US 12,454,800 B2
Intelligent construction control method, apparatus and system for mixing pile with split-grouting
Peizhi Zhuang, Jinan (CN); Chuanyi Ma, Jinan (CN); Mingpeng Liu, Jinan (CN); Jialiang Zhang, Jinan (CN); Ning Zhang, Jinan (CN); Yuanshun Qian, Jinan (CN); Chaoji Li, Jinan (CN); Shengtao Zhang, Jinan (CN); Kangxu Wang, Jinan (CN); Xiuguang Song, Jinan (CN); and Haoxiang Li, Jinan (CN)
Assigned to SHANDONG UNIVERSITY, (CN); and SHANDONG HI-SPEED GROUP CO, LTD, (CN)
Filed by SHANDONG UNIVERSITY, Jinan (CN); and SHANDONG HI-SPEED GROUP CO, LTD, Jinan (CN)
Filed on Jun. 14, 2024, as Appl. No. 18/743,175.
Claims priority of application No. 202410033776.7 (CN), filed on Jan. 8, 2024.
Prior Publication US 2025/0223770 A1, Jul. 10, 2025
Int. Cl. E02D 5/36 (2006.01); E02D 7/24 (2006.01); E02D 15/04 (2006.01)
CPC E02D 5/36 (2013.01) [E02D 7/24 (2013.01); E02D 15/04 (2013.01)] 9 Claims
OG exemplary drawing
 
1. An intelligent construction control method for splitting jet grouting mixing piles, comprising the following steps:
receiving real-time perception information from a construction information self-perception system, wherein the real-time perception information comprises drilling depth, output power of a drilling rig, torque of a drill bit, axial force of a drill rod and pore water pressure of the drill bit;
inputting the real-time perception information into the trained machine-learning model for stratum information, establishing a nonlinear implicit correspondence between the real-time perception information and construction stratum conditions, and then outputting real-time stratum state information; and
inputting the real-time stratum state information into the trained self-matching machine-learning model for optimal construction parameters, establishing a nonlinear implicit correspondence between the real-time stratum state information and optimal construction parameters, and then outputting current optimal construction parameters; specific details are as follows:
1) data preprocessing: removing data outliers based on a statistical analysis method, and using a regression interpolation method to fill local sparse data;
2) Feature selection and standardization: evaluating a correlation degree between features and target variables using a chi-square test method, and selecting five features with a highest correlation degree, and converting selected five features to a normal distribution with a mean value of 0 and a variance of 1 using a Z-score standardization method, to make them comparable;
3) Data division: dividing a database into n datasets with approximate sizes using a K-fold cross validation method;
4) User-defined kernel function: using a user-defined polynomial kernel function to map the data to a high-dimensional feature space, therefore transferring nonlinear problems to linear problems;
5) model training and evaluating: performing K-round training to evaluate on the built construction parameter self-matching model, where n−1 datasets in each round are used for training, and remaining datasets are used as a test set, and finally calculating the average value among K-round evaluation results as a performance index of the model, and performing next round of training if not meet the requirement;
6) optimal construction parameters prediction: inputting real-time stratum state information into a trained self-matching machine-learning model for optimal construction parameters, and obtaining predicted optimal construction parameters through forward propagation;
the self-matching machine-learning model for optimal construction parameters is a specially designed SVM class model, a kernel function of the model is the user-defined polynomial kernel function.