基于多元结构应力特征的沉井基础下沉速度预测

doi:10.13722/j.cnki.jrme.2022.0581

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (1283 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The sinking speed prediction of open caisson foundations has important practical significance for ensuring the sinking safety and sinking steady，and to prevent potential construction risks. Based on the structural stress monitoring data at the bottom of open caisson foundations，a two-dimensional convolutional neural network (CNN) and a three-dimensional CNN are applied for proposing a sinking speed category prediction model and a sinking speed value prediction model. The spatial and spatial-temporal characteristics of the structural stress monitoring data are extracted to predict the sinking speed. The accuracy and practicability of the two prediction models were verified by applying them to an open caisson foundation of the main tower in the Changtai Yangtze River Bridge Project. Then，the real-time prediction of the sinking speed category in the sinking process was simulated，and the influence of the prediction step and the spatial-temporal characteristics of the structural stress on prediction accuracy were analyzed. The results show that the proposed models can successfully predict the category and value of the sinking speed. The reliability and practicability of the models were verified in practical engineering that the proposed sinking speed prediction models have good prediction performance. Moreover，the spatial-temporal characteristics of structural stress have an important influence on prediction accuracy. The real-time sinking speed prediction of open caisson foundations was achieved，which has important reference value for intelligent decision-making in the monitoring during sinking process.

Key words： foundation engineering open caisson foundation sinking speed prediction convolutional neural network deep learning Changtai Yangtze River Bridge

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	DONG Xuechao1
	2
	GUO Mingwei1
	2
	WANG Shuilin1
	2

Cite this article:

DONG Xuechao1,2,GUO Mingwei1, et al. Sinking speed prediction of an open caisson foundation based on the characteristics of multivariate structural stress data[J]. , 2022, 41(S2): 3476-3487.

URL:

https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2022.0581 OR https://rockmech.whrsm.ac.cn/EN/Y2022/V41/IS2/3476

