Soft computing-based models for the prediction of masonry compressive strength

• Two soft computing models are developed for the estimation of masonry compressive strength. • The unit and mortar strengths and the height to thickness ratio have been found as main influencing parameters. • Proposed ANN model proves quite more accurate compared to existing codes and literature models. • A database of 410 individual specimens has been compiled for the development and evaluation of the models. Masonry is a building material that has been used in the last 10.000 years and remains competitive today for the building industry. The compressive strength of masonry is used in modern design not only for gravitational and lateral loading, but also for quality control of materials and execution. Given the large variations of geometry of units and joint thickness, materials and building practices, it is not feasible to test all possible combinations. Many researchers tried to provide relations to estimate the compressive strength of masonry from the constituents, which remains a challenge. Similarly, modern design codes provide lower bound solutions, which have been demonstrated to be weakly correlated to observed test results in many cases. The present paper adopts soft-computing techniques to address this problem and a dataset with 401 specimens is considered. The obtained results allow to identify the most relevant parameters affecting masonry compressive strength, areas in which more experimental research is needed and expressions providing better estimates when compared to formulas existing in codes or literature.