A Deep Learning-Based Decision Support System for Structural Component Detection from Architectural Floor Plans

Abstract

Earthquake-resistant design of reinforced concrete (RC) buildings is initiated during the architectural design stage, where the placement and directions of vertical load-bearing elements are defined. The configuration of these elements plays a critical role in the seismic performance of RC structures. In this study, a decision-support system is proposed to automatically identify load-bearing elements from architectural floor plans and to assess torsional irregularity and shear wall distribution. A dataset comprising 600 architectural floor plans of existing buildings was compiled, in which structural components were manually annotated and used to train a YOLO (You Only Look Once)-based detection model. The developed system first detects vertical load-bearing elements in the floor plan and subsequently evaluates their arrangement using structural engineering criteria, including torsional irregularity and shear wall ratio. The results show that the proposed approach enables rapid and accurate evaluation of architectural floor plans, offering effective support during the early design phase.