RESEARCH PAPER
A machine learning method for soil conditioning automated decision-making of EPBM : hybrid GBDT and Random Forest Algorithm
1
Harbin Institute of Technology, School of Mechatronics Engineering, Harbin, 150001, China
2
China Railway Construction Corporation Limited, Changsha, 410100, China
Publication date: 2022-06-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2022;24(2):237-247
HIGHLIGHTS
- A method hybrid two machine learning algorithms to predict the dosage of foam is proposed.
- GBDT is used to select geological parameters with big impact on the dosage of foam.
- Considering drive parameters as decision-making factors improves the practicability.
- Results shows that the prediction model performs better than other algorithms in accuracy.
- The proposed method can realize real-time decision-making compared with experiment.
KEYWORDS
soil conditioningautomated decision-makinghybrid algorithmgeological parametersdrive parametersfeature selection
ABSTRACT
There lacks an automated decision-making method for soil conditioning of EPBM with high accuracy and efficiency that is applicable to changeable geological conditions and takes drive parameters into consideration. A hybrid method of Gradient Boosting Decision Tree (GBDT) and random forest algorithm to make decisions on soil conditioning using foam is proposed in this paper to realize automated decision-making. Relevant parameters include decision parameters (geological parameters and drive parameters) and target parameters (dosage of foam). GBDT, an efficient algorithm based on decision tree, is used to determine the weights of geological parameters, forming 3 parameters sets. Then 3 decision-making models are established using random forest, an algorithm with high accuracy based on decision tree. The optimal model is obtained by Bayesian optimization. It proves that the model has obvious advantages in accuracy compared with other methods. The model can realize real-time decision-making with high accuracy under changeable geological conditions and reduce the experiment cost.
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CITATIONS (3):
1.
Supply Sequence Modelling Using Hidden Markov Models
Anna Borucka, Edward Kozłowski, Rafał Parczewski, Katarzyna Antosz, Leszek Gil, Daniel Pieniak
Applied Sciences
Anna Borucka, Edward Kozłowski, Rafał Parczewski, Katarzyna Antosz, Leszek Gil, Daniel Pieniak
Applied Sciences
2.
Machine learning–informed soil conditioning for mechanized shield tunneling
Shuying Wang, Xiao Yuan, Tongming Qu
Computer-Aided Civil and Infrastructure Engineering
Shuying Wang, Xiao Yuan, Tongming Qu
Computer-Aided Civil and Infrastructure Engineering
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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