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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...

Segregation in Fresh Concrete

Segregation in Fresh Concrete

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...

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.

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...

Structural Classification of Joints

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...

Design Example: Joints in Concrete Pavements

Design Example: Joints in Concrete Pavements

Concrete pavement joints are essential for maintaining the structural integrity and longevity of pavement by controlling where and how the pavement cracks. These joints can be categorized based on their functions, such as contraction or control joints, construction joints, isolation joints, and expansion joints.
Contraction joints are typically formed by sawing a groove into the concrete shortly after it has hardened. This creates a weakened vertical plane, deliberately encouraging cracking at...

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Related Experiment Video

Updated: Jun 12, 2025

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

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Concrete Crack Detection and Segregation: A Feature Fusion, Crack Isolation, and Explainable AI-Based Approach.

Reshma Ahmed Swarna^1,2, Muhammad Minoar Hossain^1,2, Mst Rokeya Khatun²

¹Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh.

Journal of Imaging

|September 27, 2024

Summary

This study introduces an intelligent scheme for concrete crack detection using fused features from convolutional neural networks and handcrafted methods. The advanced approach achieves high accuracy and provides explanations, improving structural safety assessments.

Keywords:

LDA convex hull crack recognition curvelet transform explainable AI feature fusion

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Area of Science:

Computer Vision
Artificial Intelligence
Structural Health Monitoring

Background:

Current image-based crack detection methods lack performance understanding across varied conditions.
Challenges include image resolution, fine crack detection, and differentiating crack types.
Need for enhanced algorithms for accurate structural assessments.

Purpose of the Study:

To develop an intelligent scheme for recognizing cracks and quantifying their percentage from images.
To fuse features from deep learning and handcrafted methods for improved crack detection.
To integrate explainable AI for enhanced clarity and trust in structural analysis.

Main Methods:

Feature fusion using ResNet-50 (convolutional neural network) and curvelet transform (handcrafted).
Optimization via linear discriminant analysis (LDA) and classification with eXtreme gradient boosting (XGB).
Novel algorithm combining image thresholding, morphological operations, and contour detection for crack quantification; integration of LIME and Grad-CAM++ for explainability.

Main Results:

Achieved 99.93% and 99.69% accuracy on two datasets, outperforming state-of-the-art methods.
Developed a novel algorithm for isolating and quantifying crack regions.
Demonstrated superior performance in crack detection accuracy and interpretability.

Conclusions:

The proposed method offers a robust tool for real-time crack detection in concrete structures.
Enhanced structural safety through timely maintenance facilitated by accurate assessments.
Increased trust and adoption in engineering practice via transparent AI decision-making.