Research Topic

Artificial Intelligence in Nondestructive Testing of Civil Engineering Materials

About this Research Topic

Applying nondestructive testing (NDT) to civil engineering materials allows for more effective quality management than conventional testing methods, which is why NDT has become the main method for civil structure testing. However, the data collected for quality control of civil engineering materials are becoming more complex and higher design goals are required. Therefore, a better way of evaluating test results is needed.

Deep learning, a new type of artificial intelligence (AI) algorithm, uses multilayer neural networks to alleviate the local minima of traditional training algorithms effectively. Combining deep learning algorithms with advanced NDT is a growing area of development for modern civil engineering material testing.

This Research Topic aims to publish Original Research and Review articles exploring innovations in advanced nondestructive testing methods combined with AI for civil engineering materials. Subjects of interest include, but are not limited to:

• Innovation in advanced nondestructive testing method for civil engineering material
• Studying on the combination of nondestructive practice and AI
• Multi-sensor AI fusion in NDT of civil engineering materials
• Time-series signal analysis for NDT of civil engineering material
• Development of remote sensors and data processing algorithm for precision material property measures
• Theoretical, analytical, and experimental investigations covering all aspects of AI for NDT


Keywords: civil engineering materials, nondestructive testing, artificial intelligence, neural networks, deep learning


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Applying nondestructive testing (NDT) to civil engineering materials allows for more effective quality management than conventional testing methods, which is why NDT has become the main method for civil structure testing. However, the data collected for quality control of civil engineering materials are becoming more complex and higher design goals are required. Therefore, a better way of evaluating test results is needed.

Deep learning, a new type of artificial intelligence (AI) algorithm, uses multilayer neural networks to alleviate the local minima of traditional training algorithms effectively. Combining deep learning algorithms with advanced NDT is a growing area of development for modern civil engineering material testing.

This Research Topic aims to publish Original Research and Review articles exploring innovations in advanced nondestructive testing methods combined with AI for civil engineering materials. Subjects of interest include, but are not limited to:

• Innovation in advanced nondestructive testing method for civil engineering material
• Studying on the combination of nondestructive practice and AI
• Multi-sensor AI fusion in NDT of civil engineering materials
• Time-series signal analysis for NDT of civil engineering material
• Development of remote sensors and data processing algorithm for precision material property measures
• Theoretical, analytical, and experimental investigations covering all aspects of AI for NDT


Keywords: civil engineering materials, nondestructive testing, artificial intelligence, neural networks, deep learning


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

28 June 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

28 June 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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