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Related Concept Videos

Microcracking in Concrete01:20

Microcracking in Concrete

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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...
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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.
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Creep in Concrete01:22

Creep in Concrete

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

Non-destructive Tests for Concrete Strength

106
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...
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Abrasion Resistance of Concrete01:23

Abrasion Resistance of Concrete

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Abrasion resistance is an essential characteristic of concrete that determines its durability and longevity under various wear conditions. Concrete surfaces are vulnerable to different types of abrasion. For instance, surfaces may wear down due to the constant movement of vehicles or be eroded by solids carried in water, as seen in concrete canal linings. Specific tests are conducted to measure the abrasion resistance of concrete.
One such test is the revolving disc test, where three plates...
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Fatigue Strength of Concrete01:22

Fatigue Strength of Concrete

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Fatigue, in the context of materials science and engineering, refers to the weakening or failure of a material caused by repeatedly applied loads, even if these loads are below the strength limit of the material. Fatigue strength in concrete is a critical property that influences its durability and longevity. Concrete can fail in two ways due to fatigue. Static fatigue or creep rupture occurs under a constant load or one that increases slowly. The other failure mode is due to cyclical or...
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Dataset for developing deep learning models to assess crack width and self-healing progress in concrete.

Jacek Jakubowski1, Kamil Tomczak2

  • 1Department of Civil & Geotechnical Engineering and Geomechanics, AGH University of Krakow, al.Mickiewicza 30, 30-059, Krakow, Poland. jakubjac@agh.edu.pl.

Scientific Data
|January 28, 2025
PubMed
Summary

This study presents a dataset for concrete crack assessment using deep learning. It aids in developing advanced methods for evaluating concrete self-healing and crack widths from image data.

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Area of Science:

  • Civil Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Autogenous self-healing is crucial for concrete durability.
  • Accurate crack width assessment is vital for structural health monitoring.
  • Developing automated methods for crack analysis is an ongoing challenge.

Purpose of the Study:

  • To present a comprehensive dataset for developing deep learning models for crack width assessment.
  • To facilitate the evaluation of autogenous self-healing in concrete.
  • To support the creation of advanced analytic algorithms for concrete crack analysis.

Main Methods:

  • Experimental preparation, maturation, cracking, and self-healing of concrete specimens.
  • High-resolution scanning and scale-invariant image processing of crack surfaces.
  • Extraction of brightness profiles and manual reference measurements for 19,098 records.

Main Results:

  • A dataset containing brightness profiles, manual measurements, and benchmark results from deep learning and analytic models.
  • Source images with marked grid lines are included for further analysis.
  • The dataset is suitable for training image-based deep Convolutional Neural Network (CNN) models.

Conclusions:

  • The developed dataset is a valuable resource for advancing automated crack width assessment in concrete.
  • The findings support the development of deep learning metasensors for self-healing evaluation.
  • Further research can leverage this dataset to improve concrete structural health monitoring techniques.