[1] 李军堂，秦顺全，张瑞霞. 桥梁深水基础的发展和展望[J]. 桥梁建设，2020，50(3)：17–24.(LI Juntang，QIN Shunquan，ZHANG Ruixia. Developments and prospects of deep water foundations for bridge[J]. Bridge Construction，2020，50(3)：17–24.(in Chinese)) [2] 穆保岗，朱建民，龚维明. 大型沉井设计、施工及监测[M]. 北京：中国建筑工业出版社，2015：1–21.(MU Baogang，ZHU Jianmin，GONG Weiming. Design，construction and monitoring of large open caissons[M]. Beijing：China Architecture and Building Press，2015：1–21.(in Chinese)) [3] 龚维明，王正振，戴国亮，等. 长江大桥基础的应用与发展[J]. 桥梁建设，2019，49(6)：13–23.(GONG Weiming，WANG Zhengzhen，DAI Guoliang，et al. Application and development of foundations of Changjiang River Bridges[J]. Bridge Construction，2019，49(6)：13–23.(in Chinese)) [4] 段良策，殷奇. 沉井设计与施工[M]. 上海：同济大学出版社，2006：1–10.(DUAN Liangce，YIN Qi. Design and construction of caissons[M]. Shanghai：Tongji University Press，2006：1–10.(in Chinese)) [5] ALLENBY D，WALEY G，KILBURN D. Examples of open caisson sinking in Scotland[J]. Proceedings of the Institution of Civil Engineers-Geotechnical Engineering，2009，162(1)：59–70. [6] JIANG B N，WANG M T，CHEN T，et al. Experimental study on the migration regularity of sand outside a large，deep-water，open caisson during sinking[J]. Ocean Engineering，2019，193：106601. [7] ROYSTON R，SHEIL B B，BYRNE B W. Monitoring the construction of a large-diameter caisson in sand[J]. Proceedings of the Institution of Civil Engineers-Geotechnical Engineering，2020，175(3)：1–17. [8] 施洲，李思阳，杨仕力，等. 超大型沉井基础下沉中后期摩阻力特性及突沉机制研究[J]. 岩石力学与工程学报，2019，38(增2)： 3 894–3 904.(SHI Zhou，LI Siyang，YANG Shili，et al. Study on the characteristics of friction resistance and the mechanism of sudden sinking in the middle and late sinking stages of super large caisson foundation[J]. Chinese Journal of Rock Mechanics and Engineering，2019，38(Supp.2)：3 894–3 904.(in Chinese)) [9] 朱建民，龚维明，穆保岗，等. 南京长江四桥北锚碇沉井下沉安全监控研究[J]. 建筑结构学报，2010，31(8)：112–117.(ZHU Jianmin，GONG Weiming，MU Baogang，et al. Sinking safety monitoring research on north anchorage caisson of the Fourth Nanjing Yangtze River Bridge[J]. Journal of Building Structures，2010，31(8)：112–117.(in Chinese)) [10] 米长江，王岩，穆保岗，等. 马鞍山长江公路大桥南锚碇沉井下沉分析[J]. 桥梁建设，2011，(6)：6–11.(MI Changjiang，WANG Yan，MU Baogang，et al. Analysis of sinking of south anchorage caisson of Maanshan Changjiang River Highway Bridge[J]. Bridge Construction，2011，(6)：6–11.(in Chinese)) [11] 穆保岗，朱建民，牛亚洲. 南京长江四桥北锚碇沉井监控方案及成果分析[J]. 岩土工程学报，2011，33(2)：269–274.(MU Baogang，ZHU Jianmin，NIU Yazhou. Monitoring and analysis of north anchorage caisson of Fourth Nanjing Yangtze River Bridge[J]. Chinese Journal of Geotechnical Engineering，2011，33(2)：269–274.(in Chinese)) [12] 孙彦晓. 大型沉井下沉对土体变形影响的研究[硕士学位论文][D]. 南京：东南大学，2019.(SUN Yanxiao. Research of soil behavior with the sinking of large caisson[M. S. Thesis][D]. Nanjing：Southeast University，2019.(in Chinese)) [13] 潘亚洲，王琛，梁发云. 大型沉井下沉阻力分布特征及相关工程问题[J]. 结构工程师，2020，36(6)：134–143.(PAN Yazhou，WANG Chen，LIANG Fayun. Sinking resistance distribution characteristics of large open caissons and related engineering problems[J]. Structural Engineers，2020，36(6)：134–143.(in Chinese)) [14] 张计炜. 深厚软土地区超大型沉井下沉行为及稳定性分析研究[硕士学位论文][D]. 杭州：浙江大学，2020.(ZHANG Jiwei. The research on subsidence behavior and stability analysis of ultra large open caisson in deep soft soil area[M. S. Thesis][D]. Hangzhou：Zhejiang University，2019.(in Chinese)) [15] 王红霞，王徳禹. 大型沉井结构施工力学模型的研究[J]. 力学季刊，2003，(1)：68–74.(WANG Hongxia，WANG Deyu. Mechanics model study in large extra caissons construction[J]. Chinese Quarterly of Mechanics，2003，(1)：68–74.(in Chinese)) [16] 龚维明，朱建民，穆保岗，等. 南京长江四桥北锚碇沉井首次降排水下沉研究[J]. 岩土工程学报，2010，32(增2)：537–540.