摘要
为保证钢壳沉管自密实混凝土的入舱质量,对钢壳沉管E1-E4管节现场泵后的自密实混凝土拌合物性能进行取样测试,基于灰色关联分析、支持向量机和贝叶斯推断,对影响钢壳沉管自密实混凝土拌合物泵后性能关键参数的敏感性进行评价,同时建立自密实混凝土拌合物泵后性能预测模型。结果表明:1)泵送距离、弯头数量、输送时间和环境温度均与钢壳沉管自密实混凝土拌合物的泵后性能(入模温度、扩展度、V漏斗流动时间、L型仪H 2/H 1及含气量)存在关联性,且敏感性大小为环境温度>输送时间>弯头数量>泵送距离;2)经过工程实际验证,建立的支持向量机非线性预测模型和贝叶斯线性概率预测模型的精度均较高且具有较好的鲁棒性;3)支持向量机非线性预测模型的预测精度要高于贝叶斯线性概率模型,而贝叶斯显式概率模型的实用性强于支持向量机隐式模型,此2类预测模型的结合使用,成功指导了深中通道钢壳沉管自密实混凝土后续管节的施工质量控制。
Sampling tests on the self-compacting concrete mixture performance after on-site pumping of steel shell immersed pipe(pipes E1~E4)are conducted to ensure the quality of the self-compacting concrete in the steel-shell immersed tube in the warehouse.The sensitivity of key parameters affecting the post-pump performance of self-compacting concrete mixture in steel shell immersed tube is evaluated based on the grey relational analysis,support vector machine(SVM),and Bayesian inference.In addition,prediction models for the post-pump performance of self-compacting concrete mixture are established.The results show the following:(1)The post-pump performance of self-compacting concrete mixture in steel shell immersed tube(i.e.,molding temperature,slump flow,V-funnel flow time,L-type instrument H 2/H 1,and air content)is correlated with pumping distance,number of elbows,delivery time,and ambient temperature.Further,the sensitivity order for each factor is ambient temperature>delivery time>number of elbows>pumping distance.(2)The engineering practice shows that the established SVM nonlinear and Bayesian linear probabilistic prediction models have a high prediction accuracy and excellent robustness.(3)The SVM nonlinear prediction model has a higher prediction accuracy than that of the Bayesian linear probabilistic prediction model;however,the Bayesian explicit probabilistic model is more practical.Overall,the combined application of the two prediction model types directs the quality control of subsequent self-compacting concrete pipes in steel-shell immersed tubes in the Shenzhen-Zhongshan Link.
作者
宋神友
于方
陈文广
徐金俊
赵家琦
范志宏
SONG Shenyou;YU Fang;CHEN Wenguang;XU Jinjun;ZHAO Jiaqi;FAN Zhihong(Shenzhen-Zhongshan Link Administration Center,Zhongshan 528400,Guangdong,China;CCCC Fourth Harbor Engineering Institute Co.,Ltd.,Guangzhou 510230,Guangdong,China;College of Civil Engineering,Nanjing Tech University,Nanjing 211816,Jiangsu,China)
出处
《隧道建设(中英文)》
CSCD
北大核心
2021年第10期1682-1691,共10页
Tunnel Construction
基金
国家自然科学基金资助项目(51708289)
广东省重点领域研发计划项目(2019B111105002)
国家级大学生创新创业训练计划项目(202110291040Z,202110291096Z,202110291097Z)
江苏省大学生创新创业训练计划项目(202110291227Y)。
关键词
深中通道
自密实混凝土
拌合物性能
灰色关联分析
支持向量机
贝叶斯推断
Shenzhen-Zhongshan Link
self-compacting concrete
mixture performance
grey correlation analysis
support vector machine
Bayesian inference