P, KARNIADAKIS G E. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations [J]. Journal of Computational physics, 2019, 378: 686 − 707. doi: 10.1016/j.jcp.2018.10.045
|
[18] |
KARNIADAKIS G E, KEVREKIDIS I G, LU L, et al. Physics-informed machine learning [J]. Nature Reviews Physics, 2021, 3(6): 422 − 440. doi: 10.1038/s42254-021-00314-5
|
[19] |
CAI S, MAO Z, WANG Z, et al. Physics-informed neural networks (PINNs) for fluid mechanics: A review [J]. Acta Mechanica Sinica, 2021, 37(12): 1 − 12. doi: 10.48550/arXiv.2105.09506
|
[20] |
HAGHIGHAT E, JUANES R. Sciann: A keras/tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks [J]. Computer Methods in Applied Mechanics and Engineering, 2021, 373: 113552. doi: 10.1016/j.cma.2020.113552
|
[21] |
RAISSI M, YAZDANI A, KARNIADAKIS G E. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations [J]. Science, 2020, 367(6481): 1026 − 1030. doi: 10.1126/science.aaw4741
|
[22] |
SIMO J C, HUGHES T J R. Computational inelasticity [M]. Berlin, Germany: Springer Science & Business Media, 2006.
|
[23] |
WANG C, FAN J, XU L, et al. Cyclic hardening and softening behavior of the low yield point steel: Implementation and validation [J]. Engineering Structures, 2020, 210: 110220. doi: 10.1016/j.engstruct.2020.110220
|
[24] |
BOYD S, BOYD S P, VANDENBERGHE L. Convex optimization [M]. London, UK: Cambridge University Press, 2004.
|
[25] |
WANG Y, YAO Q, KWOK J T, et al. Generalizing from a few examples: A survey on few-shot learning [J]. ACM Computing Surveys (CSUR), 2020, 53(3): 1 − 34.
|
[26] |
CHABOCHE J L. Time-independent constitutive theories for cyclic plasticity [J]. International Journal of Plasticity, 1986, 2(2): 149 − 188. doi: 10.1016/0749-6419(86)90010-0
|
[27] |
CHABOCHE J L. Constitutive equations for cyclic plasticity and cyclic viscoplasticity [J]. International Journal of Plasticity, 1989, 5(3): 247 − 302. doi: 10.1016/0749-6419(89)90015-6
|
[28] |
CHABOCHE J L. On some modifications of kinematic hardening to improve the description of ratchetting effects [J]. International Journal of Plasticity, 1991, 7(7): 661 − 678. doi: 10.1016/0749-6419(91)90050-9
|
[29] |
NAIR V, HINTON G E. Rectified linear units improve restricted Boltzmann machines [C]// The 27th International Conference on Machine Learning (ICML). Haifa Israel, Omnipress, 2010: 807 − 814.
|
[30] |
BOCHKOVSKIY A, WANG C Y, LIAO H Y M. Yolov4: Optimal speed and accuracy of object detection [J]. Computer Vision and Pattern Recognition, 2020, 23: 1 − 17. doi: 10.48550/arXiv.2004.10934
|
[31] |
XU L, NIE X, FAN J, et al. Cyclic hardening and softening behavior of the low yield point steel BLY160: Experimental response and constitutive modeling [J]. International Journal of Plasticity, 2016, 78: 44 − 63. doi: 10.1016/j.ijplas.2015.10.009
|
[32] |
CHO K, VAN MERRIËNBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation [C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha Qatar, Association for Computational Linguistics, 2014: 1724 − 1734.
|
[33] |
SUTSKEVER I, VINYALS O, LE Q V. Sequence to sequence learning with neural networks [C]// Twenty-eighth Conference on Neural Information Processing Systems (NIPS). Montréal Canada, MIT Press, 2014: 3104 − 3112.
|
[34] |
KINGMA D P, BA J. Adam: A method for stochastic optimization [C]// The 3rd International Conference for Learning Representations (ICLR). San Diego US, OpenReview.net, 2015.
|
[35] |
PASZKE A, GROSS S, MASSA F, et al. Pytorch: An imperative style, high-performance deep learning library [C]// Thirty-third Conference on Neural Information Processing Systems (NIPS). Vancouver Canada, MIT Press, 2019: 8026 − 8037.
|