Masonry compressive strength prediction using artificial neural networks

The masonry is not only included among the oldest building materials, but it is also the most widely used material due to its simple construction and low cost compared to the other modern building materials. Nevertheless, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength, based on the geometrical and mechanical characteristics of its components. This limitation is due to the highly nonlinear relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the masonry. In this paper, the application of artificial neural networks for predicting the compressive strength of masonry has been investigated. Specifically, back-propagation neural network models have been used for predicting the compressive strength of masonry prism based on experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of masonry walls in a reliable and robust manner.- (undefined

Strength and elasticity of brick masonry prisms and wallettes under compression

The characteristics of brick masonry are influenced by the properties of bricks and mortar. This paper attempts at studying the properties of brick masonry using table moulded bricks and wire-cut bricks of India with various types of mortars. The strength and elastic modulus of brick masonry under compression have been evaluated for strong-brick soft-mortar and soft-brick strong-mortar combinations. Various sizes of prisms and wallettes have been tested during these experiments to study the size effect and different bonding arrangements. The failure mechanisms of such specimens have been studied. Attempts are also made to derive empirical relationships for masonry strength as a function of brick and mortar strength in the Indian context