A Proposed Algorithm Based on Variance to Effectively Estimate Crack Source Localization in Solids

Acoustic emissions (AEs) are produced by elastic waves generated by damage in solid materials. AE sensors have been widely used in several fields as a promising tool to analyze damage mechanisms such as cracking, dislocation movement, etc. However, accurately determining the location of damage in solids in a non-destructive manner is still challenging. In this paper, we propose a crack wave arrival time determination algorithm that can identify crack waves with low SNRs (signal-to-noise ratios) generated in rocks. The basic idea is that the variances in the crack wave and noise have different characteristics, depending on the size of the moving window. The results can be used to accurately determine the crack source location. The source location is determined by observing where the variance in the crack wave velocities of the true and imaginary crack location reach a minimum. By performing a pencil lead break test using rock samples, it was confirmed that the proposed method could successfully find wave arrival time and crack localization. The proposed algorithm for source localization can be used for evaluating and monitoring damage in tunnels or other underground facilities in real time.

Authors

Institutions

Publication Details

Journal
Sensors
Published
2024-09-20
DOI
https://doi.org/10.3390/s24186092
Primary Topic
Geophysical Methods and Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Proposed Algorithm Based on Variance to Effectively Estimate Crack Source Localization in Solids

Young‐Chul Choi, Byun-Young Chung, Doyun Jung
Sensors
Geophysical Methods and Applications
article

A Proposed Algorithm Based on Variance to Effectively Estimate Crack Source Localization in Solids

Young‐Chul Choi, Byun-Young Chung, Doyun Jung
article en

Abstract

Acoustic emissions (AEs) are produced by elastic waves generated by damage in solid materials. AE sensors have been widely used in several fields as a promising tool to analyze damage mechanisms such as cracking, dislocation movement, etc. However, accurately determining the location of damage in solids in a non-destructive manner is still challenging. In this paper, we propose a crack wave arrival time determination algorithm that can identify crack waves with low SNRs (signal-to-noise ratios) generated in rocks. The basic idea is that the variances in the crack wave and noise have different characteristics, depending on the size of the moving window. The results can be used to accurately determine the crack source location. The source location is determined by observing where the variance in the crack wave velocities of the true and imaginary crack location reach a minimum. By performing a pencil lead break test using rock samples, it was confirmed that the proposed method could successfully find wave arrival time and crack localization. The proposed algorithm for source localization can be used for evaluating and monitoring damage in tunnels or other underground facilities in real time.

SensorsVol. 24(18)
Korea Atomic Energy Research Institute (KR)
National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Sustainable cities and communities
Openalex Percentile: Top 80%
Geophysical Methods and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A Proposed Algorithm Based on Variance to Effectively Estimate Crack Source Localization in Solids — Young‐Chul Choi, Byun-Young Chung, et al. · Sensors (2024) | TGRS Research Map | TGRS