AI-Powered BIM Clash Detection System
Orchestrates 7 specialized AI agents to parse IFC models, detect geometric intersections with millimeter precision, validate against building codes (Bouwbesluit, NEN standards, Eurocodes), analyze safety implications, generate resolution options with cost-benefit analysis, and produce stakeholder-ready reports with 98.9% false positive filtering accuracy.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Input Configuration for BIM model upload, project parameters, and AI agent settings with chain-of-thought reasoning enabled
AI Agent Execution showing real-time multi-agent clash detection with spatial indexing, IFC parsing, and analysis progress metrics
Detailed Clash Analysis with severity categorization, root cause identification, and cost impact comparison for design vs construction resolution
Final Output summary displaying €2.9M value delivered, 847% ROI, clash detection metrics, and AI performance statistics
AI Agents
Specialized autonomous agents working in coordination
BIM Orchestrator Agent
BIM clash detection involves multiple sequential and parallel analysis phases requiring careful coordination to ensure comprehensive coverage without redundant processing.
Core Logic
Uses Claude 3 Opus to coordinate the 9-phase analysis workflow: agent initialization, BIM parsing, spatial indexing, geometric clash detection, regulatory compliance checking, safety analysis, false positive filtering, resolution generation, and report compilation. Manages inter-agent communication, task delegation, and quality assurance gates.
BIM Parser Agent
IFC models contain complex hierarchical data structures with thousands of elements across multiple disciplines that must be extracted and classified for downstream analysis.
Core Logic
Employs GPT-4 Turbo with specialized parse_ifc tooling to extract geometry, element metadata, spatial relationships, and discipline classifications from IFC files. Processes 12K-45K elements per model, builds spatial indices for efficient intersection queries, and normalizes data across different IFC schema versions.
Clash Detection Agent
Raw geometric intersection detection produces numerous false positives from intentional design overlaps, tolerance issues, and modeling artifacts that obscure genuine conflicts.
Core Logic
Uses Claude 3 Sonnet with detect_intersections tooling to identify geometric clashes with configurable tolerance thresholds (millimeter precision). Applies clearance validation rules, filters false positives using contextual analysis (achieving 72.8% false positive rate reduction), and categorizes clashes by type (Hard, Soft, Clearance, Regulatory, Safety).
Compliance Validation Agent
Building designs must comply with complex regulatory frameworks including Bouwbesluit 2012, NEN electrical standards, Eurocodes, and Omgevingswet requirements that vary by project type and location.
Core Logic
Employs GPT-4 Turbo with check_building_code tooling to validate designs against Dutch building codes, international standards, and permit requirements. Identifies regulatory violations with specific article citations, evaluates permit-blocking issues, and flags environmental compliance gaps requiring resolution before submission.
Safety Analysis Agent
Life safety issues including fire egress obstructions, accessibility violations, and emergency egress deficiencies represent the highest-priority clashes requiring immediate attention.
Core Logic
Uses Claude 3 Opus to perform fire safety analysis (egress width validation per Bouwbesluit Article 6.24), accessibility compliance checking, and emergency egress validation. Assigns risk scores (1-100) to safety-related clashes and identifies permit-blocking violations requiring immediate resolution.
Resolution Generator Agent
Identified clashes require practical resolution options with cost-benefit analysis, feasibility assessment, and implementation guidance—traditionally requiring senior engineering judgment.
Core Logic
Employs Claude 3 Opus to generate multiple resolution options per clash with feasibility ratings (High/Medium/Low), cost estimates (current vs. construction-phase), schedule impact in days, and implementation steps. Provides AI reasoning with confidence scores (45-96%) and explicit recommendation flags based on optimal cost-schedule-risk tradeoffs.
Report Compiler Agent
Clash detection findings must be communicated to diverse stakeholders (engineers, managers, clients) in appropriate formats with varying levels of technical detail.
Core Logic
Uses GPT-4 Turbo to generate multi-format outputs: BCF for BIM coordination workflows, PDF for detailed technical reports, XLSX for clash tracking spreadsheets, and JSON for system integration. Produces executive summaries with key metrics and stakeholder-specific visualizations.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
7 technologies
Architecture Diagram
System flow visualization