深中通道钢壳沉管自密实混凝土拌合物泵后性能评价及预测模型研究

宋神友; 于方; 陈文广; 徐金俊; 赵家琦; 范志宏

doi:10.3973/j.issn.2096-4498.2021.10.007

隧道建设 >

2021 , Vol. 41 >Issue 10: 1682 - 1691

DOI: https://doi.org/10.3973/j.issn.2096-4498.2021.10.007

研究与探索

深中通道钢壳沉管自密实混凝土拌合物泵后性能评价及预测模型研究

宋神友 ,
于方 ,
陈文广 ,
徐金俊 ,
赵家琦 ,
范志宏

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(1. 深中通道管理中心，广东中山 528400； 2. 中交四航工程研究院有限公司，广东广州 510230； 3. 南京工业大学土木工程学院，江苏南京 211816)

宋神友(1974—), 男, 湖北武穴人, 1996年毕业于同济大学, 桥梁工程专业, 硕士, 教授级高级工程师, 现从事特大型桥梁与隧道技术管理工作。E-mail: 180419969@qq.com。*通信作者: 徐金俊, E-mail: jjxu_concrete@njtech.edu.cn。

网络出版日期: 2021-11-04

基金资助

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)

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Evaluation and Prediction Models of PostPump Performance of SelfCompacting Concrete Mixture in SteelShell Immersed Tube in ShenzhenZhongshan Link

SONG Shenyou ,
YU Fang ,
CHEN Wenguang ,
XU Jinjun ,
ZHAO Jiaqi ,
FAN Zhihong

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(1. ShenzhenZhongshan Link Administration Center, Zhongshan 528400, Guangdong, China; 2. CCCC Fourth Harbor Engineering Institute Co., Ltd., Guangzhou 510230, Guangdong, China; 3. College of Civil Engineering, Nanjing Tech University, Nanjing 211816, Jiangsu, China)

Online published: 2021-11-04

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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。

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

本文引用格式

宋神友 , 于方 , 陈文广 , 徐金俊 , 赵家琦 , 范志宏 . 深中通道钢壳沉管自密实混凝土拌合物泵后性能评价及预测模型研究[J]. 隧道建设, 2021 , 41(10) : 1682 -1691 . DOI: 10.3973/j.issn.2096-4498.2021.10.007

Abstract

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 H2/H1, 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.

Key words： Shenzhen-Zhongshan Link; self-compacting concrete; mixture performance; grey correlation analysis; support vector machine; Bayesian inference

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