Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Survival Tree01:19

Survival Tree

374
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
374
Design Example: Maintaining Level of an Embankment01:19

Design Example: Maintaining Level of an Embankment

388
Constructing a roadway embankment over uneven terrain requires precise leveling to ensure stability and proper drainage. Surveyors use a leveling instrument and staff to calculate ground elevations and determine the required fill material at each point along the embankment alignment.The process begins by positioning a leveling instrument near a benchmark with a known elevation. A backsight reading establishes the instrument height, which serves as a reference for subsequent measurements. A...
388
Slump Test01:20

Slump Test

790
The slump test is a widely used method to measure the workability of concrete. It employs a 12-inch high truncated cone mold that tapers from eight inches at the base to four inches at the top. Before testing, the mold is securely attached to a flat base and dampened.
Concrete is poured into the mold in three layers to conduct the test. Each layer is compacted 25 times with a steel tamping rod, which has a five-eighths-inch diameter and a rounded end, to ensure even distribution and eliminate...
790

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Application of modified multi-verse optimization for temperature control in thermal power plant condensers.

Scientific reports·2026
Same author

Enhanced slope stability prediction using ensemble machine learning techniques.

Scientific reports·2025
Same author

Global and regional long-term survival following resection for HCC in the recent decade: A meta-analysis of 110 studies.

Hepatology communications·2022
See all related articles

Related Experiment Video

Updated: Jan 10, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K

Advanced machine learning techniques for predicting dump slope stability in Indian opencast coal mines.

Arun Kumar Sahoo1,2, Debi Prasad Tripathy3, Singam Jayanthu3

  • 1Department of Mining Engineering, National Institute of Technology, Rourkela, Odisha, 769008, India. sahooarunkumar31@gmail.com.

Scientific Reports
|November 20, 2025
PubMed
Summary

Opencast coal mining in India generates large waste dumps prone to instability. This study integrates machine learning with geotechnical data to accurately predict dump slope stability, improving safety in mining operations.

Keywords:
Dump slope stability predictionH2O AutoMLLazy predictsMachine learning modelsStacking ensemble model

Related Experiment Videos

Last Updated: Jan 10, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K

Area of Science:

  • Geotechnical Engineering
  • Data Science
  • Artificial Intelligence

Background:

  • Opencast coal mining in India generates substantial overburden waste dumps, posing significant stability challenges.
  • Traditional methods for dump slope stability analysis are complex, time-consuming, and require advanced computational approaches.
  • Ensuring the stability of these dumps is critical for operational safety and environmental protection in the mining industry.

Purpose of the Study:

  • To enhance the accuracy and reliability of dump slope stability predictions in Indian opencast coal mines.
  • To integrate advanced machine learning techniques with traditional geotechnical engineering methods.
  • To identify the most influential parameters affecting dump slope stability through statistical evaluation.

Main Methods:

  • A comprehensive dataset of 2250 entries was created, incorporating six key parameters: cohesion (c), angle of internal friction (ϕ), unit weight (γ), overall bench height (H), natural moisture content (m), and overall slope angle (β).
  • Various machine learning models, including Gradient Boosting (GBM), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGB), Histogram Gradient Boosting (HGB), Nu-Support Vector Regressor (NuSVR), Extra Tree Regressor (ETR), Stacking Ensemble, and H2OAutoML, were employed.
  • Model performance was rigorously evaluated using metrics such as R-squared, MSE, MAPE, RMSE, and MAE. The Shapley additive explanations (SHAP) technique was utilized to determine input feature importance.

Main Results:

  • The H2OAutoML model demonstrated superior performance compared to other ensemble models in predicting the factor of safety (FOS) for dump slopes.
  • The study successfully identified the key input parameters influencing dump slope stability through SHAP analysis.
  • The integration of machine learning significantly improved the accuracy and applicability of dump slope stability predictions.

Conclusions:

  • Sophisticated machine learning approaches offer a powerful and efficient method for analyzing dump slope stability in opencast coal mines.
  • The developed models provide a reliable tool for geotechnical engineers to assess and mitigate risks associated with waste dump instability.
  • This research highlights the potential of AI-driven solutions to enhance safety and operational efficiency in the mining sector.