# StructureMind

- **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon)
- **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST)
- **Where:** Online
- **Team:** [Viraj Anchan](https://cerebralvalley.ai/u/virajanchan)
- **GitHub:** https://bitbucket.org/viraj07/structure_mind/src/main/
- **Demo video:** https://www.youtube.com/watch?v=074Q9ptebi4
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/279

StructureMind is an AI-powered structural inspection and evidence-management platform designed to help engineers organize building records, review inspection photographs, identify visible defects, and conduct traceable preliminary investigations.

The platform provides an end-to-end workflow. Users can register buildings and structural components, upload component-linked inspection evidence, and generate clearly labelled fictional data through a Synthetic Inspection Lab. The lab uses Muse Image 1.0 to create synthetic inspection photographs for demonstrations and exploratory testing.

Uploaded evidence is analyzed using Muse Spark 1.3, which describes visible surface anomalies and selects appropriate segmentation targets. SAM 3.1, Meta’s Segment Anything Model, then isolates defects such as cracks into independently reviewable masks. Each mask has its own confidence score, evidence reference, overlay, and human Accept or Reject controls.

After evidence review, an agentic investigation powered by Muse Spark 1.3 retrieves verified application data through controlled tools. This includes building details, inspection history, floor-level defects, adjacent components, and available test results. The investigation separates recorded observations from hypotheses, identifies contradictory and missing evidence, recommends follow-up actions, and produces a human-readable report and downloadable PDF.

StructureMind is built as a decision-support and evidence-traceability platform, not an automated safety-certification system. Synthetic inputs and AI-generated observations are explicitly labelled, measurements are never invented, and final conclusions remain subject to review by qualified structural engineers.

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