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Types of Non-structural Cracks in Concrete

Types of Non-structural Cracks in Concrete

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.

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...

Non-destructive Tests for Concrete Strength

Non-destructive Tests for Concrete Strength

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...

Abrasion Resistance of Concrete

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Design Example: Joints in Concrete Pavements

Design Example: Joints in Concrete Pavements

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Related Experiment Video

Updated: Jan 4, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Learning to Detect Cracks on Damaged Concrete Surfaces Using Two-Branched Convolutional Neural Network.

Jieun Lee¹, Hee-Sun Kim², Nayoung Kim³

¹Department of Electrical and Electronic Engineering, Ewha Womans University, Seoul 03760, Korea. leeje2993@gmail.com.

Sensors (Basel, Switzerland)

|November 7, 2019

Summary

This study introduces an autonomous crack detection algorithm using a novel two-branched convolutional neural network (CNN) for concrete structures. The method reliably identifies cracks, outperforming traditional algorithms in real-world conditions.

Keywords:

convolutional neural network crack detection deep learning edge detection fire-damaged concrete image processing

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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

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Published on: December 15, 2023

Area of Science:

Civil Engineering
Computer Vision
Artificial Intelligence

Background:

Image sensors are crucial for detecting concrete cracks, aiding structural management.
Real-world crack detection is challenging due to noise and artifacts on damaged surfaces.

Purpose of the Study:

To develop an autonomous crack detection algorithm using a convolutional neural network (CNN).
To improve the reliability of crack detection on damaged concrete surfaces.

Main Methods:

A two-branched CNN architecture was proposed, comprising a crack-component-aware (CCA) network and a crack-region-aware (CRA) network.
The CCA network learns crack gradient components, while the CRA network distinguishes critical cracks from noise.
Both sub-networks utilize convolution-deconvolution architectures and are trained end-to-end.

Main Results:

The proposed algorithm demonstrated superior performance in crack detection compared to conventional methods.
The two-branched CNN effectively localized important cracks while filtering out noise like scratches.

Conclusions:

The developed autonomous crack detection algorithm offers enhanced accuracy and reliability.
This CNN-based approach provides a robust solution for proactive concrete structure management.