基于DBN的盾构隧道施工参数优化方法研究

doi:10.13722/j.cnki.jrme.2014.0282

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摘要为严格控制地表沉降，最大限度地减小隧道盾构施工给周围环境带来的不利影响，针对现有方法的不足，基于动态贝叶斯网络(DBN)理论提出一种盾构隧道施工参数优化方法，并将其应用于武汉某盾构隧道工程。首先选定待优化施工参数作为拟建网络结构的网络节点；然后设定离散化规则划分节点状态，以离散化工程实测数据完成参数学习，得到完整的DBN优化模型；该模型经验证用于工程施工参数优化，结果表明：该模型能够正确反映地表沉降与各施工参数之间的内在联系，具备一定的科学性；基于该模型进行反向诊断推理，能够确定各施工参数的最优设定区间；在该区间内对施工参数进行实时优化，能够降低地表沉降风险；该方法实时性强，具备一定的工程应用价值。

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关键词 ：隧道工程, 地表沉降, 盾构隧道, 动态贝叶斯, 参数优化

Abstract：To control the surface subsidence and minimize the negative influence from shield tunneling to surrounding environment，a DBN-based，parameters optimization method in shield tunneling was proposed according to the deficiency of the existing method and was applied to a shield tunnel in Wuhan，China. Firstly the main construction parameters were selected to optimize as node variables and the network structure was built. Then discretizing rules were set to divide the nodes states and measured data were discredited for parameter learning to get the complete DBN optimization model. After model validation with engineering measured data applied this model to parameters optimization. The results show that this model can reflect the inner link between the surface subsidence and shield construction parameters. Based on this model the optimal setting-range of each construction parameters can be determined and within the range construction parameters can be adjusted and optimized real-timely，which is helpful to reduce surface subsidence；This method is valuable in practice

Key words： tunneling engineering surface subsidence shield tunneling dynamic Bayesian networks(DBN) parameters optimization

引用本文:

丁保军，吴贤国，张立茂，仲景冰，刘洋. 基于DBN的盾构隧道施工参数优化方法研究[J]. 岩石力学与工程学报, 2015, 34(s1): 3215-3222.
DING Baojun，WU Xianguo，ZHANG Limao，ZHONG Jingbing，LIU Yang. OPTIMIZATION OF SHIELD TUNNELING PARAMETERS BASED ON DYNAMIC BAYESIAN NETWORKS. , 2015, 34(s1): 3215-3222.

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http://www.rockmech.org/CN/10.13722/j.cnki.jrme.2014.0282 或 http://www.rockmech.org/CN/Y2015/V34/Is1/3215

