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Segregation in Fresh Concrete01:16

Segregation in Fresh Concrete

140
Segregation in fresh concrete is a phenomenon where the components of the concrete mix separate, leading to uneven distribution and compromised structural integrity. This separation typically occurs when concrete is subjected to excessive horizontal movement within forms, or when it is dropped from considerable heights or forced through narrow, winding paths. As a result, heavier coarse aggregate particles settle at the bottom, while lighter, finer materials such as cement and water rise to the...
140
Placing Concrete01:17

Placing Concrete

106
The concrete is placed as close as possible to its final position to avoid segregation. The placed concrete is then fully compacted to expel the entrapped air, and the next layer of concrete is laid while the underlying layer is still in the plastic state. The rate at which concrete is placed and compacted is kept equal.
While placing concrete, care is taken to ensure that the concrete is laid in uniform layers, and hand shoveling and moving concrete using poker vibrators is avoided. Also,...
106
Bleeding in Fresh Concrete01:22

Bleeding in Fresh Concrete

127
Bleeding in fresh concrete occurs when water from the mix rises to the surface. This happens because the mix's solid components fail to retain all the water as they settle, leading to separation where water collects at the top. The severity of bleeding can be measured by assessing the total settlement or by noting the decrease in height per unit height of concrete.
Bleeding can cause several issues in the concrete structure. Sometimes, the rising water gets trapped beneath large aggregate...
127
Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

121
The rebound hammer test, also known as the Schmidt hammer test, is a non-destructive technique for evaluating the hardness of concrete and, indirectly, the strength of concrete. It operates on the principle that the rebound of a spring-driven mass from a concrete surface correlates to the surface's hardness. The device comprises a mass within a tubular housing, a spring mechanism, and a plunger that strikes the concrete. Upon release, the energy imparted to the mass by the spring causes it...
121
Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

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Non-structural cracks are primarily of three types: plastic, early-age thermal, and drying shrinkage cracks. Plastic cracks are further classified into plastic shrinkage cracks and plastic settlement cracks.
Plastic shrinkage cracks typically form within hours after the concrete is poured. The concrete's surface dries faster than the bottom, creating tensile stress that the still-plastic concrete cannot withstand, leading to diagonal or randomly patterned cracks on the concrete surface.
161

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Related Experiment Video

Updated: Jul 8, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Deep learning-based concrete defects classification and detection using semantic segmentation.

Palisa Arafin1, Ahm Muntasir Billah2, Anas Issa3

  • 1Department of Civil Engineering, Lakehead University, Thunder Bay, ON, Canada.

Structural Health Monitoring
|December 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new dataset and deep learning models for concrete crack and spalling detection in structural health monitoring. EfficientNetB3-based U-Net achieved 95.66% F1-score for crack segmentation.

Keywords:
Concrete defectsconvolutional neural networkencoder-decoder modelsemantic segmentationstructural health monitoring

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Area of Science:

  • Civil Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Deep learning (DL) offers potential for accurate, objective infrastructure damage detection in structural health monitoring (SHM).
  • Key challenges include limited defect image datasets and selecting appropriate DL network architectures for real-time applications.

Purpose of the Study:

  • To develop and evaluate DL models for concrete crack and spalling detection using a novel dataset.
  • To address limitations in existing defect image databases and DL network depth selection for SHM.

Main Methods:

  • A diverse dataset of 4087 concrete crack and 1100 spalling images was created.
  • Convolutional Neural Network (CNN) classifiers (VGG19, ResNet50, InceptionV3) were used for defect identification.
  • Encoder-decoder models (U-Net, PSPNet) with various backbones (VGG19, ResNet50, InceptionV3, EfficientNetB3) were developed for semantic segmentation.

Main Results:

  • InceptionV3 achieved 91.98% accuracy for defect classification with the RMSprop optimizer.
  • EfficientNetB3-based U-Net yielded the best crack segmentation (95.66% F1-score).
  • InceptionV3-based U-Net excelled in spalling segmentation (89.43% F1-score).

Conclusions:

  • The developed DL models and dataset significantly advance automated visual damage detection in SHM.
  • Specific CNN architectures demonstrate high efficacy for both classification and segmentation of concrete defects.
  • This research provides a foundation for more accurate and accessible real-time structural health monitoring systems.