AI-Driven Non-Destructive Testing Insights

Non-destructive testing (NDT) is essential for evaluating the integrity and safety of structures without causing damage. The integration of artificial intelligence (AI) into traditional NDT methods can revolutionize the field by automating data analysis, enhancing defect detection accuracy, enabling predictive maintenance, and facilitating data-driven decision-making. This paper provides a comprehensive overview of AI-enhanced NDT, detailing AI models and their applications in techniques like ultrasonic testing and ground-penetrating radar. Case studies demonstrate that AI can improve defect detection accuracy and reduce inspection times. Challenges related to data quality, ethical considerations, and regulatory standards were discussed as well. By summarizing established knowledge and highlighting advancements, this paper serves as a valuable reference for engineers and researchers, contributing to the development of safer and more efficient infrastructure management practices.

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Publication Details

Journal
Encyclopedia
Published
2024-11-21
DOI
https://doi.org/10.3390/encyclopedia4040116
Citations
15
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
Field-Weighted Citation Impact
3.91
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article

AI-Driven Non-Destructive Testing Insights

Gilberto Cidreira Keserle, Amine el Mahdi Safhi, Stéphanie C. Blanchard
15 citations
Encyclopedia
Infrastructure Maintenance and Monitoring
3.91
article

AI-Driven Non-Destructive Testing Insights

Gilberto Cidreira Keserle, Amine el Mahdi Safhi, Stéphanie C. Blanchard
article en
15 citations

Abstract

Non-destructive testing (NDT) is essential for evaluating the integrity and safety of structures without causing damage. The integration of artificial intelligence (AI) into traditional NDT methods can revolutionize the field by automating data analysis, enhancing defect detection accuracy, enabling predictive maintenance, and facilitating data-driven decision-making. This paper provides a comprehensive overview of AI-enhanced NDT, detailing AI models and their applications in techniques like ultrasonic testing and ground-penetrating radar. Case studies demonstrate that AI can improve defect detection accuracy and reduce inspection times. Challenges related to data quality, ethical considerations, and regulatory standards were discussed as well. By summarizing established knowledge and highlighting advancements, this paper serves as a valuable reference for engineers and researchers, contributing to the development of safer and more efficient infrastructure management practices.

EncyclopediaVol. 4(4)
EnGlobe (Canada) (CA)
Industry, innovation and infrastructure
Openalex Percentile: Top 6%
Infrastructure Maintenance and Monitoring
3.91
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