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

You might also read

Related Articles

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

Sort by
Same author

Theoretical Prediction and Anisotropic Optoelectronic Properties of the Two-Dimensional Carbon Material Sq-Biphenylene.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

Native Aortic and Mitral Valve Endocarditis Caused by Brucella: A Rare Case Report and Systematic Review.

Cardiology in review·2026
Same author

Space-Dependent Oviposition Preference in Drosophila.

Neuroscience bulletin·2026
Same author

Shenling Baizhu San Ameliorates MASH and Associated Depression-like Behavior in Mice by Impacting Gut Microbiota and Carbohydrate Enzymes.

Combinatorial chemistry & high throughput screening·2026
Same author

Dual-Similarity Driven Just-in-Time Modeling Enables Precise Real-Time Monitoring of Cell Cultures via Dielectric Spectroscopy.

Biotechnology journal·2026
Same author

Haplotype-resolved genome assembly provides insights into the unique floral scent of Rosa rugosa originated in China.

Molecular horticulture·2026

Related Experiment Video

Updated: Mar 17, 2026

Soil Lysimeter Excavation for Coupled Hydrological, Geochemical, and Microbiological Investigations
10:30

Soil Lysimeter Excavation for Coupled Hydrological, Geochemical, and Microbiological Investigations

Published on: September 11, 2016

11.4K

Elastic-plastic model identification for rock surrounding an underground excavation based on immunized genetic

Wei Gao1, Dongliang Chen1, Xu Wang1

  • 1Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, College of Civil and Transportation Engineering, Hohai University, 1 Xikang Road, Nanjing, 210098 China.

Springerplus
|July 28, 2016
PubMed
Summary

This study introduces an immunized genetic algorithm to efficiently identify constitutive models for underground engineering. The novel approach combines artificial immune and genetic algorithms, improving computation efficiency and effect for rock mass stability analysis.

Keywords:
Elastic–plastic constitutive modelIdentificationImmunized genetic algorithmSurrounding rockUnderground engineering

More Related Videos

Pattern-based Search of Epigenomic Data Using GeNemo
06:38

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

5.4K
Experimental and Data Analysis Workflow for Soft Matter Nanoindentation
13:04

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation

Published on: January 18, 2022

5.0K

Related Experiment Videos

Last Updated: Mar 17, 2026

Soil Lysimeter Excavation for Coupled Hydrological, Geochemical, and Microbiological Investigations
10:30

Soil Lysimeter Excavation for Coupled Hydrological, Geochemical, and Microbiological Investigations

Published on: September 11, 2016

11.4K
Pattern-based Search of Epigenomic Data Using GeNemo
06:38

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

5.4K
Experimental and Data Analysis Workflow for Soft Matter Nanoindentation
13:04

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation

Published on: January 18, 2022

5.0K

Area of Science:

  • Geotechnical Engineering
  • Computational Mechanics
  • Artificial Intelligence

Background:

  • Accurate constitutive models are crucial for assessing underground engineering stability.
  • Traditional model identification involves complex parameter optimization.
  • Existing methods can be computationally intensive and inefficient.

Purpose of the Study:

  • To develop a more efficient method for identifying constitutive models of surrounding rock.
  • To enhance the computational efficiency and effectiveness of model identification for underground engineering.

Main Methods:

  • Application of a generalized constitutive law for an elastic-plastic model.
  • Transformation of model identification into a parameter identification optimization problem.
  • Integration of artificial immune principles with genetic algorithms to create an immunized genetic algorithm.

Main Results:

  • The immunized genetic algorithm significantly improves computation efficiency.
  • Enhanced computational effectiveness is demonstrated through numerical and engineering examples.
  • The proposed method addresses the complexities of parameter identification in constitutive modeling.

Conclusions:

  • The immunized genetic algorithm offers a superior approach to constitutive model identification in underground engineering.
  • This method provides a robust and efficient solution for rock mass stability analysis.
  • The study validates the algorithm's capability in practical engineering applications.