Artificial Intelligence techniques for the automatic interpretation of data from non-destructive testing

Document identifier:
Access full text here:10.1784/insi.2006.48.1.10
Keyword: Natural Sciences, Computer and Information Sciences, Naturvetenskap, Data- och informationsvetenskap, Automatisk inspektion av järnvägsslipers
Publication year: 2006
Relevant Sustainable Development Goals (SDGs):
SDG 11 Sustainable cities and communities
The SDG label(s) above have been assigned by


This paper attempts to summarise the findings of a large number of research papers deploying artificial intelligence (AI) techniques for the automatic interpretation of data from non-destructive testing (NDT). Problems in the rail transport domain are mainly discussed. However, a majority of the emphasis in this paper is laid on rail inspection problems, since it was believed that the review would provide a perfect ground to the authors in pursuing further work within the rail inspection area. NDT is a broad name for a variety of methods and procedures concerned with all aspects of uniformity, quality and serviceability of materials and structures, without causing damage to the material that is being inspected. During the past several years, problems concerning the automatic interpretation of data from NDT have received good attention and have stimulated interests in other areas like transportation, for making key assessments within some of its subject areas. Rail, air and marine industries together with bridge inspection and pavement maintenance are good examples of such areas where a considerable amount of work has been done. Such work neatly splits into two schools. The first school investigates the classical usage of data by an experienced human operator to determine the condition of the inspected material or structure. The other school focuses attention on the automatic interpretation of NDT data using AI techniques, in determining the result of inspection. The scope of this paper is only limited to the automatic interpretation of data from NDT, with the goal of assessing embedded flaws as quickly and accurately as possible in a cost effective fashion. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in the area. A secondary goal was to provide an insight into possible research methods concerning railway sleeper inspection by automatic interpretation of data. A brief introduction is provided for the benefit of the readers unfamiliar with the techniques.


Siril Yella

Högskolan Dalarna; Datateknik
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Mark Dougherty

Högskolan Dalarna; Datateknik
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N K Gupta

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