VISION-LANGUAGE MODELS
Automating Building Code Compliance with VLM Agents
This paper introduces an innovative VLM agent designed to streamline building code compliance by combining advanced reasoning and action capabilities with specialized tools, leveraging a knowledge base of building codes, and employing RAG to identify relevant standards. The agent analyzes images and text to detect critical components, retrieve code references, and generate comprehensive reports.
Executive Impact & Key Metrics
The integration of Vision-Language Models (VLMs) offers a transformative approach to building code compliance. By automating the detailed analysis of building components against complex regulatory frameworks, this technology significantly reduces human error, enhances accuracy, and accelerates the inspection process. Our VLM agent achieved an average 96.25% similarity with human-created inspection reports, demonstrating its practical efficacy and potential for substantial operational improvements in smart building and smart city contexts.
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Average Similarity to Human Reports
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Deep Analysis & Enterprise Applications
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Methodology
Experimental Design
Key Findings
Our VLM agent system integrates image and text inputs, leveraging a knowledge base of building codes (IRC 2021, IPC 2021, Virginia Residential Code 2021) and Retrieval-Augmented Generation (RAG). Specialized tools, including web search and knowledge base retrieval, enhance its reasoning and action capabilities. The system analyzes inputs, retrieves relevant standards, and generates detailed compliance reports, ensuring accuracy and consistency.
The system was evaluated using 86 detailed home inspection reports from public sources, focusing on Virginia-specific regulations. Four distinct building components (kitchen sinks, vinyl siding, chimney crowns, and coils) served as multimodal inputs. Performance was measured by comparing system-generated reports to expert-provided analyses using GPT-40 to assess similarity.
The VLM agent achieved an average 96.25% similarity with human-created inspection reports. Visually distinct cases like vinyl siding and chimney crowns showed 100% similarity, while complex scenes such as HVAC coils (95%) and kitchen sinks (90%) demonstrated high, though slightly lower, accuracy due to overlapping plumbing and electrical issues. The system successfully identified components, cited relevant codes, and highlighted violations with corrective actions.
96.25%
Average Similarity with Human-Created Inspection Reports
Enterprise Process Flow
User Submits Image & Text Query
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VLM Agent Interprets Input
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Tool Invocation (RAG/Web Search)
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Retrieves Relevant Building Codes
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Systematically Compares Findings
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Generates Comprehensive Report
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Kitchen Sink Compliance Analysis
In a detailed analysis of a kitchen sink, the VLM agent successfully identified critical components such as the P-trap, waste disposer, and an electrical junction box. It retrieved specific code references (e.g., IPC P3201.4, NEC 314.29) and highlighted violations including a "double trap," an improper trap adapter coupling, a potentially missing trap vent, and an inaccessible electrical junction box. Corrective actions were proposed with step-by-step instructions.
90% Similarity with human-generated report for kitchen sink scene.
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Annual Savings
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Annual Hours Reclaimed
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Our AI Implementation Roadmap
We guide you through a structured, phase-by-phase implementation to ensure seamless integration and maximum value from your AI solutions.
Phase 1: Knowledge Base Expansion
Expand the current building code knowledge base to include additional codes like NEC (National Electrical Code) and IFGC (International Fuel Gas Code), enabling broader compliance checks across residential, commercial, and industrial settings.
Phase 2: Advanced Embedding & Reranking
Integrate more advanced embedding models and introduce reranking algorithms to improve the accuracy and relevance of information retrieved by the RAG component, ensuring precise code citations.
Phase 3: Multi-Agent Architecture
Evolve the VLM agent into a multi-agent framework where specialized agents handle different subtasks of compliance analysis, such as visual component identification, code retrieval, and report generation, enhancing efficiency and accuracy.
Phase 4: Human-in-the-Loop Integration
Develop an interactive system for human validation and correction of agent findings. Incorporate datasets annotated by certified inspectors for fine-tuning, allowing the agent to learn and adapt from expert feedback.
Phase 5: Scalable Deployment & Monitoring
Implement the VLM agent system with robust infrastructure for scalable deployment in diverse environments. Establish continuous monitoring and update mechanisms to ensure the system remains current with evolving building codes and best practices.
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