TY - EJOU
AU - Shah, Vishal Shreyans
AU - Shah, Henyl Rakesh
AU - Samui, Pijush
AU - Murthy, A. Ramachra
TI - Prediction of Fracture Parameters of High Strength and Ultra-High Strength Concrete Beams using Minimax Probability Machine Regression and Extreme Learning Machine
T2 - Computers, Materials \& Continua
PY - 2014
VL - 44
IS - 2
SN - 1546-2226
AB - This paper deals with the development of models for prediction of facture parameters, namely, fracture energy and ultimate load of high strength and ultra high strength concrete based on Minimax Probability Machine Regression (MPMR) and Extreme Learning Machine (ELM). MPMR is developed based on Minimax Probability Machine Classification (MPMC). ELM is the modified version of Single Hidden Layer Feed Foreword Network (SLFN). MPMR and ELM has been used as regression techniques. Mathematical models have been developed in the form of relation between several input variables such as beam dimensions, water cement ratio, compressive strength, split tensile strength, notch depth, and modulus of elasticity and output is fracture energy and ultimate load A total of 87 data sets (input-output pairs) are used, 61 of which are used to train the model and 26 are used to test the models. The data-sets used in this study are derived from experimental results. A comparative study has been presented between the developed MPMR and ELM models. The results showed that the developed models give reasonable performance for prediction of fracture energy and ultimate load.
KW - High strength concrete
KW - Ultra high strength concrete
KW - Minimax Probability Machine Regression
KW - Extreme Learning Machine
KW - Fracture energy
KW - Ultimate load
DO - 10.3970/cmc.2014.044.073