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相关概念视频

Microcracking in Concrete01:20

Microcracking in Concrete

205
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...
205
Types of Non-structural Cracks in Concrete01:28

Types of Non-structural Cracks in Concrete

244
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.
244
Instrument Transformers01:23

Instrument Transformers

141
Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
141

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使用混合视觉变压器进行智能城市基础设施监测

Rashid Nasimov1, Young Im Cho1

  • 1Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of Korea.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
概括

这项研究引入了用于结构健康监测 (SHM) 的新深度学习模型,可以准确地检测基础设施中的微裂. 视觉局部特征探测器 (ViLFD) 在识别微妙的结构缺陷方面实现了最先进的性能.

科学领域:

  • 土木工程
  • 计算机科学
  • 人工智能

背景情况:

  • 目前的结构健康监测 (SHM) 方法效率低,劳动密集,易出错,特别是在检测微裂时.
  • 细微的结构异常需要先进的检测技术以确保基础设施的安全性和寿命.
  • 深度学习为城市基础设施的自动和精确缺陷识别提供了潜力.

研究的目的:

  • 为可靠的结构健康监测 (SHM) 开发一个新的深度学习框架.
  • 增强缺陷检测能力, 特别是微小的异常如微裂.
  • 建立自动化结构缺陷识别的新技术.

主要方法:

  • 开发了一个改进的检测变压器 (DETR) 架构,集成了一个视觉变压器 (ViT) 骨干.
  • 一个局部特征提取器 (LFE) 模块旨在增强复杂的局部空间特征的提取.
  • 视觉局部特征探测器 (ViLFD) 模型在基准和定制数据集上进行了训练和验证.

主要成果:

  • ViLFD模型表现出比现有的DETR和YOLO变种更高的性能.
  • 获得了高准确度 (95.0%),精度 (0.94),回忆 (0.93),F1得分 (0.93) 和mAP@0.5 (0.89).
  • 在实验验证中成功和可靠地检测出微妙的结构缺陷.
关键词:
基础设施安全检测微裂纹智慧城市结构健康监测城市基础设施

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结论:

  • 拟议的基于ViT的DETR框架与LFE模块显著提升了SHM能力.
  • ViLFD模型为自动检测结构缺陷提供了精确可靠的解决方案.
  • 这是迈向更安全,更可靠的城市基础设施的重要一步.