Estimation of Punching Shear Capacity of Concrete Slabs Using Data Mining Techniques

Abstract

Punching shear capacity is a key factor for governing the collapsed form of slabs. This fragile failure that occurs at the slab-column connection is called punching shear failure and has been of concern for the engineers. The most common practice in evaluating the punching strength of the concrete slabs is to use the empirical expressions available in different building design codes. The estimation of punching loads involves experimental setup which is time-consuming, uneconomical and also, more manpower and materials are required. The present study demonstrates the use of data mining techniques as a substitute of former to predict the punching loads on the variation of various parameters. In this study, various type of data mining techniques including Adaptive Neuro-fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Generalized Neural Network (GRNN) were applied to model and estimate the punching load of reinforced concrete slab–column connections. For the study, a data set consisting of 89 observations from available literature was analysed and randomly selected 62 observations were used for model development whereas the rest 27 were used to test the developed models. While the outcomes of ANN and GRNN model provides suitable estimation performance, the Gaussian membership based ANFIS model performed best in the determination of coefficient of correlation (Cc). Sensitivity study indicates that the parameter effective depth of slab (d) is the most influencing one for the estimation of punching load of reinforced concrete slab–column connections for this data set.