Mechanical properties of masonry using artificial neural networks

Abstract

Non-engineered construction constitutes a significant portion of the existing housing stock in Colombia and other South American countries. These masonry constructions frequently lack technical and engineering characteristics, affecting their performance under seismic and gravitational loads. The assessment of their mechanical properties, such as compressive strength, modulus of elasticity, shear strength, and shear modulus triggers challenges when using existing models, as experimental results of unreinforced masonry houses often deviate from these theoretical models. To address these challenges, machine learning models using neural networks were proposed to predict masonry mechanical properties. To develop the neural networks, a comprehensive database of experimental results from various tests conducted in Colombia was complied, categorizing data into four main masonry types based on material and masonry units: (1) horizontally hollow clay bricks, (2) vertically hollow clay bricks, (3) solid clay bricks, and (4) vertically hollow concrete bricks. These categories encompass the prevalent types of masonry units used in unreinforced masonry constructions. The neural networks developed herein demonstrate superior performance when compared to existing predictive models in the literature, facilitating their application in structural analyses of masonry. In addition, recalibrated equations were proposed to better reflect the properties of masonry of unreinforced constructions. A user-friendly application was developed to enable practical use of both the database and the predictive models, supporting researches and engineers in characterizing masonry for seismic resilience and retrofitting.