Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
Document Type
Event
Start Date
22-4-2026 4:00 PM
Description
This study addresses the high hallucination rates of Vision-Language Models (LLMs) when analyzing complex, hybrid construction blueprints. We developed a dual-input pipeline that pairs high-resolution images with a four-layer JSON "Digital Twin" (vector text, raster OCR, geometry) to mathematically ground the LLM's visual interpretation. Key Engineering Achievement: We processed a massive 137-sheet civil engineering project with zero errors. By introducing a multi-tier JSON pruning strategy, we cut token usage by up to 70% and processed the entire batch from $13-$15 to just $0.93. Decision Support Extension: We integrated real-time traffic data (TomTom) and GDOT procedural policies to transform the pipeline from a simple document reader into a strategic project management advisor.
Included in
GRM-095-230 A Multimodal LLM Framework for Automated Construction Blueprint Analysis with Real-Time Decision Support
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
This study addresses the high hallucination rates of Vision-Language Models (LLMs) when analyzing complex, hybrid construction blueprints. We developed a dual-input pipeline that pairs high-resolution images with a four-layer JSON "Digital Twin" (vector text, raster OCR, geometry) to mathematically ground the LLM's visual interpretation. Key Engineering Achievement: We processed a massive 137-sheet civil engineering project with zero errors. By introducing a multi-tier JSON pruning strategy, we cut token usage by up to 70% and processed the entire batch from $13-$15 to just $0.93. Decision Support Extension: We integrated real-time traffic data (TomTom) and GDOT procedural policies to transform the pipeline from a simple document reader into a strategic project management advisor.