Application of fuzzy cognitive maps for crack categorization in columns of reinforced concrete structures

The detection of damage at an early stage that affects the supporting element of civil structures proves to be very significant to save invaluable human life and valuable possessions. In this research work, the severity of cracks in the supporting column is assessed using a new technique. This piece of research study uses the soft computing method of fuzzy cognitive map (FCM) to model the domain experts' knowledge and the knowledge assimilated through relevant literature to grade the severity of cracks in supporting column. The FCM grading model is further improved by using the Hebbian learning algorithms. The presented work demonstrates the classification and prediction capabilities of FCM for the respective structural health monitoring application, using two well-known and efficient FCM learning approaches viz. nonlinear Hebbian learning (NHL) and data-driven nonlinear Hebbian learning (DD-NHL). The proposed crack severity grading model classifies the cracks in supporting column into three categories, namely fine crack, moderate crack and severe crack. The proposed model uses DD-NHL algorithm. DD-NHL is trained with 70 records and tested with 30 records and gives an overall classification accuracy of 96 %. The obtained results are better compared to other popular machine learning-based classifiers. The proposed method helps even the non-experts to find the possible causes of crack and reports them to structural engineers, to start maintenance in an appropriate stage, using various crack control techniques. Also, a software tool for crack categorization was developed based on the FCM method and its learning capabilities. Thus, it is easier for the users/civil engineers to use this software to make decisions in civil engineering domain and improve their knowledge about the health of the structure.