[1] |
XUE J Y, WU Z J, ZHANG F L. Seismic damage evaluation model of Chinese ancient timber buildings[J]. Advances in Structural Engineering, 2015, 18(10): 1671-1683.
|
[2] |
LYU Mengning, ZHU Xinqun, YANG Qingshan. Dynamic field monitoring data analysis of an ancient wooden building in seismic and operational environments [J]. Earthquakes and Structures, 2016, 11(6): 1043-1060.
|
[3] |
BONALI E, PESCI A, CASULA G. Deformation of ancient buildings inferred by terrestrial laser scanning methodology: the cantalovo church case study[J]. Archaeometry, 2014, 56(4): 703-716.
|
[4] |
FREGONESE L, BARBIERI G, BIOLZI L, et al. Surveying and monitoring for vulnerability assessment of an ancient building[J]. Sensors, 2013, 13(8): 9747-9773.
|
[5] |
ROSOWSKY D V, BULLEIT W M. Load duration effects in wood members and connections: order statistics and critical loads[J]. Structural Safety, 2002, 24(2-4): 347-362.
|
[6] |
NGUYEN M N, LEICESTER R H, WANG c h, et al. Probabilistic procedure for design of untreated timber piles under marine borer attack[J]. Reliability Engineering and System Safety, 2008, 93(3): 482-488.
|
[7] |
DAI Lu, YANG Na, ZHANG Lei. Monitoring crowd load effect on typical ancient Tibetan building [J]. Structural Control & Health Monitoring, 2016, 23(7): 998-1014.
|
[8] |
瞿伟廉, 王雪亮. 基于DOL强度衰减模型的古建筑木桁架的剩余寿命预测[J]. 华中科技大学学报, 2008, 25(3): 1-4.
|
[9] |
FANG Shiqiang, ZHANG Kun, ZHANG Hui, et al. A study of traditional blood lime mortar for restoration of ancient buildings[J].Cement and Concrete Research, 2015, 76: 232-241.
|
[10] |
YANG Na, LI Peng, LAW S S. Experimental research on mechanical properties of timber in ancient Tibetan building[J]. Journal of Materials in Civil Engineering, 2012, 24(6): 635-643.
|
[11] |
ZHANG Xicheng, XUE Jianyang, ZHAO Hongtie, et al. Experimental study on Chinese ancient timber-frame building by shaking table test[J]. Structural Engineering and Mechanics, 2011, 40(4): 453-469.
|
[12] |
WYSOCKI A, AWRYN′CZUK M. Elman neural network for modeling and predictive control of delayed dynamic systems[J]. Archives of Control Sciences, 2016, 26(1): 117-142.
|
[13] |
CHANDRA R. Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction[J]. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(12): 3123-3136.
|
[14] |
SHEIKHAN M, ARABI M A, GHARAVIAN D. Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: A comparative study[J]. 2015, 27(4): 340-357.
|
[15] |
毛澄映, 喻新欣, 薛云志. 基于粒子群优化的测试数据生成及其实证分析[J]. 计算机研究与发展, 2013, 50(2): 260-268.MAO Chengying, YU Xinxin, XUE Yunzhi. Algorithm design and empirical analysis for particle swarm optimization-based test data generation[J]. Journal of Computer Research and Development, 2013, 50(2): 260-268.
|
[16] |
CHOUIKHI N, AMMA, B, ROKBAN N, et al. PSO-based analysis of echo state network parameters for time series forecasting[J]. Applied Soft Computing, 2017, 55: 211-225.
|
[17] |
JAFARI M, HOSEYNI S A M, CHALESHTARI M H. Determination of optimal parameters for perforated plates with quasi-triangular cutout by PSO[J]. Structural Engineering and Mechanics, 2016, 60(5): 795-807.
|
[18] |
PALMER S, GORSE D, MUK-PAVIC E. Neural networks and particle swarm optimization for function approximation in Tri-SWACH hull design[C]// Proceedings of the 16th International Conference on Engineering Applications of Neural Networks. Rhodes, Greece: ACM, 2015: 32-36.
|
[19] |
RAZA S, MOKHLIS H, AROF H, et al. Minimum-features-based ANN-PSO approach for islanding detection in distribution system[J]. IET Renewable Power Generation, 2016, 10(9): 1255-1263.
|
[20] |
SITHARTHAN R, GEETHANJALI M. An adaptive Elman neural network with C-PSO learning algorithm based pitch angle controller for DFIG based WECS[J]. Journal of vibration and control, 2017, 23(5): 716-730.
|
[21] |
ZHOU C, DING L Y, HE R. PSO-based Elman neural network model for predictive control of air chamber pressure in slurry shield tunneling under Yangtze River[J]. Automation in construction, 2013, 36(5): 208-217.
|
[22] |
QIN Shanshan, WANG Jianzhou, WU Ji. A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China[J]. International Journal of Green Energy, 2016, 13(6): 595-607.
