Evidence map›Paper›PMID 39893193›Full record

ArticleScientific reports2025

Enhancing safety in surface mine blasting operations with IoT based ground vibration monitoring and prediction system integrated with machine learning.

Mangalpady Aruna, Harsha Vardhan, Abhishek Kumar Tripathi, Satyajeet Parida, N V Raja Sekhar Reddy, Krishna Moorthy Sivalingam, Li Yingqiu, P V Elumalai

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In one paragraph

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

8 authors.

Mangalpady ArunaDepartment of Mining Engineering, National Institute of Technology Karnataka, Surathkal, 575025, India. arunamangalpady@gmail.com.
Harsha VardhanDepartment of Mining Engineering, National Institute of Technology Karnataka, Surathkal, 575025, India.
Abhishek Kumar TripathiDepartment of Mining Engineering, Aditya University, Surampalem, 53347, Andhra Pradesh, India.
Satyajeet ParidaDepartment of Mining Engineering, Aditya University, Surampalem, 53347, Andhra Pradesh, India.
N V Raja Sekhar ReddyDepartment of Information Technology, MLR Institute of Technology, Hyderabad, Telangana, India.
Krishna Moorthy SivalingamDepartment of Biology, College of Natural and Computational Sciences, Wolaita Sodo University, Post Box No.:138, Wolaita Sodo, Ethiopia. drkrishna.micro@wsu.edu.et.
Li YingqiuFaculty of Education, Shinawatra University, Pathum Thani, Thailand.
P V ElumalaiDepartment of Mechanical Engineering, Aditya University, Surampalem, 53347, Andhra Pradesh, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Monitoring and predicting ground vibration levels during blasting operations is essential to safeguard mining sites and surrounding communities. This study introduces an IoT-based ground vibration monitoring device specifically designed for limestone mining operations, combined with machine learning algorithms to predict ground vibration intensity. The primary aim is to provide an efficient predictive tool for anticipating hazardous vibration levels, enabling proactive safety measures. A comparative analysis with the industry-standard Minimate Blaster indicates high accuracy of the IoT device, with percentage errors as low as 0.803% across multiple blasts. The study also employed Support Vector Regression (SVR), Gradient Boosting Regression (GBR), and Random Forest (RF) algorithms to predict Peak Particle Velocity (PPV) values. Among these, the Random Forest model outperformed the others, achieving an R

Indexed as

Ground vibrationIoT-based monitoringMachine learningSafety enhancementSafety netSurface mines

Identifiers

PMID39893193
PMCPMC11787382

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.