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- ArticleAçık Erişim2026A Deep Learning-Based Decision Support System for Structural Component Detection From Architectural Floor Plans
Karakose, Perihan; Ince, Elif Gokce; Eren, Erkin; Atar, Muhammed; Cakil, Burak; Ince, Ozan
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 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.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2026Automated identification of structural elements in RC buildings based on structural floor plan data
İnce, Ozan; Karaköse, Perihan; İnce, Elif Gökçe; Eren, Erkin; Çakil, Burak; Atar, Muhammed
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.
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