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Digitizing older bridges is difficult because manual extraction from 2D drawings is time-consuming and error-prone and often lacks BIM models. This study automatically detects key structural components, including anchors, tendons, and section views, using YOLOv8/YOLOv11 with OCR via Pytesseract to extract component IDs and improve BIM-ready model creation.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/automated-extraction-of-structural-elements-and-section-views-from-bridge-drawings-using-deep-learning-paper/649675/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/automated-extraction-of-structural-elements-and-section-views-from-bridge-drawings-using-deep-learning-paper/649675.png","ImageObject",300,407,{"name":92,"@type":93},"A glass of water","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-26","2026-10-24",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the paper address?","Question",{"text":112,"@type":113},"It addresses the challenge of digitizing existing bridges when BIM models are missing, since manual extraction from 2D bridge drawings is slow and error-prone.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which methods are used to extract structural information?",{"text":117,"@type":113},"The approach uses state-of-the-art object detection models (YOLOv8 and YOLOv11) to detect structural elements and section views, and OCR with Pytesseract to extract IDs for anchors and tendons.",{"name":119,"@type":110,"acceptedAnswer":120},"What kinds of outputs does the method produce for BIM workflows?",{"text":121,"@type":113},"It produces extracted data that provides semantic information about key structural components, supporting the creation of high-quality digital bridge models and improving integration into BIM workflows.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},649675,1792977029,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":46},962090760832,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","42nd International Symposium on Automation and Robotics in Construction (ISARC 2025)  \nAutomated Extraction of Structural Elements and Section Views from Bridge Drawings Using Deep Learning  \nHakan Bayer 1 and Markus Knig 1  \n1 Chair of Computing in Engineering, Ruhr University Bochum, Bochum, Germany  \n[hakan.bayer@ruhr-uni-bochum.de](hakan.bayer@ruhr-uni-bochum.de), [koenig@inf.bi.rub.de](koenig@inf.bi.rub.de)  \nAbstract  \nTechnical drawings are essential for managing and maintaining existing bridges, especially in creating accurate digital models and integrating them into Building Information Modeling (BIM) workflows. However, digitizing older bridges, which often lack BIM models is challenging due to the time-consuming and error-prone process of manual data extraction from 2D drawings. Therefore, this study focuses on detecting key structural components, such as anchors, tendons, and section views, which are critical for structural analysis, maintenance, and retrofitting. Using state-of-the-art object detection models, YOLOv8 andYOLOv11, the proposed pipeline achieves a mean average precision (mAP) of 97.4% for anchors, 95.7% for tendons, and 63.9% for section views. Optical Character Recognition (OCR) tool Pytesseract further enhances the process by accurately extracting IDs for anchors and tendons with 90% precision. Combining object detection with precise text extraction simplifies the creation of high-quality digital bridge models and improves BIM integration. Specifically, the extracted data provides semantic information on key structural components. This information can facilitate the process of creating a digital model and integrating it into BIM workflows.  \nKeywords  \nBuilding Information Modeling; Computer Vision; Deep Learning; Symbol Detection; Optical Character Recognition; Construction Drawings  \n1 Introduction  \nBridges are a crucial component of transportation infrastructure that enables the transportation of people and goods. Maintaining their functionality and ensuring safe operations requires routine inspections and effective maintenance management. Digital tools like Building Information Modelling (BIM) can enhance maintenance planning efficiency. BIM is a collaborative digital methodology that involves creating and sharing interdisciplinary digital models of structures [1] . These models contain semantic and geometric information, sharing the state of the project in real time.  \nAmong all phases of a project’s life cycle, the operations and maintenance (O&M) phase presents significant opportunities for BIM application [2] . A key challenge however lies in the limited availability of accurate digital building models for existing structures [3] . In many cases, this is because the old constructions were designed and constructed without the implementation of BIM. Con-  \nsequently, it is necessary to develop these models in retrospect.  \nThe digitization of existing high-rise buildings and infrastructure is particularly difficult, requiring the expertise of architects and engineers to reconstruct geometric and semantic information from various sources such as point clouds, images, and construction drawings [4] . While point clouds and images are valuable resources, they often rely on costly equipment and fail to reveal internal structural details. In contrast, construction drawings offer a more accessible and practical alternative, especially for older bridges where they are often the only available source of information. As a result, construction drawings are frequently the preferred choice for creating digital building models.  \nExtracting key information from bridge construction drawings, including section views, structural elements, and their identifiers, is a crucial initial step in developing accurate digital models. These drawings often feature critical internal components like anchors, tendons, and section views, which are essential for understanding bridges’ structural integrity and functionality.","cbCaioRoiKoNP8Aq","https://ap.wps.com/l/cbCaioRoiKoNP8Aq","pdf",3923311,"English","# Abstract\n# Keywords\n# 1 Introduction\n# 2 Related Work","[{\"question\":\"What problem does the paper address?\",\"answer\":\"It addresses the challenge of digitizing existing bridges when BIM models are missing, since manual extraction from 2D bridge drawings is slow and error-prone.\"},{\"question\":\"Which methods are used to extract structural information?\",\"answer\":\"The approach uses state-of-the-art object detection models (YOLOv8 and YOLOv11) to detect structural elements and section views, and OCR with Pytesseract to extract IDs for anchors and tendons.\"},{\"question\":\"What kinds of outputs does the method produce for BIM workflows?\",\"answer\":\"It produces extracted data that provides semantic information about key structural components, supporting the creation of high-quality digital bridge models and improving integration into BIM workflows.\"}]","Automated Extraction of Structural Elements and Section Views from Bridge Drawings Using Deep Learning - Paper | PDF",1792803650]