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Intelligent Decision Method for Main Control Parameters of TBM Based on Machine Learning and Multi-objective Optimization Algorithm
https://orcid.org/http://orcid.org/0000-0003-4327-3095
https://orcid.org/http://orcid.org/0000-0001-8886-0416
https://orcid.org/http://orcid.org/0009-0000-3439-9513
https://orcid.org/http://orcid.org/0000-0002-0363-9702
Due to the poor adaptability of TBM to geological formations, there are problems in actual construction, such as unclear mechanisms and laws of rock-machine interaction, insufficient exploration and perception lag of geological conditions, and strong subjectivity in equipment excavation parameter control. This article is based on the TBM excavation dataset that is publicly available for the Yinsong Project. The main monitoring parameters during the TBM excavation process are divided into three categories: control parameters, state parameters, and performance parameters. The outliers are identified and removed using the Turkey box plot, and then the data is dimensionally reduced using the APCA method. On this basis, a rock machine interaction model (i.e. the relationship between the three types of parameters) for the TBM excavation process was established using the random forest algorithm that integrates Bayesian optimization methods. Finally, with TBM advance speed and specific energy as optimization objectives, the main control parameters of TBM were intelligently optimized using the multi-objective genetic algorithm NSGA II. After optimization, the specific energy was reduced by 5.55%, and the advance rate was increased by 65.79%. The conclusion shows that the above optimization method can achieve efficient and intelligent TBM excavation and has certain engineering application prospects.
Intelligent Decision Method for Main Control Parameters of TBM Based on Machine Learning and Multi-objective Optimization Algorithm
https://orcid.org/http://orcid.org/0000-0003-4327-3095
https://orcid.org/http://orcid.org/0000-0001-8886-0416
https://orcid.org/http://orcid.org/0009-0000-3439-9513
https://orcid.org/http://orcid.org/0000-0002-0363-9702
Due to the poor adaptability of TBM to geological formations, there are problems in actual construction, such as unclear mechanisms and laws of rock-machine interaction, insufficient exploration and perception lag of geological conditions, and strong subjectivity in equipment excavation parameter control. This article is based on the TBM excavation dataset that is publicly available for the Yinsong Project. The main monitoring parameters during the TBM excavation process are divided into three categories: control parameters, state parameters, and performance parameters. The outliers are identified and removed using the Turkey box plot, and then the data is dimensionally reduced using the APCA method. On this basis, a rock machine interaction model (i.e. the relationship between the three types of parameters) for the TBM excavation process was established using the random forest algorithm that integrates Bayesian optimization methods. Finally, with TBM advance speed and specific energy as optimization objectives, the main control parameters of TBM were intelligently optimized using the multi-objective genetic algorithm NSGA II. After optimization, the specific energy was reduced by 5.55%, and the advance rate was increased by 65.79%. The conclusion shows that the above optimization method can achieve efficient and intelligent TBM excavation and has certain engineering application prospects.
Intelligent Decision Method for Main Control Parameters of TBM Based on Machine Learning and Multi-objective Optimization Algorithm
https://orcid.org/http://orcid.org/0000-0003-4327-3095
https://orcid.org/http://orcid.org/0000-0001-8886-0416
https://orcid.org/http://orcid.org/0009-0000-3439-9513
https://orcid.org/http://orcid.org/0000-0002-0363-9702
Due to the poor adaptability of TBM to geological formations, there are problems in actual construction, such as unclear mechanisms and laws of rock-machine interaction, insufficient exploration and perception lag of geological conditions, and strong subjectivity in equipment excavation parameter control. This article is based on the TBM excavation dataset that is publicly available for the Yinsong Project. The main monitoring parameters during the TBM excavation process are divided into three categories: control parameters, state parameters, and performance parameters. The outliers are identified and removed using the Turkey box plot, and then the data is dimensionally reduced using the APCA method. On this basis, a rock machine interaction model (i.e. the relationship between the three types of parameters) for the TBM excavation process was established using the random forest algorithm that integrates Bayesian optimization methods. Finally, with TBM advance speed and specific energy as optimization objectives, the main control parameters of TBM were intelligently optimized using the multi-objective genetic algorithm NSGA II. After optimization, the specific energy was reduced by 5.55%, and the advance rate was increased by 65.79%. The conclusion shows that the above optimization method can achieve efficient and intelligent TBM excavation and has certain engineering application prospects.
Intelligent Decision Method for Main Control Parameters of TBM Based on Machine Learning and Multi-objective Optimization Algorithm
Springer Ser.Geomech.,Geoengineer.
Gutierrez, Marte (Herausgeber:in) / Zheng, Zhaohui (Autor:in) / Guo, Yongfa (Autor:in) / Xu, Pengzu (Autor:in) / Xue, Yadong (Autor:in)
International Conference on Inforatmion Technology in Geo-Engineering ; 2024 ; Golden, CO, USA
03.11.2024
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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