Open AccessA CRACK DETECTION SYSTEM FOR STRUCTURAL HEALTH MONITORING AIDED BY A CONVOLUTIONAL NEURAL NETWORK AND MAPREDUCE FRAMEWORK
Author(s) -
Darya Filatova,
Charles El-Nouty
Publication year - 2020
Publication title -
international journal for computational civil and structural engineering
Language(s) - English
Resource type - Journals
eISSN - 2588-0195
pISSN - 2587-9618
DOI - 10.22337/2587-9618-2020-16-4-38-49
Subject(s) - convolutional neural network , computer science , pipeline (software) , process (computing) , artificial intelligence , big data , machine learning , deep learning , artificial neural network , pattern recognition (psychology) , data mining , programming language , operating system
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.