LightGBM integration with modified data balancing and whale optimization algorithm for rock mass classification

The accurate prediction of uneven rock mass classes is crucial for intelligent operation in tunnel-boring machine (TBM) tunneling. However, the classification of rock masses presents significant challenges due to the variability and complexity of geological conditions. To address these challenges, t...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Scientific reports Ročník 14; číslo 1; s. 23028 - 25
Hlavní autor: Li, Long
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 03.10.2024
Nature Publishing Group
Nature Portfolio
Témata:
639/166/986
639/705/117
Accuracy
Algorithms
Classification
Data balancing algorithm
Decision making
Engineering
Geology
Humanities and Social Sciences
Improved whale optimization algorithm
Light gradient boosting machine
Machine learning
multidisciplinary
Optimization algorithms
Outdoor air quality
Prediction models
Rock mass classification
Rocks
Science
Science (multidisciplinary)
Skewness
Improved whale optimization algorithm
Data balancing algorithm
Light gradient boosting machine
Rock mass classification
ISSN:2045-2322, 2045-2322
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The accurate prediction of uneven rock mass classes is crucial for intelligent operation in tunnel-boring machine (TBM) tunneling. However, the classification of rock masses presents significant challenges due to the variability and complexity of geological conditions. To address these challenges, this study introduces an innovative predictive model combining the improved EWOA (IEWOA) and the light gradient boosting machine (LightGBM). The proposed IEWOA algorithm incorporates a novel parameter l for more effective position updates during the exploration stage and utilizes sine functions during the exploitation stage to optimize the search process. Additionally, the model integrates a minority class technique enhanced with a random walk strategy (MCT-RW) to extend the boundaries of minority classes, such as Classes II, IV, and V. This approach significantly improves the recall and F 1 -score for these rock mass classes. The proposed methodology was rigorously evaluated against other predictive algorithms, demonstrating superior performance with an accuracy of 94.74%. This innovative model not only enhances the accuracy of rock mass classification but also contributes significantly to the intelligent and efficient construction of TBM tunnels, providing a robust solution to one of the key challenges in underground engineering.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-73742-9