DETERMINATION OF BUILDINGS WITH TORSIONAL IRREGULARITY BY ARTIFICIAL INTELLIGENCE METHODS

Abstract

Reinforced Concrete (RC) frame buildings with shear wall are widely used in severe seismic zones. Shear walls are bearing system elements that provide the greatest resistance against hori-zontal force under the effect of the earthquake, limit displacements, and prevent torsions. A re-inforced concrete shear wall is one of the most critical structural members in buildings, in terms of carrying lateral loads. However, irregular layouts cause torsional irregularity in buildings. For this purpose, different shear wall frame reinforced concrete building models are designed. The model buildings have a regular formwork plan. The shear wall layout has different variations in each plan. These structure plans were mainly classified into two classes according to their tor-sional irregularities as structures with torsional irregularities and Structures with non-torsional ir-regularities. Artificial intelligence (AI) has revolutionized industries such as healthcare, agricul-ture, transportation, and education, as well as a variety of structural engineering problems. Arti-ficial intelligence is transforming decision-making easier and reshaping building design process-es to be smarter and automated. Artificial intelligence technology of learning from an existing knowledge base is used to automate various civil engineering applications such as compressive strength estimation of concrete, project pre-cost and duration, structural health monitoring, crack detection, and more. In this study, it is aimed to determine the structures with torsional irregulari-ty using artificial intelligence methods. Besides, the study is expected to introduce and demon-strate the capability of Artificial intelligence-based frameworks for future relevant studies within structural engineering applications and irregularities.