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Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity01:15

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Deformation occurs in axial and transverse directions when an axial load is applied to a slender bar. This deformation impacts the cubic element within the bar, transforming it into either a rectangular parallelepiped or a rhombus, contingent on its orientation. This transformation process induces shearing strain. Axial loading elicits both shearing and normal strains. Applying an axial load instigates equal normal and shearing stresses on elements oriented at a 45° angle to the load axis.
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Dynamic Modulus of Elasticity of Concrete01:16

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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.
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Hooke's Law01:26

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Hooke's law, a pivotal principle in material science, establishes that the strain a material undergoes is directly proportional to the applied stress, defined by a factor called the modulus of elasticity or Young's modulus.
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Elasticity in Concrete01:20

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Upon subjecting concrete to moderate or high uniaxial compressive or tensile stresses, the strain response is non-linear relative to the stress applied. As the stress is removed, the resulting stress-strain curve deviates from the original path traced during loading, creating a hysteresis loop, indicative of the concrete's non-linear and non-elastic properties. Typically, a material's modulus of elasticity, which is a measure of the material's stiffness, is inferred from the linear...
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In analyzing a structural member composed of two different materials with identical cross-sectional areas, it is crucial to understand how their distinct elastic properties affect the member's response under load. The analysis involves assessing stress and strain distributions using the transformed section concept, which accounts for variations in material properties.
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Experimental and Data Analysis Workflow for Soft Matter Nanoindentation
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Predicting resilient modulus: A data driven approach integrating physical and numerical techniques.

Kashif Riaz1, Naveed Ahmad1

  • 1University of Engineering & Technology, Taxila, Pakistan.

Heliyon
|February 8, 2024
PubMed
Summary
This summary is machine-generated.

This study efficiently predicts pavement sub-grade quality using Artificial Neural Networks (ANN) and Ultrasonic Pulse Velocity (UPV) methods. These techniques offer a practical alternative to traditional, time-consuming resilient modulus (MR) testing.

Keywords:
Artificial neural networkCyclic triaxial compressionResilient modulusUltra-sonic pulse velocity

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Area of Science:

  • Geotechnical Engineering
  • Materials Science

Background:

  • Resilient modulus (MR) is crucial for pavement design, assessing sub-grade material quality.
  • Experimental determination of MR is often impractical due to cost, time, and equipment limitations.

Purpose of the Study:

  • To determine MR values using experimental Ultrasonic Pulse Velocity (UPV) and Cyclic Triaxial tests.
  • To develop and validate an Artificial Neural Network (ANN) model for predicting MR.

Main Methods:

  • Collected 24 soil samples (coarse and fine-grained) for experimental analysis.
  • Utilized Atterberg limits and compaction properties as input variables for ANN modeling.
  • Validated ANN predictions against experimental MR values from UPV and Cyclic Triaxial tests.

Main Results:

  • Cyclic Triaxial tests yielded MR values approximately 5% higher than UPV tests.
  • ANN and UPV methods showed strong agreement with Cyclic Triaxial test results for MR.
  • ANN modeling provides an efficient approach for predicting resilient modulus.

Conclusions:

  • UPV and ANN techniques offer viable, efficient alternatives for MR determination.
  • The developed ANN model demonstrates significant accuracy in predicting resilient modulus.
  • This research advances efficient methods for pavement sub-grade characterization.