NEXT-GEN ROOFING SOLUTIONS: SMART ASSEMBLY RECOMMENDER FOR ROOFNAV IN COMMERCIAL PROJECTS
DOI:
https://doi.org/10.15662/IJRAI.2025.0803003Keywords:
Roofing Design, Assembly Recommender, RoofNav, BIM Integration, Machine Learning, Code Compliance, Construction TechnologyAbstract
Commercial roofing is going through a sea change enabled by data analytics, artificial intelligence (AI) and decision support systems. Developed by FM Approvals, the powerful RoofNav is a comprehensive yet user-friendly tool enabling you to design class 1-90 code compliant roofing systems. However, the manual method in RoofNav is complicated and time-consuming. In this paper the Smart Assembly Recommender (SAR) is introduced as a new AI-enabled system that is an extension of the RoofNav platform for commercial roofing specification. The key goal of SAR is the simplification of selecting assemblies, a process that results in code-compliant designs and a contract bid faster than ever by delivering intelligent recommendations directly within industry standard workflows. Great Expectations was built with three deployment options: a browser-based notebook interface, a BIM plugin for Autodesk Revit and a RESTful API for enterprise integration. Quantitative assessments indicate that the browser interface reduced the design time by around 30%, and the Revit plugin increased the design accuracy by ˇ25% by performing automatic compliance checks. The RESTful API was enterprise-ready and provided an average response time as low as 150 milliseconds for high concurrent usage. Underneath it all, the machine-learning engine trained on historical RoofNav data was delivering 91 percent accuracy in its assembly matchup, and it was bringing down non-compliant suggestion by 20 percent against manual approaches. The SAR utilizes the data driven tools and AI incorporation to progress development technology in the market place, creating faster, smarter and more dependable roofing solutions for commercial installation.
References
1. Kenan Liu, Alice Chang-Richards, Seosamh B. Costello, Cécile L’Hermitte, and Nan Li. What factors influence building material supply for post-disaster reconstruction and recovery? A systematic review applying systems thinking. International Journal of Disaster Risk Reduction, 114:104981, November 2024.
2. Patrick Aravena Pelizari, Christian Geiß, Paula Aguirre, Hernán Santa María, Yvonne Merino Peña, and Hannes Taubenböck. Automated building characterization for seismic risk assessment using street-level imagery and deep learning. ISPRS Journal of Photogrammetry and Remote Sensing, 180:370–386, October 2021.
3. Daniela Gonzalez, Diego Rueda-Plata, Ana B. Acevedo, Juan C. Duque, Raúl Ramos Pollán, Alejandro Betancourt, and Sebastian García. Automatic detection of building typology using deep learning methods on street level images. Building and Environment, 177:106805, June 2020.
4. Surya Prasath Ramalingam and Vaibhav Kumar. Building usage prediction in complex urban scenes by fusing text and facade features from street view images using deep learning. Building and Environment, 267:112174, January 2025.
5. Lingwen Sun. Study on the Cultural Impact of Local Materials in Regional Architectural Design. Communications in Humanities Research, 45:9–14, August 2024.
6. Y. Law and R. Miura, "RoofNet: A Global Multimodal Dataset for Roof Material Classification," arXiv preprint arXiv:2505.19358, May 2025. [Online]. Available: https://arxiv.org/abs/2505.19358
7. Y. Meng, M. Yu, and J. Li, "Automatic Roof Type Classification Through Machine Learning for Regional Wind Risk Assessment," arXiv preprint arXiv:2305.17315, May 2023. [Online]. Available: https://arxiv.org/abs/2305.17315
8. J. Ren, M. Ikehata, and K. Ikeuchi, "Intuitive and Efficient Roof Modeling for Reconstruction and Synthesis," arXiv preprint arXiv:2109.07683, Sep. 2021. [Online]. Available: https://arxiv.org/abs/2109.07683
9. Mindfire Solutions, "AI Roof Visualization Tool for Construction," [Online]. Available: https://www.mindfiresolutions.com/expertise/ai-roof-visualization-tool-for construction/
10. FieldCamp, "Roofing Business Software for Contractors," [Online]. Available: https://fieldcamp.ai/industries/roofing/
11. L. Berens, "AI is Changing How to Manage Roofing Crews Safely," Roofing Contractor, Dec. 2023. [Online]. Available: https://www.roofingcontractor.com/articles/99828-ai-is-changing-how-to-manage roofing-crews-safely
12. New Tech Machinery, "The Impact of AI on the Metal Roofing Industry," [Online]. Available: https://newtechmachinery.com/learning-center/the-impact-of-ai-on-the metal-roofing-industry/
13. Achievion Solutions, "Roofing Marketplace Powered by AI," [Online]. Available: https://achievion.com/portfolio/roof-size-calculation-ai.html
14. Castagra, "Artificial Intelligence for Roofing Estimators," [Online]. Available: https://www.castagra.com/blog/artificial-intelligence-for-roofing-estimators/
15. Solea AI, "AI Phone Assistant for Roofing Businesses," [Online]. Available: https://www.solea.ai/industry/roofing
16. Beam AI, "Automate Your Roofing Takeoffs with Beam AI," [Online]. Available: https://www.ibeam.ai/blog/automate-your-roofing-takeoffs-with-beam-ai
17. J. Romano, M. Tesch, B. V. Barragan, and H. Lipson, "Nailed It: Autonomous Roofing with a Nailgun-Equipped Octocopter," arXiv preprint arXiv:1909.08162, Sep. 2019. [Online]. Available: https://arxiv.org/abs/1909.08162
