Research article
Vol. 16 No. 4 (2020): International Journal for Computational Civil and Structural Engineering
A CRACK DETECTION SYSTEM FOR STRUCTURAL HEALTH MONITORING AIDED BY A CONVOLUTIONAL NEURAL NETWORK AND MAPREDUCE FRAMEWORK
A CRACK DETECTION SYSTEM FOR STRUCTURAL HEALTH MONITORING AIDED BY A CONVOLUTIONAL NEURAL NETWORK AND MAPREDUCE FRAMEWORK
DOI:
https://doi.org/10.22337/2587-9618-2020-16-4-38-49Keywords:
deep learning, ground truth segmentation, machine learning, structural health monitoring, crack detection, MapReduceAbstract
The quickly expanded development of artificial intelligence offers alternative ways to solve numerous civil engineering problems. The work is devoted to the development of a computer-vision-based crack detection system capable to process big data related to pathology recognition. In this study, we discuss an automated crack type classification pipeline based on CNN deep learning algorithm and MapReduce framework. The results of numerical modeling illustrate the potential of the crack detection system.
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Published
2020-12-28
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Research article
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
Filatova, D., & El-Nouty, C. (2020). A CRACK DETECTION SYSTEM FOR STRUCTURAL HEALTH MONITORING AIDED BY A CONVOLUTIONAL NEURAL NETWORK AND MAPREDUCE FRAMEWORK. International Journal for Computational Civil and Structural Engineering, 16(4), 38-49. https://doi.org/10.22337/2587-9618-2020-16-4-38-49
Keywords
deep learning, ground truth segmentation, machine learning, structural health monitoring, crack detection, MapReduce
