Automated generation of bim models from architectural and structural plans using artificial intelligence
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
UMT. Lahore
Abstract
This study introduces a new method that uses Artificial Intelligence (AI) and Machine
Learning (ML) models to automatically change old two-dimensional architectural and
structural plans into Building Information Modeling (BIM) systems. This method helps solve
the usual problems and delays found in manual BIM work, offering clear benefits to the
Architecture, Engineering, and Construction (AEC) industry. Significantly, the system
employs the Mask R-CNN deep learning model within the Detectron2 framework, trained on
a dataset comprising 552 annotated elements from real-world projects, to reliably identify and
distinguish primary building components including walls, columns, doors, and beams even
when analyzing diverse or low-quality plans. The data augmentation technique was used
successfully to make the model resistant to various drawing styles and trained on more than
200 iterations and achieved more than 95 percent accuracy in detection. Seen objects are
converted to the IFC standard directly, originating 3D BIM models that may immediately be
used in BIM programs. The automatized strategy significantly eliminates the amount of manual
work, improves the quality of modelling, and assists sustainable management since it allows
performing digital analysis of existing buildings. This study also points out to the scalability of
the solution to large-scale projects, collaboration of the team due to standardized models, and
usefulness in the energy efficiency planning and renovation planning. In conclusion, this
research shows that deep learning can effectively automate the BIM process, providing an
efficient and scalable way to convert old building records and support digital transformation in
construction. Additionally, this work contributes to Sustainable Development Goals 9
(Industry, Innovation, and Infrastructure) and 11 (Sustainable Cities and Communities),
highlighting its importance for sustainable growth.