Videos de Conceptos Relacionados
01:20Microcracking in Concrete
508
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...
508
01:28Types of Non-structural Cracks in Concrete
535
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.
535
01:05Difference from Background: Limit of Detection
8.6K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
The LOD indicates the presence or absence...
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01:20Structural Classification of Joints
7.7K
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...
A fibrous joint is where the adjacent bones are united by fibrous connective...
7.7K
01:15Elastic Collisions: Case Study
20.8K
Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
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01:10Detection of Black Holes
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Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
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Scientific reports·2026
Video Experimental Relacionado
Updated: Feb 22, 2026
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Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
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Un marco de detección en tiempo real para grietas en carreteras en entornos ruidosos y morfológicamente complejos
Luxin Fan1, SaiHong Tang2, Mohd Khairol Anuar B Mohd Ariffin3
1Faculty of Engineering, Universiti Putra Malaysia UPM, Serdang, 43400, Selangor, Malaysia. gs59924@student.upm.edu.my.
Scientific reports
|February 20, 2026
Resumen
Este estudio presenta Crack-YOLO, un sistema rápido y preciso para la detección de grietas en carreteras para el mantenimiento de infraestructuras inteligentes. El modelo ligero mejora significativamente el rendimiento de detección en entornos complejos, superando a los métodos existentes.
Área de la Ciencia:
- Visión por Computadora
- Inteligencia Artificial
- Ingeniería Civil
Sus antecedentes:
- La detección automatizada de defectos en carreteras es crucial para el mantenimiento de infraestructuras de tráfico inteligentes.
- Los modelos de detección de objetos existentes tienen problemas con velocidades de inferencia lentas y baja precisión, especialmente en entornos complejos con sombras, manchas de aceite y oclusión.
- Los modelos actuales muestran una fuerte caída en la precisión de detección en condiciones naturales desafiantes.
Objetivo del estudio:
- Proponer un marco de detección de grietas en carreteras ligero y de alta precisión denominado Crack-YOLO.
- Abordar las limitaciones de la baja velocidad y la baja precisión en los sistemas existentes de detección de defectos en carreteras.
- Mejorar el rendimiento de detección en condiciones ambientales complejas.
Principales métodos:
- Se desarrolló Crack-YOLO, un marco novedoso basado en YOLOv8s.
- Se reemplazaron los módulos de convolución originales con módulos Context-Guided (CG).
- Se implementó C2f_DynamicConv para sustituir los kernels de convolución estáticos.
- Se introdujo una cabeza ASFF (Adaptive Spatial Feature Fusion) para reemplazar la cabeza de detección original.
Principales resultados:
- Crack-YOLO demostró una velocidad y precisión de detección superiores en comparación con YOLOv8s en cuatro conjuntos de datos (CrackVariety, CrackTree200, Crack500, CFD).
- Se logró un 71,4% de mAP@0,5 en el conjunto de datos CrackVariety con una velocidad de inferencia de 416 FPS.
- Se mostró un aumento del 31,0% en la precisión y un aumento de casi el doble en la velocidad en comparación con el modelo base YOLOv8s.
- Se implementó con éxito en un dispositivo de borde Raspberry Pi 5 para el cálculo en tiempo real del Índice de Condición del Pavimento (PCI).
Conclusiones:
- Crack-YOLO ofrece una solución práctica para la detección eficiente y precisa de defectos en carreteras, incluso en dispositivos de borde con recursos limitados.
- La capacidad del marco para mantener un alto rendimiento en entornos complejos marca un avance significativo en el mantenimiento de infraestructuras de tráfico inteligentes.
- La integración con estándares como ASTM D6433 permite el cálculo automatizado del PCI, lo que demuestra la aplicabilidad en el mundo real.