Proper placement of load-bearing elements plays a critical role in earthquake-resistant building design. Design mistakes and deficiencies in the arrangement of columns, shear walls, and beams may reduce seismic performance, increase torsional effects, cause frame discontinuities, and lead to inefficient or uneconomical structural solutions. Therefore, early identification of such deficiencies directly from structural floor plans can provide important support during both preliminary design and rapid assessment stages. This study presents an automated methodology for detecting, classifying, and evaluating load-bearing elements in RC building floor plans by integrating image processing and deep learning techniques. The YOLO object detection algorithm was employed to identify structural components within floor plan images. A dataset comprising 500 RC building floor plans was developed, and structural elements were manually annotated and labeled for model training and validation. Unlike previous approaches that mainly focus on limited structural components, the proposed model detects columns, shear walls, and four different beam types according to their support conditions. In addition, detected bounding box coordinates are converted into real-world dimensions using grid distances obtained from drawings, allowing the cross-sectional dimensions of each element to be determined. The model achieved high detection performance, particularly for columns, shear walls, and main beams, with accuracy values exceeding 93%. The results show that the proposed approach can accurately obtain information from structural floor plans and provide a promising decision-support system.
