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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...

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...

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.

Placing Concrete

Placing Concrete

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,...

Tensile Strength Considerations of Concrete

Tensile Strength Considerations of Concrete

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...

Creep in Concrete

Creep in Concrete

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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Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures.

Luqman Ali¹, Fady Alnajjar¹, Hamad Al Jassmi²

¹Department of Computer Science and Software Engineering, College of Information Technology, UAEU, Al Ain 15551, United Arab Emirates.

Sensors (Basel, Switzerland)

|April 3, 2021

Summary

A customized convolutional neural network (CNN) excels at concrete crack detection. This CNN and VGG-16 show superior performance, especially with limited, diverse data, outperforming other deep learning models.

Keywords:

automatic inspection convolutional neural networks crack detection deep learning transfer learning

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Area of Science:

Civil Engineering
Computer Science
Artificial Intelligence

Background:

Concrete structures are vital infrastructure.
Cracks in concrete can compromise structural integrity.
Automated crack detection is crucial for timely maintenance.

Purpose of the Study:

To propose a customized convolutional neural network (CNN) for concrete crack detection.
To compare the proposed CNN with existing deep learning models.
To analyze the impact of training data characteristics on model performance.

Main Methods:

Developed a customized CNN model.
Compared performance against VGG-16, VGG-19, ResNet-50, and Inception V3.
Evaluated models on eight datasets varying in size and diversity.
Assessed computational time, crack localization, and classification metrics (accuracy, precision, recall, F1-score).

Main Results:

Training data size and heterogeneity significantly impact model performance.
Overfitting occurred with increased data size and reduced diversity.
The customized CNN and VGG-16 demonstrated superior classification and localization on limited, diverse data.
These models also showed competitive computational efficiency.

Conclusions:

Customized CNNs and VGG-16 are effective for concrete crack detection and localization.
Data diversity is critical for robust model generalization.
Model performance is sensitive to training dataset characteristics.