Prediction of jumbo drill penetration rate in underground mines using various machine learning approaches and traditional models.

Sasan Heydari, Seyed Hadi Hoseinie, Raheb Bagherpour

Open access · goldAbstract read

In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

3.2field-weighted citation impact, top 8% of its field

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 12 citations in OpenAlex.

Improving the accuracy of dynamic inclination measurement by machine learning.Scientific reports · 2024
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Sasan HeydariDepartment of Mining Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.ORCID 0000-0001-9140-3486

Seyed Hadi HoseinieDepartment of Mining Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran. hadi.hoseinie@iut.ac.ir.ORCID 0000-0002-9767-3644

Raheb BagherpourDepartment of Mining Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.ORCID 0000-0002-7235-9966

Isfahan University of Technology · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Estimating penetration rates of Jumbo drills is crucial for optimizing underground mining drilling processes, aiming to reduce costs and time. This study investigates various regression and machine learning methods, including Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Random Forests (RF), to predict the penetration rates (ROP) using multivariate inputs such as operation parameters and rock mass characteristics. The Rock Mass Drillability Index (RDi), incorporating both intact rock properties and structural parameters, was utilized to characterize the rock mass. The dataset was split into 80% for training and 20% for testing. Performance metrics including correlation coefficient (R

Indexed as

Machine LearningSupport Vector MachineAlgorithmsNeural Networks, ComputerMultilayer perceptron neural networks (MLP)Penetration rate predictionRandom Forests (RF)Support Vector Regression (SVR)The Rock Mass Drillability Index (RDi)Traditional models

Identifiers

PMID38637673

DOI10.1038/s41598-024-59753-6

PMCPMC11026510

OpenAlexW4394925942

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.