Microwave Imaging Based Damage Detection in Columns Using Artificial Neural Network

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 are detected using Microwave sensors for columns. Damages like Horizontal and vertical cracks are determined by training Artificial Neural Network with known data. ANN approach is required as a Structural health monitoring tool for predicting damage in columns. Crack detection system is built in columns of civil structures based on Artificial Neural Network. This is 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 ANN model. Since, mean and covariance of the statistical data are well known features used in feature extraction. Finally, performance analysis of the model in terms of Crack Error Rate (CER) justifies that dynamic modelling using ANN yields better results and this can also be used in developing Automatic Crack detection systems.