Academic Journal
An Agentic AI System for Roof Design Compliance Using Computer Vision, Retrieval-Augmented Generation and Large Language Models.
| Title: | An Agentic AI System for Roof Design Compliance Using Computer Vision, Retrieval-Augmented Generation and Large Language Models. |
|---|---|
| Authors: | Nawari, Nawari O., Lawal, Oluwatoyin O. |
| Source: | Buildings (2075-5309); Jul2026, Vol. 16 Issue 13, p2637, 34p |
| Subject Terms: | Computer vision, Language models, Intelligent agents, Engineering standards, Natural language processing |
| Geographic Terms: | Florida |
| Abstract: | Designers, engineers, and building officials face increasing pressure to accelerate and improve the accuracy of design review for buildings and infrastructure. Roof assemblies and rooftop structures are particularly challenging due to the complexity and fragmentation of regulatory requirements, especially in jurisdictions such as Florida, where compliance must be verified across both the residential and commercial volumes of the Florida Building Code (FBC). The resulting review process is technically demanding and time-intensive, imposing significant cognitive and operational burdens on practitioners and under-resourced public agencies. To address these challenges, this study proposes and evaluates an agentic artificial intelligence (AI) framework for automated code compliance checking of roof assemblies and rooftop structures. The framework employs a multi-agent architecture in which specialized AI agents collaboratively interpret regulatory provisions and evaluate roof design parameters across four core modules: data preprocessing and code ingestion, rule-based and semantic analysis, results visualization, and iterative validation. YOLO11m-seg and Mask R-CNN were used for element detection and segmentation, and the system was developed using 150 design projects, including roof plans, section details, and specifications. Four large language models from two families (Mistral and GPT) were comparatively evaluated on standardized compliance tasks. The framework was then tested on a held-out portfolio of 15 distinct roof-design projects comprising 60 code-compliance decisions derived from the FBC 2023, with performance measured by precision, recall, F1-score, and accuracy. GPT-5.4 achieved the highest overall performance (F1 = 0.97; accuracy = 97%). Because the reasoning and vision components were evaluated separately rather than as an integrated end-to-end pipeline, and the scope was limited to one jurisdiction and two drawing types, broader code coverage and production-setting validation are needed before claims of generality. Nonetheless, the results suggest that agentic AI can meaningfully support compliance review and reduce reviewer burden in roof-design permitting. [ABSTRACT FROM AUTHOR] |
| Copyright of Buildings (2075-5309) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: An Agentic AI System for Roof Design Compliance Using Computer Vision, Retrieval-Augmented Generation and Large Language Models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nawari%2C+Nawari+O%2E%22">Nawari, Nawari O.</searchLink><br /><searchLink fieldCode="AR" term="%22Lawal%2C+Oluwatoyin+O%2E%22">Lawal, Oluwatoyin O.</searchLink> – Name: TitleSource Label: Source Group: Src Data: Buildings (2075-5309); Jul2026, Vol. 16 Issue 13, p2637, 34p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+agents%22">Intelligent agents</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+standards%22">Engineering standards</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Florida%22">Florida</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Designers, engineers, and building officials face increasing pressure to accelerate and improve the accuracy of design review for buildings and infrastructure. Roof assemblies and rooftop structures are particularly challenging due to the complexity and fragmentation of regulatory requirements, especially in jurisdictions such as Florida, where compliance must be verified across both the residential and commercial volumes of the Florida Building Code (FBC). The resulting review process is technically demanding and time-intensive, imposing significant cognitive and operational burdens on practitioners and under-resourced public agencies. To address these challenges, this study proposes and evaluates an agentic artificial intelligence (AI) framework for automated code compliance checking of roof assemblies and rooftop structures. The framework employs a multi-agent architecture in which specialized AI agents collaboratively interpret regulatory provisions and evaluate roof design parameters across four core modules: data preprocessing and code ingestion, rule-based and semantic analysis, results visualization, and iterative validation. YOLO11m-seg and Mask R-CNN were used for element detection and segmentation, and the system was developed using 150 design projects, including roof plans, section details, and specifications. Four large language models from two families (Mistral and GPT) were comparatively evaluated on standardized compliance tasks. The framework was then tested on a held-out portfolio of 15 distinct roof-design projects comprising 60 code-compliance decisions derived from the FBC 2023, with performance measured by precision, recall, F1-score, and accuracy. GPT-5.4 achieved the highest overall performance (F1 = 0.97; accuracy = 97%). Because the reasoning and vision components were evaluated separately rather than as an integrated end-to-end pipeline, and the scope was limited to one jurisdiction and two drawing types, broader code coverage and production-setting validation are needed before claims of generality. Nonetheless, the results suggest that agentic AI can meaningfully support compliance review and reduce reviewer burden in roof-design permitting. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Buildings (2075-5309) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/buildings16132637 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 34 StartPage: 2637 Subjects: – SubjectFull: Florida Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Language models Type: general – SubjectFull: Intelligent agents Type: general – SubjectFull: Engineering standards Type: general – SubjectFull: Natural language processing Type: general Titles: – TitleFull: An Agentic AI System for Roof Design Compliance Using Computer Vision, Retrieval-Augmented Generation and Large Language Models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nawari, Nawari O. – PersonEntity: Name: NameFull: Lawal, Oluwatoyin O. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20755309 Numbering: – Type: volume Value: 16 – Type: issue Value: 13 Titles: – TitleFull: Buildings (2075-5309) Type: main |
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