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相关概念视频

Maximum Deflection01:13

Maximum Deflection

424
When analyzing beams under unsymmetrical loads, such as a train moving on a bridge, it is crucial to accurately determine the points of maximum stress and deflection. The process involves identifying the maximum deflection of the beam, which may not always occur at its midpoint due to the uneven distribution of the load.
The maximum deflection occurs at a specific point, known as point O, where the tangent to the deflection curve is horizontal. To find point O, the slope of the tangent at any...
424
Shrinkage in Concrete01:27

Shrinkage in Concrete

67
Shrinkage in concrete is primarily due to water loss from evaporation, hydration of cement, or carbonation, leading to a reduction in volume. The volumetric contraction results in volumetric strain in concrete. However, in practice, shrinkage is measured as linear strain, which is one-third of the volumetric strain.
When concrete is still in its plastic state, it can undergo a decrease in volume by about 1% of its absolute volume. This decrease is known as plastic shrinkage. It arises either...
67
Beams with Unsymmetric Loadings01:17

Beams with Unsymmetric Loadings

107
Analyzing a supported beam under unsymmetrical loadings is essential in structural engineering to understand how beams respond to varied force distributions. This analysis involves calculating the deflection and identifying points where the slope of the beam is zero, which are crucial for ensuring structural stability and functionality.
The first moment-area theorem determines the slope at any point on the beam. This theorem indicates that the change in slope between two points on a beam...
107
Microcracking in Concrete01:20

Microcracking in Concrete

96
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...
96
Dynamic Modulus of Elasticity of Concrete01:16

Dynamic Modulus of Elasticity of Concrete

231
The dynamic modulus of elasticity assesses how a concrete structure deforms under impact or dynamic loads. It is typically higher than the static modulus of elasticity, measured under slow, steady loading conditions.
The sonic test is a common method to determine the dynamic modulus. In this test, a concrete beam, sized either 6 x 6 x 30 inches or 4 x 4 x 20 inches, is clamped at its center. Vibrations are initiated at one end of the beam by an electromagnetic exciter unit powered by...
231
Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

102
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...
102

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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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基于SATH-YOLO模型的混凝土桥梁缺陷的智能检测算法

Lanlin Zou1, Ao Liu1

  • 1College of Automotive and Transportation Engineering, Wuhan University of Science and Technology, Wuhan 430081, China.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
概括
此摘要是机器生成的。

一个新的轻量级SATH-YOLO模型通过减少计算复杂性和模型大小来增强桥梁缺陷检测. 这一创新提高了效率而不牺牲准确性,使其成为边缘设备的理想选择.

关键词:
适应性功能内部交互互动星际网络 星际网络 星际网络桥梁缺陷检测检测 桥梁缺陷检测功能融合功能融合功能轻量级的建筑轻量级的建筑.任务动态检测检测任务动态检测

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科学领域:

  • 土木工程 土木工程是指土木工程.
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 传统的物体检测模型,如YOLOv8,在桥梁缺陷检测方面面临挑战,原因是高计算需求和缓慢的处理速度.
  • 智能建筑和检查需要更高效,更准确的自动缺陷识别系统.

研究的目的:

  • 提出一个轻量级和高效的物体检测模型,SATH-YOLO,用于检测桥梁缺陷.
  • 提高对资源有限的环境和边缘设备的桥梁检查系统的性能.

主要方法:

  • 通过将星网中的星块集成到STNC2f模块中来开发SATH-YOLO模型,以实现增强的语义丰富和多尺度特征融合.
  • 将SPPF模块替换为AIFI模块,以捕捉更细粒度的局部特征并提高特征融合精度.
  • 实现了一个轻量级的TDMDH检测头,具有共享的卷积和动态特征选择,以进一步降低计算成本.

主要成果:

  • 通过SATH-YOLO模型实现了参数数量 (39.9%),计算 (8.6%) 和模型大小 (36.2%) 的显著减少.
  • 与传统模型相比,保持和提高了1%的平均检测精度.
  • 证明了该模型适用于边缘设备和计算资源有限的环境的适用性.

结论:

  • SATH-YOLO模型为桥梁缺陷检测提供了一个计算效率高,准确的解决方案.
  • 拟议的模型有效平衡性能和资源利用,满足智能检查系统的需求.