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

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.