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

Typical Model Studies01:30

Typical Model Studies

385
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
385
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

84
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
84
Design Example: Maintaining Level of an Embankment01:19

Design Example: Maintaining Level of an Embankment

97
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...
97
Dynamic Modulus of Elasticity of Concrete01:16

Dynamic Modulus of Elasticity of Concrete

427
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...
427

You might also read

Related Articles

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

Sort by
Same author

Poor Knowledge and Suboptimal Practice Regarding Postmenopausal Osteoporosis Prevention Among Chinese Postmenopausal Women: A Structural Equation Model Analysis.

Journal of multidisciplinary healthcare·2026
Same author

Plasma exosome-derived miRNA-887-5p alleviates high glucose- and lipid-induced endothelial cell dysfunction.

Nutrition & diabetes·2026
Same author

Multimodal multitask deep learning for grading management system in non-small cell lung cancer.

Nature communications·2026
Same author

CLINT1 is a subtype-specific biomarker and a downstream effector of p53-R273H in lung adenocarcinoma migration.

Acta biochimica et biophysica Sinica·2026
Same author

Anlotinib for Advanced Peritoneal Follicular Dendritic Cell Sarcoma: A Case Report and Literature Revie.

Current cancer drug targets·2026
Same author

TMEM72 Inhibits the proliferation by promoting cellular senescence through the activation of the P38/MAPK signaling pathway in renal cell carcinoma.

Translational oncology·2026

Related Experiment Video

Updated: Jul 26, 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

A novel combined intelligent algorithm prediction model for the tunnel surface settlement.

You Wang1, Fang Dai2, Ruxue Jia2

  • 1School of Civil Engineering, Central South University, Changsha, 410075, China. ywang1920@csu.edu.cn.

Scientific Reports
|June 17, 2023
PubMed
Summary

This study introduces a novel method for predicting ground settlement during shield tunnel construction. The EMD-CASSA-ELM model enhances prediction accuracy and speed, crucial for construction safety.

More Related Videos

Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images
12:45

Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images

Published on: August 31, 2022

2.9K
Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ
08:59

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Published on: December 16, 2019

8.2K

Related Experiment Videos

Last Updated: Jul 26, 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
Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images
12:45

Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images

Published on: August 31, 2022

2.9K
Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ
08:59

Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ

Published on: December 16, 2019

8.2K

Area of Science:

  • Geotechnical Engineering
  • Civil Engineering
  • Computational Intelligence

Background:

  • Shield tunnel construction poses risks of ground settlement, necessitating accurate prediction for safety.
  • Existing prediction methods may lack accuracy and speed for real-time monitoring.

Purpose of the Study:

  • To develop an advanced prediction model for ground settlement induced by shield construction.
  • To improve the accuracy and efficiency of surface settlement prediction for enhanced safety monitoring.

Main Methods:

  • Empirical Mode Decomposition (EMD) to separate settlement data into trend and fluctuation components.
  • Chaotic Adaptive Sparrow Search Algorithm (CASSA) for optimizing Extreme Learning Machine (ELM) parameters.
  • Integration of EMD, CASSA, and ELM to create a hybrid prediction model (EMD-CASSA-ELM).

Main Results:

  • The CASSA algorithm, enhanced with Cubic chaotic mapping and adaptive factors, optimizes ELM weights and thresholds.
  • The EMD-CASSA-ELM model accurately predicts and reconstructs surface settlement by analyzing decomposed components.
  • Compared to traditional ELM, the meta-heuristic optimized ELM model demonstrated a 10.70% improvement in prediction accuracy.

Conclusions:

  • The EMD-CASSA-ELM model significantly enhances the accuracy and speed of surface settlement prediction.
  • This intelligent prediction method offers a new approach for safety monitoring in shield tunnel construction.
  • Automated and rapid surface subsidence prediction represents a key development trend in the field.