Microwave Imaging based Automatic Crack Detection System using Machine Learning for Columns

Abstract

Buildings are exposed to damage and deterioration during their life cycle. So, damage assessment plays an important role in Structural stability. Cracks in the structures are of common occurrence, hence early detection of cracks is necessary. Damages like cracks can be detected using Microwave Imaging of the columns. Damages like Horizontal and vertical cracks are determined by training the Bayesian classifier and the Artificial Neural Networks. Both these approaches are required as Structural health to be monitored for predicting damages in columns. Crack detection system is built in columns of civil structures based on Artificial Neural Network and Bayesian Classifiers, which are constructed upon probabilistic pattern recognition and data modelling. The frequency data was collected from 12 microwave sensors for 30 positions of column and is required to train and test the mathematical models. Since, mean and covariance of the statistical data are well known features used in feature extraction. Finally, performance analysis of the models has been provided in terms of Crack Error Rate (CER) justifies that dynamic modelling using ANN yields better results than Bayesian Classifiers and this can also be used in developing Automatic Crack detection systems of civil structures.