LAUSR

Determining the compressive strength of masonry structures is critical for assessing their service life and thus providing safety assurances to their occupants and valued stakeholders. This paper presents a methodology based on various data fusion systems for predicting the compressive strength using data collected from nondestructive testing. According to the experimental readings obtained from the laboratory tests for masonry wallettes, 44 samples are used to construct the training datasets and results validated against a masonry structure located in Kharagpur. The compressive strength of masonry units is predicted using statistical regression models and other state-of-the-art approaches. Two indices, namely the coefficient of determination ( $$R^2$$ R 2 ) and root mean square error, are used to test the performance of different models. The results indicate that both neural network and neuro-fuzzy inference system have a superior predictive capacity than other models and can be reliably employed in the field to evaluate the compressive strength of brick–mortar masonry structures.