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相关概念视频

Microcracking in Concrete01:20

Microcracking 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...
613
Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

666
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.
666

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相关实验视频

Updated: Apr 29, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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通过CNN-block开发机制和边缘造型,有效地识别裂和表面类型.

Ali Raza1, Fareeha Hanif2, Heba Abdelgader Mohammed3

  • 1Department of Mathematics, University of the Punjab, Quaid e Azam Campus, Lahore, Pakistan. alleerazza786@gmail.com.

Scientific reports
|November 17, 2025
PubMed
概括

一个新的轻量级神经网络有效地将裂和表面类型分为六类. 该模型为在资源有限的环境中进行结构性健康监测提供了高精度和可解释性.

关键词:
建筑的复杂性 建筑的复杂性卷积神经网络是一种卷积神经网络.损坏检测 检测损坏检测 损坏检测图像建模 图像建模轻量级的足迹 轻量级的足迹

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科学领域:

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

背景情况:

  • 自动裂检测对于基础设施健康监测至关重要.
  • 现有的方法往往缺乏多类能力,并且对于实时应用程序来说是计算密集型的.
  • 需要为边缘设备提供高效,可部署的模型.

研究的目的:

  • 开发一个轻量级的卷积神经网络 (CNN) 用于多类裂和表面类型的分类.
  • 创建一个适合实时和边缘部署的紧而高性能模型.
  • 确保模型是可解释的,并且对现实世界的扭曲具有稳定性.

主要方法:

  • 使用CNN-Block开发机制 (CNN-BDM) 开发了一个轻量级的CNN.
  • 集成的域驱动数据增强,平衡的标签设计和系统的规范化.
  • 代地改进了架构,以创建Lite-V2模型.
  • 在SDNET2018,CrackForest (CFD) 和DeepCrack数据集上验证了性能.
  • 采用Grad-CAM进行可解释性和扰动实验以获得稳定性.

主要成果:

  • 在SDNET2018上,Lite-V2架构在仅有0.28万个参数的情况下,获得了0.928的宏F1得分和95.7%的准确性.
  • 在CFD上表现出强大的泛化,F1得分为0.975,在DeepCrack上为0.96.
  • 在树派4上实现了显著降低的推断延迟 (11毫秒),超过了MobileNetV2,EfficientNet-B0和ResNet-18.
  • 对亮度和模糊差异表现出强大的弹性.

结论:

  • Lite-V2是一个高效,易于解释和部署的裂分类框架.
  • 该模型非常适合在资源有限的环境中实践状态监测.
  • CNN-BDM方法促进了紧而有效的深度学习模型的开发.