A LightGBM-based strategy to predict tunnel rockmass class from TBM construction data for building control

Li, Long; Liu, Zaobao; Shen, Ji-Mei; Wang, Fei; Qi, Wenbiao; Jeon, Seokwon

doi:10.1016/j.aei.2023.102130

Cited by 36 publications

(1 citation statement)

References 45 publications

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“…LGBM [19] is an iterative tree lifting system provided by Microsoft, which has the advantages of fast efficiency, low memory consumption, and high precision. At present, an LGBM algorithm has been applied in financial risk prediction [20], haze risk assessment [21], tunnel surrounding rock grade prediction [22], tunnel surrounding rock mechanical parameter inversion [23], and other fields.…”

Section: Overview Of Lgbm Modelmentioning

confidence: 99%

Research on Prediction of EPB Shield Tunneling Parameters Based on LGBM

Wang,

Feng,

et al. 2024

Buildings

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At present, the determination of tunnel parameters mainly rely on engineering experience and human judgment, which leads to the subjective decision of parameters and an increased construction risk. Machine learning algorithms could provide an objective theoretical basis for tunnel parameter decision making. However, due to the limitations of a machine learning model’s performance and parameter selection methods, the prediction model had poor prediction results and low reliability for parameter research. To solve the above problems, based on a large number of construction parameters of a composite section subway in Shenzhen, this paper combined dimensionality reduction data with service analysis to optimize the selection process of shield tunneling parameters, and determined the total propulsion force, cutter head torque, cutter head speed, and advance rate as key tunneling parameters. Based on an LGBM algorithm and Bayesian optimization, the prediction model of key tunneling parameters of an earth pressure balance shield was established. The results showed that the average error of the LGBM model on the test set was 8.18%, the average error of the cutter head torque was 13.93%, the average error of the cutter head speed was 3.16%, and the average error of advance rate was 13.35%. Compared with the RF model, the prediction effect and the generalization on the test set were better. Therefore, an LGBM algorithm could be used as an effective prediction method for tunneling parameters in tunnel construction and provide guidance for the setting of tunneling parameters.

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Section: Overview Of Lgbm Modelmentioning

confidence: 99%

Research on Prediction of EPB Shield Tunneling Parameters Based on LGBM

Wang,

Feng,

et al. 2024

Buildings

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Prediction of non-stationary daily streamflow series based on ensemble learning: a case study of the Wei River Basin, China

Ma,

Zhang,

Xie

et al. 2024

Stoch Environ Res Risk Assess

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AI-Powered Geotechnics: Enhancing Rock Mass Classification for Safer Engineering Practices

Saadati,

Javankhoshdel,

Mohebbi Najm Abad

et al. 2024

Rock Mech Rock Eng

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Rock mass classification is fundamental for evaluating rock mass quality, essential for stability analysis and geotechnical design. Traditional classification methods are limited by joint observation technology, which typically gathers joint information from one-dimensional or two-dimensional perspectives, failing to comprehensively capture three-dimensional joint occurrences. This often necessitates empirical formulas for joint distribution, resulting in less precise joint parameter calculations. This paper reviews 44 seminal articles on rock engineering classification in construction and subterranean projects, tracing the evolution from foundational methods like Rock Quality Designation, Rock Mass Rating, Q-system, Basic Quality, and Hydropower Classification to contemporary techniques. It highlights the transformative impact of data science, particularly artificial intelligence, on rock engineering. The analysis reveals 73 distinct algorithms used 162 times in literature, with Support Vector Machines Support, Vector Regression, K-means clustering, K-Nearest Neighbors, Artificial Neural Networks and Random Forest being the most successful. This paper examines each method's advantage and limitations, discussing the challenges of algorithm deployment in the scientific community. The findings underscore the integration of machine learning and meta-heuristic optimization methods in rock engineering classification, offering valuable insights for future research and applications.

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A LightGBM-based strategy to predict tunnel rockmass class from TBM construction data for building control

Cited by 36 publications

References 45 publications

Research on Prediction of EPB Shield Tunneling Parameters Based on LGBM

Research on Prediction of EPB Shield Tunneling Parameters Based on LGBM

Prediction of non-stationary daily streamflow series based on ensemble learning: a case study of the Wei River Basin, China

AI-Powered Geotechnics: Enhancing Rock Mass Classification for Safer Engineering Practices

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