[4]	熊静，喻钢. 基于多级神经网络的盾构法隧道施工参数优化[J]. 计算机工程与科学，2006，28(5)：133-135.(XIONG Jing，YU Gang. Construction parameter optimization for shield tunneling based on the multi-level ANN[J]. Computer Engineering and Science，2006，28(5)：133-135.(in Chinese))
[5]	孙钧，王东栋. 地铁施工变形预测与控制的智能方法[J]. 施工技术，2009，(1)：3-9.(SUN Jun，WANG Dongdong. Intelligent prediction and control method of deformation in metro construction[J]. Construction Technology，2009，(1)：3-9.(in Chinese)) " target="_blank">
[8]	ZHANG L M，WU X G，DING L Y，et al. Decision support analysis for safety control in complex project environments based on Bayesian Networks[J]. Expert Systems with Applications，2013，40(11)：4 273- 4 282. " target="_blank">
[9]	KHODAKARAMI V，ABDI A. Project cost risk analysis：A Bayesian networks approach for modeling dependencies between cost items[J]. International Journal of Project Management，2014，32(7)：1 233- 1 245. " target="_blank">
[12]	周忠宝，周经伦，金光，等. 基于贝叶斯网络的概率安全评估方法研究[J]. 系统工程学报，2006，21(6)：636-643.(ZHOU Zhongbao，ZHOU Jinglun，JIN Guang，et al. Probabilistic safety assessment research based on Bayesian networks[J]. Journal of Systems Engineering，2006，21(6)：636-643.(in Chinese)) " target="_blank">
[13]	晏祎. 基于动态贝叶斯网络的隧道盾构施工诱发变形分析[硕士学位论文][D]. 武汉：华中科技大学，2013.(YAN Yi. Shield tunneling induced deformation analysis based on dynamic bayesian network[M. S. Thesis][D]. Wuhan：Huazhong University of Science and Technology，2013.(in Chinese)) " target="_blank">
[3]	沈鋩杰，张子新，蒋华钦. 盾构法隧道地表变形影响因素多尺度数值模拟[J]. 地下空间与工程学报，2013，9(2)：263-269.(SHEN Mangjie，ZHANG Zixin，JIANG Huaqin. Multi-scale numerical simulation on the influencing factors of surface deformations in shield tunneling[J]. Chinese Journal of Underground Space and Engineering， 2013，9(2)：263-269.(in Chinese)) " target="_blank">
[7]	乔金丽，范永利，刘波，等. 基于改进BP网络的盾构隧道开挖地表沉降预测[J]. 地下空间与工程学报，2012，8(2)：352-357.(QIAO Jinli，FAN Yongli，LIU Bo，et al. Predicting the surface settlement by shield tunneling based on modified BP network[J]. Chinese Journal of Underground Space and Engineering，2012，8(2)：352-357.(in Chinese)) " target="_blank">
[11]	魏冠军，党亚民，章传银. 应用贝叶斯动态模型的地基沉降概率分析与预测[J]. 测绘科学，2012，37(2)：52-53.(WEI Guanjun，DANG Yamin，ZHANG Chuanying. Foundation settlement probability analysis and prediction based on Bayes dynamic linear model[J]. Science of Surveying and Mapping，2012，37(2)：52-53.(in Chinese)) " target="_blank">
[15]	CODETTA-RAITERI D，BOBBIO A，MONTANI S，et al. A dynamic Bayesian network based framework to evaluate cascading effects in a power grid[J]. Engineering Applications of Artificial Intelligence，2012，25(4)：683-697. " target="_blank">
[1]	王建秀，付慧仙，朱雁飞，等. 基于地层损失的盾构沉降计算方法研究进展[J]. 地下空间与工程学报，2010，6(1)：112-119.(WANG Jianxiu，FU Huixian，ZHU Yanfe，et al. Advance in calculation of subsidence caused by shield tunnel based on strata loss[J]. Chinese Journal of Underground Space and Engineering，2010，6(1)：112-119.(in Chinese)) " target="_blank">
[2]	姚爱军，赵强，管江，等. 基于北京地层地铁隧道施工的Peck公式的改进[J]. 地下空间与工程学报，2010，6(4)：789-793.(YAO Aijun，ZHAO Qiang，GUAN Jiang，et al. Improvement of peck formula in subway construction in Beijing[J]. Chinese Journal of Underground Space and Engineering，2010，6(4)：789-793.(in Chinese)) " target="_blank">
[6]	傅德明，周文波. 土压盾构技术在我国地铁隧道工程中的应用和发展[J]. 岩石力学与工程学报，2004，23(增2)：4 888-4 892.(FU Deming， ZHOU Wenbo. Application and development of EPB shield tunneling in metro tunnel projects in China[J]. Chinese Journal of Rock Mechanics and Engineering，2004，23(Supp.2)：4 888-4 892.(in Chinese)) " target="_blank">
[10]	周健，王红卫，吴邵海. 盾构法施工风险的多态贝叶斯网络模型分析[J]. 同济大学学报：自然科学版，2013，(2)：186-190.(ZHOU Jian，WANG Hongwei，WU Shaohai. Risk analysis of shield construction by polymorphic Bayesian networks model[J]. Journal of Tongji University：Natural Science，2013，(2)：186-190.(in Chinese)) " target="_blank">
[14]	CAI B，LIU Y，ZHANG Y，et al. A dynamic bayesian networks modeling of human factors on offshore blowouts[J]. Journal of Loss Prevention in the Process Industries，2013，26(4)：639-649. " target="_blank">
[16]	CHENG J. The fault diagnosis method of mixed-model assembly line based on dynamic causality bayesian network[J]. Applied Mechanics and Materials，2013，2341(310)：592-595. " target="_blank">
[17]	李维乾，解建仓，张永进，等. 动态贝叶斯网络在水文预报中的应用[J]. 计算机工程与应用，2010，46(6)：231-234.(LI Weiqian，XIE Jiancang，ZHANG Yongjin，et al. Application of dynamic Bayesian networks in hydrologic forecast[J]. Computer Engineering and Applications，2010，46(6)：231-234.(in Chinese))