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Related Concept Videos

Echo01:06

Echo

The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
Imagine the sound is reflected back to the ears. Assuming that the source is very close to the human, the difference between hearing the two sounds—the emitted sound and the reflected sound—may be more than the minimum time for perceiving distinct sounds. If this is the case, then the...
Atomic Emission Spectroscopy: Overview01:20

Atomic Emission Spectroscopy: Overview

Atomic emission spectroscopy (AES) is an analytical technique used to determine the elemental composition of a sample by analyzing the light emitted from excited atoms. In AES, atoms in a sample are excited to higher energy levels by thermal energy from high-temperature sources, such as plasma, arcs, or sparks. When these excited atoms return to lower energy states, they emit light at specific wavelengths characteristic of each element. The resulting atomic emission spectrum, which consists of...
Atomic Emission Spectroscopy: Instrumentation01:22

Atomic Emission Spectroscopy: Instrumentation

The instrumentation of atomic emission spectrometry (AES) involves various components, including atomization devices that convert samples into gas-phase atoms and ions. There are two main types of atomization devices: continuous and discrete atomizers.  Continuous atomizers, like plasmas and flames, introduce samples in a constant stream, while discrete atomizers inject individual samples using syringes or autosamplers. The most common discrete atomizer is the electrothermal atomizer.
Atomic Emission Spectroscopy: Interference01:30

Atomic Emission Spectroscopy: Interference

In atomic emission spectroscopy (AES), high-temperature atomizers excite a broad range of elements and molecules that generate complex emissions from sources such as oxides, hydroxides, and flame combustion products in the flame or plasma. Several strategies can be employed to minimize spectral interferences caused by overlapping emission lines or bands. These include increasing instrument resolution, choosing alternative emission lines, optimally placing the detector in low-background regions,...
Atomic Emission Spectroscopy: Lab01:29

Atomic Emission Spectroscopy: Lab

AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
Microcracking in Concrete01:20

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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...

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Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
04:58

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Published on: January 6, 2023

Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization.

Jonathan Melchiorre1, Amedeo Manuello Bertetto1, Marco Martino Rosso1

  • 1Department of Structural, Geotechnical and Building Engineering (DISEG), Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10128 Turin, Italy.

Sensors (Basel, Switzerland)
|January 21, 2023
PubMed
Summary

This study introduces two methods for detecting acoustic emission (AE) signal onset times. An artificial intelligence approach using a recurrent convolutional neural network significantly improves crack localization accuracy in structural monitoring.

Keywords:
Akaike Information Criterion (AIC)acoustic emissionartificial neural networkcrack locationseismic signalssound event detectionsource location

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Area of Science:

  • Structural Health Monitoring
  • Non-Destructive Testing
  • Signal Processing

Background:

  • Acoustic emission (AE) is a passive non-destructive technique for monitoring structures.
  • AE utilizes piezoelectric sensors to detect elastic waves from crack formation.
  • Accurate crack localization relies on precise determination of signal onset times.

Purpose of the Study:

  • To present and evaluate two novel techniques for determining the onset time of AE signals.
  • To enhance the accuracy of crack localization in structural monitoring.
  • To explore the application of artificial intelligence in AE signal analysis.

Main Methods:

  • Comparison of two onset time detection methods: Akaike Information Criterion (AIC) and artificial intelligence (AI).
  • Development and training of a recurrent convolutional neural network (R-CNN) for sound event detection (SED).
  • Training the R-CNN on seismic and AE datasets, followed by testing on a real-world AE dataset.

Main Results:

  • The AI-based method demonstrates enhanced accuracy in determining AE signal onset times.
  • Leveraging similarities between seismic, sound, and AE signals improves detection precision.
  • Improved onset time detection directly translates to more accurate crack localization.

Conclusions:

  • The developed AI technique offers a more accurate approach to AE onset time determination.
  • This advancement contributes to more reliable structural health monitoring and damage assessment.
  • The study highlights the potential of AI in analyzing complex acoustic signals for engineering applications.