Automated air-coupled impact echo based non-destructive testing using machine learning

Tyler Epp; Dagmar Svecova; Young-Jin Cha

doi:10.1117/12.2295947

27 March 2018 Automated air-coupled impact echo based non-destructive testing using machine learning

Tyler Epp, Dagmar Svecova, Young-Jin Cha

Proceedings Volume 10598, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018; 105981M (2018) https://doi.org/10.1117/12.2295947
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2018, Denver, Colorado, United States

Abstract

The integration of sensing technology with structural health monitoring (SHM) has lead to advancements in how structures are monitored and investigated. One of the issues that has accompanied advancement in the industry is the time required to carry out testing on large-scale concrete reinforced structures using methods like impact-echo and ground penetrating radar (GPR). Back end processing and automation of testing systems are two means of addressing time consuming testing programs. This study proposes a semi-autonomous testing setup to carry out impact-echo testing on a lab specimen and a full-scale field structure. The testing method is coupled with artificial neural network (ANN) processing to decrease the need for user-interactions to produce results from the testing. The use of the semi-autonomous testing method and ANN processing is postulated to decrease the time needed for testing and improve the repeatability and accuracy of the impact-echo testing.

Citation Download Citation

Tyler Epp, Dagmar Svecova, and Young-Jin Cha "Automated air-coupled impact echo based non-destructive testing using machine learning", Proc. SPIE 10598, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, 105981M (27 March 2018); https://doi.org/10.1117/12.2295947

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