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Related Concept Videos

Flexural Stress01:16

Flexural Stress

When analyzing bending in symmetric members, it's crucial to understand how stresses distribute when subjected to bending moments. This stress distribution is effectively described by applying fundamental mechanics and material science principles, particularly Hooke's Law for elastic materials.
Hooke's Law states that within the material's elastic limits, stress is directly proportional to strain. In a member experiencing a bending moment, the strain at any point is relative to its distance...
Deformation of Member under Multiple Loadings01:11

Deformation of Member under Multiple Loadings

When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...
Deformation of a Beam under Transverse Loading01:15

Deformation of a Beam under Transverse Loading

Understanding beam deflection, particularly for indeterminate beams with overhanging segments and multiple concentrated loads, is crucial for ensuring structural integrity and functionality. The process begins with constructing an accurate free-body diagram, which helps identify the forces and moments acting on the beam. This diagram is vital for visualizing how bending moments vary along the beam's length, influencing its curvature.
The insights from the bending moment diagram extend to...
Normal Strain under Axial Loading01:20

Normal Strain under Axial Loading

Normal strain under axial loading is an important concept in the field of mechanics of materials. Axial loading implies the application of a force along the axis of a material, like a column or bar. This force can either compress or stretch the material. In the context of axial loading, normal strain is the deformation experienced by the material in the direction of the loading force. It's calculated as the change in length divided by the original length of the material. This unitless ratio...
Internal Loadings in Structural Members: Problem Solving01:28

Internal Loadings in Structural Members: Problem Solving

When designing or analyzing a structural member, it is important to consider the internal loadings developed within the member. These internal loadings include normal force, shear force, and bending moment. Engineers can ensure that the structural member can support the applied external forces by calculating these internal loadings.
To illustrate this, let's consider a beam OC of 5 kN, inclined at an angle of 53.13° with the horizontal and supported at both ends. Determine the internal loadings...
Stresses under Combined Loadings01:23

Stresses under Combined Loadings

When analyzing a bent tube with a circular cross-section subjected to multiple forces, it is crucial to determine the stress distribution in order to maintain structural integrity under varied load conditions.
The process begins by slicing the tube at critical points and analyzing the internal forces and stress components at these sections, focusing on the centroid. Normal stresses, generated by axial forces and bending moments, are either compressive or tensile and vary across the section from...

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Related Experiment Video

Updated: May 14, 2026

A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation
09:34

A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation

Published on: September 14, 2017

Convolutional Neural Network for Specimen-Invariant Structural Health Monitoring of FRC Under Flexural Loading.

George M Sapidis1, Ioannis Kansizoglou2, Maria C Naoum1

  • 1Laboratory of Reinforced Concrete and Seismic Design of Structures, Civil Engineering Department, School of Engineering, Democritus University of Thrace, 67100 Xanthi, Greece.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning framework using Electromechanical Impedance (EMI) for structural health monitoring of fiber-reinforced concrete (FRC) beams. The method accurately detects flexural damage progression, enhancing infrastructure safety.

Keywords:
convolutional neural network (CNN)deep learningelectromechanical impedance (EMI)fiber-reinforced concrete (FRC)flexural damage identificationpiezoelectric lead zirconate titanate (PZT)repeated loadingstructural health monitoring (SHM)

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Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

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Last Updated: May 14, 2026

A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation
09:34

A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation

Published on: September 14, 2017

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

Area of Science:

  • Civil Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Reinforced concrete (RC) structures degrade over time, compromising safety.
  • Fiber-reinforced concrete (FRC) offers enhanced durability and ductility.
  • Structural Health Monitoring (SHM) for FRC is underdeveloped, necessitating advanced techniques.

Purpose of the Study:

  • To evaluate a deep learning-enabled Electromechanical Impedance (EMI) framework for assessing the structural condition of FRC beams.
  • To develop and validate a 1D-CNN model for automated damage detection in FRC.
  • To compare specimen-invariant validation with traditional methods for SHM model generalization.

Main Methods:

  • Utilized a deep learning approach with a 1D-CNN to analyze high-frequency EMI signatures from FRC beams.
  • Employed piezoelectric transducers (PZT) for EMI data acquisition.
  • Implemented specimen-invariant validation for robust model evaluation on FRC specimens.

Main Results:

  • The proposed 1D-CNN accurately classified structural health states and identified flexural damage progression in FRC beams.
  • Specimen-invariant validation demonstrated superior robustness compared to conventional methods.
  • An ablation study confirmed the effectiveness of individual 1D-CNN architectural components.

Conclusions:

  • The deep learning-enabled EMI framework shows significant potential for reliable and automated SHM of FRC structures.
  • This approach contributes to the development of resilient concrete infrastructures.
  • Integrating EMI sensing with deep learning offers scalable solutions for monitoring next-generation concrete.