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Related Concept Videos

Korotkoff Sounds01:12

Korotkoff Sounds

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Korotkoff sounds are the specific sounds heard while measuring blood pressure using a sphygmomanometer, typically with a stethoscope or a Doppler device. They are named after Russian physician Nikolai Korotkov, who first described them in 1905. These sounds correspond to turbulent blood flow in the artery as the blood pressure cuff is gradually released after inflation.
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Heart Sounds01:15

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Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
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Soundness of Cement01:17

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The soundness of cement refers to the ability of cement paste to retain its volume after setting. Unsound cement can lead to expansion and structural damage due to the presence of free lime, magnesia, and calcium sulfate. Free lime hydrates very slowly, expanding and causing unsoundness, which is difficult to detect because it intercrystallizes with other compounds. Magnesia also reacts with water, forming crystals that can disrupt the cement's structure. Calcium sulfate can create...
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Sound Waves01:01

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Sound waves can be thought of as fluctuations in the pressure of a medium through which they propagate. Since the pressure also makes the medium's particles vibrate along its direction of motion, the waves can be modeled as the displacement of the medium's particles from their mean position.
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Sound Intensity00:58

Sound Intensity

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The loudness of a sound source is related to how energetically the source is vibrating, consequently making the molecules of the propagation medium vibrate. To measure the loudness of a source, the physical quantity of interest is the intensity. This is defined as the energy emitted per unit of time per unit of area perpendicular to the sound wave's propagation direction. Since the total energy is greater if the source vibrates for a longer duration and over a larger area, dividing the...
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Machines01:19

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Computerized Hammer Sounding Interpretation for Concrete Assessment with Online Machine Learning.

Jiaxing Ye1, Takumi Kobayashi2, Masaya Iwata3

  • 1National Metrology Institute of Japan (NMIJ), The National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 2, Tsukuba 305-8568, Japan. jiaxing.you@aist.go.jp.

Sensors (Basel, Switzerland)
|March 10, 2018
PubMed
Summary

This study introduces an AI system using online machine learning for concrete structure analysis via hammering. The novel approach adapts to complex real-world data, achieving near-human performance in defect detection.

Keywords:
audio signal processinghammer soundingmachine learningnon-destructive evaluationonline learning

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Area of Science:

  • Engineering
  • Computer Science
  • Materials Science

Background:

  • Non-destructive testing (NDT) of concrete structures is crucial for safety and maintenance.
  • Current automated systems often struggle with real-world data complexity.
  • Artificial Intelligence (AI) offers potential for enhanced NDT analysis.

Purpose of the Study:

  • To develop an AI-enabled system for hammering response analysis in concrete structures.
  • To achieve near-human performance in defect detection using online machine learning.
  • To create an adaptive system capable of handling complex, unseen data patterns.

Main Methods:

  • A novel two-stage framework for response characterization was proposed.
  • The system utilizes sequential treatment and adaptive model updating.
  • Feature extraction and online learning algorithms were employed and evaluated.

Main Results:

  • The system demonstrated favorable assessment accuracy in defect detection.
  • High efficiency and low computational load were achieved.
  • Experimental validation used 10,940 real-world response instances annotated by experts.

Conclusions:

  • The proposed AI system effectively analyzes hammering responses for concrete defect detection.
  • Online machine learning enables adaptive performance close to human experts.
  • The system offers an efficient and computationally light solution for NDT.