Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Microcracking in Concrete

Microcracking in Concrete

Microcracking in concrete refers to the tiny cracks that can form within the material even before any external load is applied. These microcracks typically occur at the interface between the coarse aggregate and the hydrated cement paste, often as a result of differential volume changes prompted by variations in stress-strain behavior, as well as thermal and moisture movement. Initially, these microcracks remain stable and do not grow substantially until the concrete is stressed to about 30...

You might also read

Related Articles

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

Sort by

Same author

Investigation of the Carbonation Behavior of Cement Mortar Containing Interior Stone Sludge and Recycled Mask Fibers.

Materials (Basel, Switzerland)·2025

Same author

Autonomous Concrete Crack Monitoring Using a Mobile Robot with a 2-DoF Manipulator and Stereo Vision Sensors.

Sensors (Basel, Switzerland)·2025

Same author

Validation and Development of Vocal Fatigue Handicap Questionnaire for Koreans.

Journal of voice : official journal of the Voice Foundation·2025

Same author

Artificial Intelligence-Guided Inverse Design of Deployable Thermo-Metamaterial Implants.

ACS applied materials & interfaces·2025

Same author

Influence of Conductive Filler Types on the Ratio of Reflection and Absorption Properties in Cement-Based EMI Shielding Composites.

Materials (Basel, Switzerland)·2024

Same author

Experimental Investigation into the Mechanical and Piezoresistive Sensing Properties of Recycled Carbon-Fiber-Reinforced Polymer Composites for Self-Sensing Applications.

Polymers·2024

Search research articles

Related Experiment Video

Updated: Jan 11, 2026

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Published on: September 29, 2019

Advanced Signal Analysis Model for Internal Defect Mapping in Bridge Decks Using Impact-Echo Field Testing.

Avishkar Lamsal¹, Biggyan Lamsal¹, Bum-Jun Kim¹

¹Department of Civil Engineering, The University of Texas at Arlington, Nedderman Hall, 416 Yates St, Arlington, TX 76019, USA.

Sensors (Basel, Switzerland)

|November 13, 2025

Summary

This summary is machine-generated.

This study introduces a deep learning model to improve internal defect detection in bridge decks using impact echo testing. The advanced signal analysis significantly enhances accuracy in identifying structural issues.

Keywords:

convolutional neural network deep learning delamination impact echo

More Related Videos

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

Published on: May 28, 2016

Related Experiment Videos

Last Updated: Jan 11, 2026

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Published on: September 29, 2019

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

Published on: May 28, 2016

Area of Science:

Civil Engineering
Structural Health Monitoring
Non-Destructive Testing

Background:

Bridge decks are critical infrastructure requiring regular condition assessment.
Impact echo testing is a common non-destructive method for detecting internal defects.
Signal noise and data variability challenge accurate defect identification in field inspections.

Purpose of the Study:

To develop an advanced signal analysis model for improved internal defect identification in bridge decks.
To mitigate signal noise and variability using deep learning techniques.
To enhance the accuracy of defect detection in impact echo field testing data.

Main Methods:

Field tests were conducted on a concrete bridge deck using an automated inspection system.
A deep learning framework, specifically a convolutional neural network (CNN), was employed for signal analysis.
Signal parameters like duration and zero-crossing starting point were optimized through systematic tuning.

Main Results:

Optimal signal parameters (1 ms duration, 0.1 ms start time) were identified, achieving 88.8% classification accuracy.
CNN-optimized parameters significantly enhanced defect detection accuracy compared to pre-optimization.
Laboratory tests validated the observed signal behavior trends during optimization.

Conclusions:

The integration of advanced signal analysis and deep learning with impact echo testing provides a robust NDT approach.
The developed model effectively refines signal parameters for accurate internal defect identification in bridge decks.
This method offers a promising solution for large-scale infrastructure condition assessment.