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01:20Microcracking 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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01:28Types 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.
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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Research on Synthetic Data Methods and Detection Models for Micro-Cracks.
Yaotong Jiang1, Tianmiao Wang1, Xuanhe Chen2
1School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China.
Sensors (Basel, Switzerland)
|March 28, 2026
Summary
This study introduces a new method to detect tiny cracks in concrete, improving data availability and detection accuracy in challenging conditions. The Complex-Scene-Tolerant YOLO (CST-YOLO) detector enhances real-time inspection capabilities.
Area of Science:
- Civil Engineering
- Computer Vision
- Materials Science
Background:
- Micro-crack detection in concrete is hindered by scarce labeled data, weak crack features, and complex backgrounds.
- Existing methods struggle with robustness and data availability for practical applications.
Purpose of the Study:
- To enhance micro-crack detection data availability and improve detection robustness in complex concrete surfaces.
- To develop a real-time detection system for practical infrastructure inspection.
Main Methods:
- A Poisson image editing-based synthesis strategy was used to generate realistic micro-crack training data.
- A Complex-Scene-Tolerant YOLO (CST-YOLO) detector, based on YOLOv10, was proposed with specialized modules.
- Key modules include Lighting-Adaptive Preprocessing (LAPM), Spatial-Channel Sparse Transformer (SCS-Former), and Small Object Focus Block (SOFB).
Main Results:
- The CST-YOLO detector achieved high performance with 0.990 mAP@0.5 and 0.926 mAP@0.5:0.95 at 139 FPS.
- Ablation studies confirmed the significant contributions of LAPM, SCS-Former, and SOFB modules.
- The model demonstrated effectiveness using synthesized data for training and real images for validation/testing.
Conclusions:
- Combining realistic data synthesis with an robust deep learning architecture significantly improves micro-crack detection.
- The proposed CST-YOLO detector offers a promising solution for real-time, accurate micro-crack inspection in complex environments.
- The study highlights the importance of addressing data scarcity and detection challenges in concrete structural health monitoring.