(GONG Weiming，ZHU Jianming，MU Baogang，et al. Dewater sinking of north anchorage caisson of Fourth Nanjing Yangtze River Bridge[J]. Chinese Journal of Geotechnical Engineering，2010，32(Supp.2)：537–540.(in Chinese)) [17] 董晓朋. 超深大沉井施工期间结构内力试验研究[硕士学位论文][D]. 成都：西南交通大学，2018.(DONG Xiaopeng. Experimental research on structural internal force of the ultra-deep and large caisson during construction[M. S. Thesis][D]. Chengdu：Southwest Jiaotong University，2018.(in Chinese)) [18] 刘诗洋，陈祖煜，张云旆，等. 基于卷积神经网络对TBM塌方段的反演分析[J]. 固体力学学报，2021，42(3)：287–301.(LIU Shiyang，CHEN Zuyu，ZHANG Yunpei，et al. Back analysis of the TBM collapse section based on convolutional neural networks[J]. Chinese Journal of Solid Mechanics，2021，42(3)：287–301.(in Chinese)) [19] SWAPNA G，KP S，VINAYAKUMAR R. Automated detection of diabetes using CNN and CNN-LSTM network and heart rate signals[J]. Procedia Computer Science，2018，132：1 253–1 262. [20] CAVALLI S，AMORETTI M. CNN-based multivariate data analysis for bitcoin trend prediction[J]. Applied Soft Computing，2021，101：107065. [21] CHEN H Z，CHEN A，XU L L，et al. A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources[J]. Agricultural Water Management，2020，240：106303. [22] GOODFELLOW I，BENGIO Y，COURVILLE A. Deep learning[M]. Cambridge：MIT Press，2016：29–487. [23] NAIR V，HINTON G E. Rectified linear units improve restricted Boltzmann machines[C]// Proceedings of the International Conference on Machine Learning. [S. l.]：[s. n.]，2010：807–814. [24] 邱锡鹏. 神经网络与深度学习[M]. 北京：机械工业出版社，2020：81–108.(QIU Xipeng. Neural networks and deep learning[M]. Beijing：China Machine Press，2020：81–108.(in Chinese)) [25] GLOROT X，BORDES A，BENGIO Y. Deep sparse rectifier neural networks[C]// Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research. [S. l.]：[s. n.]，2011：315–323. [26] 秦顺全，徐伟，陆勤丰，等. 常泰长江大桥主航道桥总体设计与方案构思[J]. 桥梁建设，2020，50(3)：1–10.(QIN Shunquan，XU Wei，LU Qinfeng，et al. Overall design and concept development for main navigational channel bridge of Changtai Changjiang River Bridge[J]. Bridge Construction，2020，50(3)：1–10.(in Chinese)) [27] 李小珍，李星星，舒晓峰，等. 常泰长江大桥主桥风–车–线–桥耦合分析[J]. 桥梁建设，2021，51(3)：17–24.(LI Xiaozhen，LI Xingxing，SHU Xiaofeng，et al. Analysis of wind–train–track–bridge interaction for main bridge of Changtai Changjiang River Bridge[J]. Bridge Construction，2021，51(3)：17–24.(in Chinese)) [28] 秦顺全，谭国宏，陆勤丰，等. 超大沉井基础设计及下沉方法研究[J]. 桥梁建设，2020，50(5)：1–9.(QIN Shunquan，TAN Guohong，LU Qinfeng，et al. Research on design and sinking methods for super-large caisson foundation[J]. Bridge Construction，2020，50(5)：1–9.(in Chinese)) [29] 郭明伟，董学超，沈孔健，等. 超大沉井基础取土下沉端阻力变化规律研究[J]. 岩石力学与工程学报，2021，40(增1)：2 976–2 985. (GUO Mingwei，DONG Xuechao，SHEN Kongjian，et al. Study on the variation of the bottom resistance during sinking stage of super large caisson foundation[J]. Chinese Journal of Rock Mechanics and Engineering，2021，40(Supp.1)：2 976–2 985.(in Chinese)) [30] 施洲，李佳奇，秦搏聪，等. 大型沉井基础初沉阶段受力特性及开裂控制[J]. 中国铁道科学，2021，42(2)：9–18.(SHI Zhou，LI Jiaqi，QIN Bocong，et al. Mechanical characteristics and cracking control of large caisson foundation in initial sinking stage[J]. China Railway Science，2021，42(2)：9–18.(in Chinese)) [31] 周小雄，龚秋明，殷丽君，等. 基于BLSTM-AM模型的TBM稳定段掘进参数预测[J]. 岩石力学与工程学报，2020，39(增2)：3 505– 3 515.(ZHOU Xiaoxiong，GONG Qiuming，YIN Lijun，et al. Predicting boring parameters of TBM stable stage based on BLSTM network combined with attention mechanism[J]. Chinese Journal of Rock Mechanics and Engineering，2020，39(Supp.2)：3 505–3 515.(in Chinese)) [32] 贾俊平，何晓群，金勇进. 统计学[M]. 北京：中国人民大学出版社，2000：233–317.(JIA Junping，HE Xiaoqun，JIN Yongjin. Statistics[M]. Beijing：China Renmin University Press，2000：233–317.(in Chinese))