Sampling tests on the selfcompacting
concrete mixture performance after onsite 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 postpump performance of self-compacting concrete mixture are established. The results show the
following: (1) The postpump performance of selfcompacting 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 selfcompacting
concrete pipes in steelshell immersed tubes in the
ShenzhenZhongshan Link.
SONG Shenyou
,
YU Fang
,
CHEN Wenguang
,
XU Jinjun
,
ZHAO Jiaqi
,
FAN Zhihong
. Evaluation and Prediction Models of PostPump
Performance of SelfCompacting
Concrete Mixture in SteelShell Immersed Tube in ShenzhenZhongshan Link[J]. Tunnel Construction, 2021
, 41(10)
: 1682
-1691
.
DOI: 10.3973/j.issn.2096-4498.2021.10.007
