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Microcracking in Concrete01:20

Microcracking in Concrete

128
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...
128
Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

168
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.
168
Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

124
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...
124
Reinforcements in Concrete01:25

Reinforcements in Concrete

94
Reinforced concrete is a composite material used extensively in construction, combining the compressive strength of concrete with the tensile strength of steel. This synergy is essential as concrete, while excellent at resisting compression, is weak under tension. Steel bars, or rebars, are embedded in the concrete to handle these tensile forces. The choice of steel is strategic; it shares a similar coefficient of thermal expansion with concrete, which ensures uniformity in response to...
94
Tensile Strength Considerations of Concrete01:16

Tensile Strength Considerations of Concrete

134
Considering the tensile strength of concrete involves recognizing that the theoretical strength of cement paste can be up to a thousand times higher than what is observed in practical applications. This significant discrepancy is largely attributed to the presence of microscopic cracks within the concrete. These cracks tend to amplify stress at their tips when a load is applied, a phenomenon explained by Griffith's theory of brittle fracture.
The dimensions and shape of a concrete specimen...
134
Creep in Concrete01:22

Creep in Concrete

274
Creep refers to the time-dependent increase in strain under a sustained load, excluding other time-dependent deformations associated with shrinkage, swelling, and thermal expansion in concrete. The primary mechanism behind creep involves the loss of physically adsorbed water from the calcium silicate hydrate within the hydrated cement paste. This process is further exacerbated by concrete's non-linear stress-strain relationship, microcrack development in the interfacial transition zone, and...
274

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Related Experiment Video

Updated: Jul 12, 2025

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
00:05

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

Published on: September 29, 2019

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Threshold-Based BRISQUE-Assisted Deep Learning for Enhancing Crack Detection in Concrete Structures.

Sanjeetha Pennada1, Marcus Perry1, Jack McAlorum1

  • 1Department of Civil and Environmental Engineering, University of Strathclyde, 75 Montrose St., Glasgow G1 1XJ, UK.

Journal of Imaging
|October 27, 2023
PubMed
Summary
This summary is machine-generated.

Image quality significantly impacts crack detection using deep learning. Lower BRISQUE scores indicate better image quality, leading to higher accuracy in identifying concrete cracks and reducing computational costs.

Keywords:
BRISQUEVGG16binary classificationconcrete crack detectiondata cleaningdeep learningimage processingimage quality assessmentneural networksstructural health monitoring

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Area of Science:

  • Civil Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Automated visual inspection systems for concrete structures rely on accurate crack detection.
  • Convolutional Neural Networks (CNNs) performance is hindered by low-quality images.
  • Evaluating image dataset suitability is crucial for reliable deep learning models.

Purpose of the Study:

  • To assess the impact of image degradations (Gaussian noise, blur) on crack detection using VGG16.
  • To explore the correlation between image quality metrics (BRISQUE) and CNN performance.
  • To propose a method for optimizing training and testing for reduced computational costs.

Main Methods:

  • Utilized the BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator) method to quantify image degradations.
  • Trained and evaluated the VGG16 model on datasets with varying levels of Gaussian noise and blur.
  • Analyzed the relationship between BRISQUE scores and crack classification metrics (accuracy, F1 score, MCC).

Main Results:

  • A strong correlation was found between lower BRISQUE scores and improved crack classification performance.
  • Higher accuracy, F1 score, and Matthew's Correlation Coefficient (MCC) were achieved with less degraded images.
  • Implementation of a BRISQUE score threshold can optimize model training and testing.

Conclusions:

  • Image quality is a critical factor for the success of CNN-based crack detection.
  • BRISQUE is a sensitive metric for evaluating image quality in the context of crack detection.
  • Utilizing BRISQUE thresholds can lead to more efficient and cost-effective automated visual inspection systems for structural health monitoring.