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相关概念视频

Dynamic Modulus of Elasticity of Concrete01:16

Dynamic Modulus of Elasticity of Concrete

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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...
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Non-destructive Tests for Concrete Strength01:12

Non-destructive Tests for Concrete Strength

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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...
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相关实验视频

Updated: May 17, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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基于机器学习的堆基础的多点振动采集的智能测试方法

Ke Wang1,2, Weikai Zhao1, Juntao Wu1

  • 1College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.

Sensors (Basel, Switzerland)
|May 14, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种智能机器学习模型,用于评估堆基础完整性,改进了传统方法. 拟议的模型使用振动数据准确地识别了使用中的堆基础中的缺陷.

关键词:
高市值的公司.低应变低应变的压力机器学习是机器学习.多点振动采集器多点振动采集器堆基础的基础 堆基础

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科学领域:

  • 土木工程 土木工程是指土木工程.
  • 地质技术工程 地质技术工程
  • 人工智能的人工智能

背景情况:

  • 传统的低应变反射波方法用于堆基础测试具有局限性.
  • 评估使用中的完整性,高密度堆基础需要先进的技术.

研究的目的:

  • 提出一个智能多点振动采集测试模型,用于评估堆基础完整性.
  • 克服现有的堆检测方法的局限性.

主要方法:

  • 开发一种基于机器学习的模型,利用多点振动采集.
  • 对不同的模型框架进行比较评估,包括卷积神经网络 (CNN) 和长短期记忆 (LSTM) 神经网络.
  • 使用时间序列堆叠方法预处理多传感器融合信号.

主要成果:

  • 两种CNN和LSTM模型都在确定堆轴中的第一个反射点时表现出高精度.
  • 获得的R平方值>0.98,平均绝对误差<0.41m,差异占比>98%.
  • 这些模型表现出强大的预测能力,测试稳定性和实际实用性.

结论:

  • 智能多点振动采集模型对于评估在使用中的堆基础完整性是有效的.
  • 建议使用CNN来分析堆基础完整性,使用预处理的多点振动和多传感器融合信号.
  • 这些发现支持运营商在地质工程应用中的决策.