Autoclaved aerated concrete (AAC) attracts attention as it provides superior material characteristics such as high thermal insulation and environmentally friendly properties. Apart from non-structural applications, AAC is being considered as a structural material due to its characteristics such as lighter weight compared to normal concrete, resulting in lower design cost. This study focuses on the feasibility of support vector regression (SVR) in predicting the shear resistance of reinforced AAC slabs. An experimental dataset with 271 data points extracted from eight sources is used to develop models. Based on random selection, the dataset is divided into two portions, 75% for model development and 25% is for testing the validity of the model. Two SVR model types (epsilon and Nu) and four kernel functions (linear, polynomial, sigmoid and radial basis) are used for model development and the results of each model and kernel type is presented in terms of correlation coefficient (R2) and mean squared error (MSE). Results show that epsilon model type with radial basis function yields the best SVR model.
Autoclaved aerated concrete, reinforced concrete slab, shear strength, support vector regression, modelling