Collaboration between different stakeholders is a key part of project-based industries like construction. A variety of documents are required for effective communication and inconsistencies between them could lead to project delays and increased costs, necessitating early detection. Digital collaboration tools like building information modeling (BIM) have been proposed to address this challenge. BIM allows multiple designers to share and revise engineering drawings and documents, enabling early detection of inconsistencies. However, high initial costs, lack of skilled personnel and technical barriers limit its adoption, leaving many projects reliant on traditional, document-based collaboration.
When different stakeholders use different tools and document formats, information can be misinterpreted or inconsistently represented. This is particularly concerning for structural calculation documents and structural drawings. Such discrepancies can introduce human errors that, in severe cases, could compromise building safety. Preventing such errors, therefore, requires meticulous management.
To alleviate this burden, a research team led by Associate Professor Jaewook Jeong from the Department of Safety Engineering at Seoul National University of Science and Technology in South Korea, has now developed a new artificial intelligence (AI)-assisted automated pipeline for detecting human errors in structural design documents. "Our AI-assisted framework automatically extracts, organizes, and compares information from structural calculation documents and drawings to identify inconsistencies that may result from human error. Importantly, the system performs these checks without relying on Building Information Modeling (BIM), making it particularly relevant to construction projects that continue to use conventional design documents," explains Prof. Jeong. Their study was made available online on October 13, 2026, and published in Volume 185 of Engineering Applications of Artificial Intelligence on December 01, 2026.
The proposed automated pipeline starts with collection of project data and preprocessing, where necessary documents are acquired and low-quality information is pruned. It then extracts and standardizes structural information from calculation documents and drawings. For calculation documents, the DocLayNet-YOLO net model, a generalized document layout analysis model, first identifies regions containing target information. Next, key structural member information in the identified regions is extracted using the Surya-OCR model in text form. In the final step, this extracted information is merged and standardized into a single file.
For structural drawings, where multiple members are listed in schedules with varying row and column widths, member codes are identified first. OCR, together with Canny edge detection, Hough transform, and morphological operations are employed to locate table regions containing these member codes. The extracted information in these member code regions is then filtered and refined, followed by standardization.
The pipeline then validates the extracted information and compares data from the two document types. Member codes and direction are used as key identifiers to establish correspondence between structural calculation and drawing documents. The system detects errors, identifies and reports duplicate entries, organizing the results into a consistent dataset. The errors identified are then categorized. The researchers also developed a user-friendly, web-based application to deploy this pipeline.
In a case study involving a residential construction project in South Korea, the proposed system achieved information extraction accuracies of 95.56% for structural calculation documents and 90.47% for structural drawing documents. The consistency verification process identified 253 discrepancies, including 224 missing entries, 15 main reinforcement mismatches, three stirrup mismatches, eight dimensional discrepancies, and three duplicate entries. These findings demonstrate the framework's potential to support more systematic and efficient structural design document verification.
"Our AI-assisted pipeline could help structural engineers, design consultants, contractors, and construction supervision teams to identify discrepancies before they lead to construction errors, improving reliability of buildings and infrastructure," remarks Prof. Jeong. "It could be particularly valuable for small- and medium-sized construction projects that cannot implement BIM."
Overall, this innovative AI framework could help reduce the burden of manual checking and improve consistency in construction projects where BIM cannot be implemented. In the future, it could become integrated into routine engineering workflows, making advanced safety technologies more accessible.
Reference
Title of original paper: Detection of human errors in construction structural design documents using automated extraction algorithm
Journal: Engineering Applications of Artificial Intelligence
DOI: https://doi.org/10.1016/j.engappai.2026.116556
About the institute Seoul National University of Science and Technology (SEOULTECH)
Seoul National University of Science and Technology, commonly known as 'SEOULTECH,' is a national university located in Nowon-gu, Seoul, South Korea. Founded in April 1910, around the time of the establishment of the Republic of Korea, SEOULTECH has grown into a large and comprehensive university with a campus size of 504,922 m2.
It comprises 10 undergraduate schools, 35 departments, 6 graduate schools, and has an enrollment of approximately 14,595 students.
Website: https://en.seoultech.ac.kr/
About the author
Jaewook Jeong is an Associate Professor in the Department of Safety Engineering at Seoul National University of Science and Technology, South Korea, and a certified Professional Engineer (P.E.). He leads the S3PARC Lab (Safety, Smart, Sustainability, Proactive and Advanced Research in Construction) and teaches construction safety and management. He received his Ph.D. from Yonsei University, South Korea. With 17 years of professional and research experience across South Korea, the United States, and Singapore, his research focuses on quantitative construction accident analysis, artificial intelligence applications, and the advancement of proactive construction safety management systems.
