Modeling of compressive strength of masonry structure using relevance vector machine and minimax probability machine regression

Compressive Strength (f) of masonry structure is a key parameter for designing masonry structure. This article employs Relevance Vector Machine (RVM) and Minimax Probability Machine Regression (MPMR) for estimation of compressive strength (f) of a masonry structure. RVM is a Bayesian model. MPMR is constructed based on the concept of Minimax Probability Machine Classification. Mortar compressive strength (f_m), and brick compression strength (f_b) are used as inputs of the RVM and MPMR. The output of RVM and MPMR is f. The results of  RVM and MPMR have been compared with the other models. The results show that the developed RVM and MPMR are useful tools for estimation of f of masonry structure.