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Microcracking in Concrete01:20

Microcracking in Concrete

115
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...
115
Lumber Defects01:23

Lumber Defects

110
Lumber defects, which can affect both the appearance and structural integrity of wood, include a variety of growth and manufacturing flaws. Growth defects such as knots and knotholes occur where branches were once attached to the tree trunk, with knotholes forming when these knots fall out. Other natural defects include decay and insect damage, which compromise the wood's strength and durability.
Shakes are minor fractures that run along or across the wood's annual rings, while wane is...
110
Prismatic Beams: Problem Solving01:15

Prismatic Beams: Problem Solving

110
In the design of a supported timber beam subjected to a distributed load, both the beam's physical dimensions and the timber's characteristics, such as its grade and species, are critical. These factors determine the allowable stress values, which are crucial for calculating the necessary beam depth to ensure structural integrity and safety.
The design begins with analyzing the beam as a free body to identify moments and force balances, thereby determining support reactions. Next, the...
110
Lumber01:19

Lumber

110
Lumber is derived from logs which are harvested, debarked, and processed into long pieces with a rectangular cross-section. The transformation of logs into lumber involves multiple steps, beginning with an automated saw that slices the log into slabs. These slabs are then transported via a conveyor belt to smaller saws, where they are cut into square-edged pieces of specific widths.
Initially, the surfaces of these lumber pieces are rough, and their dimensions may vary slightly from one end to...
110
Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

140
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.
140
Structural Properties and Dimensions of Lumber01:21

Structural Properties and Dimensions of Lumber

82
Wood's structural properties derive from fibers aligned along the tree's length, contributing significantly to its mechanical strength. Wood exhibits up to twenty times greater tensile strength along these fibers compared to across them, and generally shows better performance under compression than tension. The length of fibers varies, with hardwoods having fibers around one twenty-fifth inch long and softwoods ranging from one-eighth to one-third inch.
The strength characteristics of...
82

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相关实验视频

Updated: Jun 21, 2025

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
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Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

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ICDW-YOLO:一个高效的木制建筑裂纹检测算法

Jieyang Zhou1, Jing Ning2, Zhiyang Xiang1

  • 1College of Computer Science and Engineering, Jishou University, Jishou 416000, China.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了ICDW-YOLO,一种改进的木材裂检测算法. 增强的YOLOv8模型有效地识别小裂,改善结构安全和生产质量.

关键词:
裂检测 裂检测 裂检测 裂检测神经网络的神经网络的神经网络对象检测检测对象检测对象检测

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科学领域:

  • 计算机视觉 计算机视觉
  • 材料科学 材料科学 材料科学
  • 结构健康监测 结构健康监测

背景情况:

  • 对木材的精确裂纹检测对于安全和质量控制至关重要.
  • 现有的算法在木材裂检测中难以处理尺度变化和数据质量.

研究的目的:

  • 为木材材料开发一个改进的裂检测算法.
  • 增强YOLOv8模型,以便更好地识别小而多样化的裂.

主要方法:

  • 拟议的ICDW-YOLO (改善木材破裂检测-YOLO) 基于YOLOv8.
  • 引入了新的子网络,层结构 (GSConv,GS瓶) 和带有双层注意力的定算法.
  • 实现了收集分配机制和更高分辨率的输入层.

主要成果:

  • 在定制木裂数据集上,ICDW-YOLO实现了79.018%的mAP50-95,比YOLOv8.69提高了1.869%.
  • 在火/烟,空中遥感和 coco128 数据集上展示了强大的概括.
  • 在不同的数据集上实现了具有竞争力的mAP50 (69.226%) 和mAP50-95 (44.210%).

结论:

  • 拟议的ICDW-YOLO算法有效地检测木材材料中的裂,精度提高,目标灵敏度小.
  • 这些改进提供了更好的功能融合和识别,而不会显著增加复杂性.
  • 在各种检测任务中,ICDW-YOLO显示出强大的概括能力.