|
[1] |
XUE J Y, WU Z J, ZHANG F L. Seismic damage evaluation model of Chinese ancient timber buildings[J]. Advances in Structural Engineering, 2015, 18(10): 1671-1683.
|
[2] |
LYU Mengning, ZHU Xinqun, YANG Qingshan. Dynamic field monitoring data analysis of an ancient wooden building in seismic and operational environments [J]. Earthquakes and Structures, 2016, 11(6): 1043-1060.
|
[3] |
BONALI E, PESCI A, CASULA G. Deformation of ancient buildings inferred by terrestrial laser scanning methodology: the cantalovo church case study[J]. Archaeometry, 2014, 56(4): 703-716.
|
[4] |
FREGONESE L, BARBIERI G, BIOLZI L, et al. Surveying and monitoring for vulnerability assessment of an ancient building[J]. Sensors, 2013, 13(8): 9747-9773.
|
[5] |
ROSOWSKY D V, BULLEIT W M. Load duration effects in wood members and connections: order statistics and critical loads[J]. Structural Safety, 2002, 24(2-4): 347-362.
|
[6] |
NGUYEN M N, LEICESTER R H, WANG c h, et al. Probabilistic procedure for design of untreated timber piles under marine borer attack[J]. Reliability Engineering and System Safety, 2008, 93(3): 482-488.
|
[7] |
DAI Lu, YANG Na, ZHANG Lei. Monitoring crowd load effect on typical ancient Tibetan building [J]. Structural Control & Health Monitoring, 2016, 23(7): 998-1014.
|
[8] |
瞿伟廉, 王雪亮. 基于DOL强度衰减模型的古建筑木桁架的剩余寿命预测[J]. 华中科技大学学报, 2008, 25(3): 1-4.
|
[9] |
FANG Shiqiang, ZHANG Kun, ZHANG Hui, et al. A study of traditional blood lime mortar for restoration of ancient buildings[J].Cement and Concrete Research, 2015, 76: 232-241.
|
[10] |
YANG Na, LI Peng, LAW S S. Experimental research on mechanical properties of timber in ancient Tibetan building[J]. Journal of Materials in Civil Engineering, 2012, 24(6): 635-643.
|
[11] |
ZHANG Xicheng, XUE Jianyang, ZHAO Hongtie, et al. Experimental study on Chinese ancient timber-frame building by shaking table test[J]. Structural Engineering and Mechanics, 2011, 40(4): 453-469.
|
[12] |
WYSOCKI A, AWRYN′CZUK M. Elman neural network for modeling and predictive control of delayed dynamic systems[J]. Archives of Control Sciences, 2016, 26(1): 117-142.
|
[13] |
CHANDRA R. Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction[J]. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(12): 3123-3136.
|
[14] |
SHEIKHAN M, ARABI M A, GHARAVIAN D. Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: A comparative study[J]. 2015, 27(4): 340-357.
|
[15] |
毛澄映, 喻新欣, 薛云志. 基于粒子群优化的测试数据生成及其实证分析[J]. 计算机研究与发展, 2013, 50(2): 260-268.MAO Chengying, YU Xinxin, XUE Yunzhi. Algorithm design and empirical analysis for particle swarm optimization-based test data generation[J]. Journal of Computer Research and Development, 2013, 50(2): 260-268.
|
[16] |
CHOUIKHI N, AMMA, B, ROKBAN N, et al. PSO-based analysis of echo state network parameters for time series forecasting[J]. Applied Soft Computing, 2017, 55: 211-225.
|
[17] |
JAFARI M, HOSEYNI S A M, CHALESHTARI M H. Determination of optimal parameters for perforated plates with quasi-triangular cutout by PSO[J]. Structural Engineering and Mechanics, 2016, 60(5): 795-807.
|
[18] |
PALMER S, GORSE D, MUK-PAVIC E. Neural networks and particle swarm optimization for function approximation in Tri-SWACH hull design[C]// Proceedings of the 16th International Conference on Engineering Applications of Neural Networks. Rhodes, Greece: ACM, 2015: 32-36.
|
[19] |
RAZA S, MOKHLIS H, AROF H, et al. Minimum-features-based ANN-PSO approach for islanding detection in distribution system[J]. IET Renewable Power Generation, 2016, 10(9): 1255-1263.
|
[20] |
SITHARTHAN R, GEETHANJALI M. An adaptive Elman neural network with C-PSO learning algorithm based pitch angle controller for DFIG based WECS[J]. Journal of vibration and control, 2017, 23(5): 716-730.
|
[21] |
ZHOU C, DING L Y, HE R. PSO-based Elman neural network model for predictive control of air chamber pressure in slurry shield tunneling under Yangtze River[J]. Automation in construction, 2013, 36(5): 208-217.
|
[22] |
QIN Shanshan, WANG Jianzhou, WU Ji. A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China[J]. International Journal of Green Energy, 2016, 13(6): 595-607.
|