ArticleScientific reports2025
An intelligent approach to predict the drilling penetration rate using acoustic emission technique (AET).
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Intelligent identification method for drilling conditions based on artificial neural networks.Scientific reports · 2026Article
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4 authors.
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Abstract
Optimization has become a central concern in geotechnical engineering with increasing constraints on energy resources and the rising demand for cost-effective operations. Drilling, as a critical and energy-intensive component of mining and tunneling (particularly in transportation infrastructure), requires efficient and intelligent performance strategies. Monitoring While Drilling (MWD) provides a promising approach for real-time acquisition of drilling conditions. Recent advancements, including the integration of Acoustic Emission Technique (AET) with artificial intelligence (AI), enhance data-driven modeling and predictive analysis of drilling performance. In this study, vibroacoustic signals and drilling parameters were analyzed to predict penetration rate (PR) using three machine learning models: Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Regression (SVR). Comparative evaluation showed that all three models achieved reliable predictive accuracy, with ANN reaching R
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