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

Response Surface Methodology01:16

Response Surface Methodology

206
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
206
Vibrating Concrete01:19

Vibrating Concrete

149
Mechanical vibrators are instrumental in compacting newly poured concrete within formwork and around reinforcements. This process is essential to eliminate trapped air pockets and establish a dense concrete mass. One widely used method is vibrating by internal vibrators, often referred to as a poker vibrator or immersion vibrator. It is rapidly inserted through the full depth of the freshly laid concrete and slightly extends into the layer below it (which remains in a plastic state). Consistent...
149
Dynamic Modulus of Elasticity of Concrete01:16

Dynamic Modulus of Elasticity of Concrete

451
The dynamic modulus of elasticity assesses how a concrete structure deforms under impact or dynamic loads. It is typically higher than the static modulus of elasticity, measured under slow, steady loading conditions.
The sonic test is a common method to determine the dynamic modulus. In this test, a concrete beam, sized either 6 x 6 x 30 inches or 4 x 4 x 20 inches, is clamped at its center. Vibrations are initiated at one end of the beam by an electromagnetic exciter unit powered by...
451
Electrostatic Boundary Conditions01:16

Electrostatic Boundary Conditions

544
Consider an external electric field propagating through a homogeneous medium. When the electric field crosses the surface boundary of the medium, it undergoes a discontinuity. The electric field can be resolved into normal and tangential components. The amount by which the field changes at any boundary is given by the difference between the field components above and below the surface boundary.
The surface integral of an electric field is given by Gauss's law in integral form and is related to...
544

You might also read

Related Articles

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

Sort by
Same author

Evaluation of standard, black-box, and bayesian RSM-SVR models in the semi-arid area of south-eastern Iran for predicting soil chemical properties.

Scientific reports·2026
Same author

Comparative reliability assessment of PET and UTCI thermal comfort indices using Monte Carlo simulation in urban microclimates.

Scientific reports·2025
Same author

Deep learning approach to energy consumption modeling in wastewater pumping systems.

Scientific reports·2025
Same author

Cerchar abrasiveness index prediction based on rock properties leveraging hybrid soft computing techniques.

Scientific reports·2025
Same author

An interpretable dynamic ensemble selection multiclass imbalance approach with ensemble imbalance learning for predicting road crash injury severity.

Scientific reports·2025
Same author

Investigation of heavy metals adsorbed on microplastics in drinking water and water resources of Zabol.

Scientific reports·2025

Related Experiment Video

Updated: Aug 9, 2025

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

1.2K

Intelligent ground vibration prediction in surface mines using an efficient soft computing method based on field

Behrooz Keshtegar1, Jamshid Piri2, Rini Asnida Abdullah3

  • 1Department of Civil Engineering, Faculty of Engineering, University of Zabol, Zabol, Iran.

Frontiers in Public Health
|February 23, 2023
PubMed
Summary
This summary is machine-generated.

A new hybrid soft computing (SC) method, RSM-SVR, accurately predicts ground vibration from blasting. This approach improves upon existing models, offering superior accuracy for mining environmental impact assessments.

Keywords:
RSMSVRblastingground vibrationhybrid soft computing method

More Related Videos

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.2K
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

8.9K

Related Experiment Videos

Last Updated: Aug 9, 2025

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

1.2K
Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.2K
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

8.9K

Area of Science:

  • Geotechnical Engineering
  • Environmental Science
  • Computational Intelligence

Background:

  • Ground vibration from blasting is a significant environmental concern in mining.
  • Predicting and minimizing ground vibration is crucial for protecting structures.

Purpose of the Study:

  • To develop and evaluate a hybrid soft computing (SC) method for estimating ground vibration.
  • To enhance the accuracy of ground vibration predictions compared to existing models.

Main Methods:

  • A novel hybrid model, Response Surface Model-Support Vector Regression (RSM-SVR), was developed.
  • The RSM-SVR model integrates Response Surface Model (RSM) and Support Vector Regression (SVR) in a two-stage calibration process.
  • Performance was benchmarked against Particle Swarm Optimization-Support Vector Regression (PSO-SVR), Genetic Algorithm-Support Vector Regression (GA-SVR), Multivariate Linear Regression (MLR), SVR, and RSM.

Main Results:

  • The RSM-SVR model demonstrated superior accuracy in predicting ground vibration.
  • The coefficient of determination (R^2) for RSM-SVR reached 0.896, outperforming other tested models.
  • RSM-SVR significantly improved prediction accuracy compared to standalone RSM, SVR, MLR, PSO-SVR, and GA-SVR.

Conclusions:

  • The hybrid RSM-SVR model offers a highly accurate and effective approach for ground vibration prediction in mining.
  • This method provides a valuable tool for managing environmental impacts associated with blasting operations.
  • The findings highlight the potential of hybrid soft computing techniques in addressing complex geotechnical and environmental challenges.