Multi-objective optimization control for shield cutter wear and cutting performance using LightGBM and enhanced NSGA-II

被引：1
作者：
Yin, Ziwei [1 ,2 ,3 ]
Jiao, Jianwei [1 ,3 ]
Xie, Ping [1 ,3 ]
Luo, Hanbin [1 ,3 ]
Wei, Linchun [1 ,3 ,4 ]
机构：
[1] Huazhong Univ Sci & Technol, Sch Civil & Hydraul Engn, Wuhan, Peoples R China
[2] Huazhong Univ Sci & Technol, Inst Artificial Intelligence, Wuhan, Hubei, Peoples R China
[3] Huazhong Univ Sci & Technol, Natl Ctr Technol Innovat Digital Construct, Wuhan, Peoples R China
[4] Shanghai Tunnel Engn Co Ltd, Shanghai, Peoples R China
来源：
AUTOMATION IN CONSTRUCTION | 2025年 / 171卷
基金：
中国国家自然科学基金;
关键词：
Cutter wear; Cutting performance; Multi-objective optimization; The light gradient boosting machine; (LightGBM); Enhanced non-dominated sorting genetic-II; (NSGA-II) algorithm; PREDICTION MODEL; TBM; TUNNEL; TOOL; ALGORITHM; LIFE;
D O I：
10.1016/j.autcon.2024.105957
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
Varying results in cutter wear and cutting performance can be observed based on different selections of shield operational parameters, particularly in hard rock or soil with a high quartz content. Improperly selecting operational parameters may result in excessive wear and reduced cutting performance, leading to longer project duration and increased costs. Furthermore, it is still challenging to balance cutter wear and cutting performance. To address these issues, a multi-objective optimization (MOO) framework based on the Light Gradient Boosting Machine (LightGBM) algorithm and the enhanced non-dominated sorting genetic-II (NSGA-II) algorithm is proposed to predict and optimize the cutter wear and cutting performance. To validate this framework, a shield tunneling project in China is presented. The results show that the efficiency and accuracy of predicting and optimizing the two objectives have been improved compared with other common methods. This MOO framework is valuable for operators to formulate rational operational control strategies.
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页数：19
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