Open AccessReview on Buildings Health Monitoring System by Using Hybrid Data Optimization Based on Machine Learning Algorithm
Author(s) -
Shweta D Shenmare
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-857
Subject(s) - computer science , artificial intelligence , process (computing) , task (project management) , gray (unit) , structural health monitoring , gray level , point (geometry) , computer vision , visual inspection , image (mathematics) , pattern recognition (psychology) , data mining , algorithm , engineering , structural engineering , mathematics , medicine , geometry , systems engineering , radiology , operating system
The detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. The manual process of crack detection is pains takingly time-consuming and suers from subjective judgments of inspectors. This study establishes an intelligent model based on image processing techniques for automatic crack recognition and analyses. In the new model, a gray intensity adjustment method, called Min-Max Gray Level Discrimination (M2GLD), is proposed to preprocess the image thresholded by the Otsu method. The goal of this gray intensity adjustment method is to meliorate the accuracy of the crack detection results. Experimental results point out that the integration of M2GLD and the Otsu method, followed by other shape analysis algorithms, can successfully detect crack defects in digital images. Therefore, the constructed model can be a useful tool for building management agencies and construction engineers in the task of structure maintenance.