ARTIFICIAL NEURAL NETWORKS AND THEIR APPLICATION IN THE DESIGN OF MASONRY STRUCTURES

Abstract

Masonry structures are widely used thanks to the variety of materials used for their construction. Calculating their load-bearing capacity is complex because walls are composite materials with an inhomogeneous and anisotropic nature. As masonry structures are exposed to different types of loads, accurate determination of load bearing capacity is a key aspect in the design and reconstruction phases, which represents a major design challenge to ensure the safety and efficiency of the structure. Artificial neural networks (ANN) are increasingly being used to solve engineering problems, including data analysis, decision making, optimization, and structural response prediction. Their application in civil engineering allows for a more accurate and faster load analysis compared to traditional methods. This paper aims to present the existing research in literature dealing with the application of ANN in the calculation of the bearing capacity of masonry structures, analyzing their advantages and possibilities in the optimization of design solutions.