Optimized Random Forest Models for Rock Mass Classification in Tunnel Construction
Optimized Random Forest Models for Rock Mass Classification in Tunnel Construction
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Publisher
Basel: MDPI AG
Journal title
Language
English
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Publisher
Basel: MDPI AG
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Scope and Contents
Contents
The accurate prediction of rock mass quality ahead of the tunnel face is crucial for optimizing tunnel construction strategies, enhancing safety, and reducing geological risks. This study developed three hybrid models using random forest (RF) optimized by moth-flame optimization (MFO), gray wolf optimizer (GWO), and Bayesian optimization (BO) algor...
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Full title
Optimized Random Forest Models for Rock Mass Classification in Tunnel Construction
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TN_cdi_doaj_primary_oai_doaj_org_article_937a12ab4ba94f90989f602aac2bf4d6
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_937a12ab4ba94f90989f602aac2bf4d6
Other Identifiers
ISSN
2076-3263
E-ISSN
2076-3263
DOI
10.3390/geosciences15020047
