Next Article in Journal
Indentation Response of Calcium Aluminoborosilicate Glasses Subjected to Humid Aging and Hot Compression
Next Article in Special Issue
Sensitivity of Ultrasonic Coda Wave Interferometry to Material Damage—Observations from a Virtual Concrete Lab
Previous Article in Journal
Optimized Zirconia 3D Printing Using Digital Light Processing with Continuous Film Supply and Recyclable Slurry System
Previous Article in Special Issue
Assessment of the Deterioration State of Post-Installed Bonded Anchors Using Ultrasonic
Article

Single-Station Coda Wave Interferometry: A Feasibility Study Using Machine Learning

1
Fachbereich Bau- und Umweltingenieurwesen, Bochum University of Applied Sciences, 44801 Bochum, Germany
2
Fraunhofer IEG, Fraunhofer Research Institution for Energy Infrastructure and Geothermal Systems, 44801 Bochum, Germany
3
Fakultät für Geowissenschaften, Ruhr-University Bochum, 44801 Bochum, Germany
4
Mining Engineering Group, Faculty of Engineering, University of Zanjan, Zanjan 45371-38791, Iran
5
College of Engineering and Applied Science, University of Wyoming, Laramie, WY 82071, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Sukhoon Pyo
Materials 2021, 14(13), 3451; https://doi.org/10.3390/ma14133451
Received: 20 May 2021 / Revised: 17 June 2021 / Accepted: 20 June 2021 / Published: 22 June 2021
(This article belongs to the Special Issue Concrete and Concrete Structures Monitored by Ultrasound)
Coda wave interferometry usually is applied with pairs of stations analyzing the signal transmitted from one station to another. A feasibility study was performed to evaluate if one single station could be used. In this case, the reflected coda wave signal from a zone to be identified was analyzed. Finite-difference simulations of wave propagation were used to study whether ultrasonic measurements could be used to detect velocity changes in such a zone up to a depth of 1.6 m in a highly scattering medium. For this aim, 1D convolutional neural networks were used for prediction. The crack density, the crack length, and the intrinsic attenuation were varied in the considered background material. The influence of noise and the sensor width was elaborated as well. It was shown that, in general, the suggested single-station approach is a possible way to identify damage zones, and the method was robust against the studied variations. The suggested workflow also took advantage of machine-learning techniques, and can be transferred to the detection of defects in concrete structures. View Full-Text
Keywords: coda waves; reflection; machine learning; wave propagation; feasibility study coda waves; reflection; machine learning; wave propagation; feasibility study
Show Figures

Figure 1

MDPI and ACS Style

Saenger, E.H.; Finger, C.; Karimpouli, S.; Tahmasebi, P. Single-Station Coda Wave Interferometry: A Feasibility Study Using Machine Learning. Materials 2021, 14, 3451. https://doi.org/10.3390/ma14133451

AMA Style

Saenger EH, Finger C, Karimpouli S, Tahmasebi P. Single-Station Coda Wave Interferometry: A Feasibility Study Using Machine Learning. Materials. 2021; 14(13):3451. https://doi.org/10.3390/ma14133451

Chicago/Turabian Style

Saenger, Erik H., Claudia Finger, Sadegh Karimpouli, and Pejman Tahmasebi. 2021. "Single-Station Coda Wave Interferometry: A Feasibility Study Using Machine Learning" Materials 14, no. 13: 3451. https://doi.org/10.3390/ma14133451

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop