Ditapis dengan
Optimized Random Forest Models for Rock Mass Classification in Tunnel Constru…
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) algorithms to classify the surrounding rock in real …
- Edisi
- Vol.15, Issue 2, February 2025
- ISBN/ISSN
- 2076-3263
- Deskripsi Fisik
- 26 hlm PDF, 3.524 KB
- Judul Seri
- Geosciences
- No. Panggil
- 550
A comparative study on machine learning approaches for rock mass classificati…
Current rock engineering design in drill and blast tunnelling primarily relies on engineers' observational assessments. Measure While Drilling (MWD) data, a high-resolution sensor dataset collected during tunnel excavation, is underutilised, mainly serving for geological visualisation. This study aims to automate the translation of MWD data into actionable metrics for rock engineering. It seeks…
- Edisi
- Vol.24, December 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 24 hlm PDF, 15.152 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Batas Wilayah 
Ekologi 
Fotogrametri 
Geografi 
Geologi 
GIS 
Ilmu Tanah 
Kartografi 
Manajemen Bencana 
Oceanografi 
Penginderaan Jauh 
Peta