{
  "abstract": "Historic masonry structures require monitoring workflows that are non-destructive, interpretable, and robust to field variability. This paper presents a proof-of-concept, RAG-enhanced structural health monitoring (SHM) workflow demonstrated on a single historic masonry minaret case study, in which a manager–orchestrator coordinates signal analysis, visual analysis, and cross-modal fusion. Vibration data from four sensors installed on this minaret are processed through a reproducible pipeline that includes data cleaning, PSD-based baseline characterisation, adaptive windowing, robust normalisation, and anomaly screening with a 1D convolutional autoencoder trained on healthy reference data (Measurements 1–12), followed by evaluation on Measurements 13–20. Localisation is strengthened through relation-based autoencoders on sensor pairs and sensor-to-zone damage mapping. In parallel, a VLM analyses sensor-linked inspection images from the same case study, while a curated retrieval-augmented structural memory package for the monitored minaret (geometry, materials, baseline dynamics, FE notes, and inspection playbook) supports context-aware interpretation. A three-agent decision layer (Agent A: LLM+RAG, Agent B: VLM+RAG, Agent C: fusion + RAG) reconciles agreements and conflicts between signal- and image-derived evidence to produce case-specific global and zone-level risk assessments. The study compares with-RAG and without-RAG analysis modes within this single deployment and reports structured, traceable outputs, including JSON artefacts, per-image findings, per-sensor summaries, narrative reports, and final risk maps. Its main contribution is to demonstrate how signal-derived anomalies, image-derived evidence, and retrieval-grounded structural memory can be combined within a traceable multi-agent SHM workflow for one monitored historic masonry minaret.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Kemal Hacıefendioğlu"
    },
    {
      "name": "Volkan Kahya"
    },
    {
      "name": "Tunahan Aslan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3cj03q1txmjkt.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-57424-2",
  "fulltext_url": null,
  "id": "3cj03q1txmjkt",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-57424-2",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2205|Structural Health Monitoring Techniques"
    ]
  ],
  "openalex": "W7222006849",
  "pdf_url": "https://www.nature.com/articles/s41598-026-57424-2.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/3cj03q1txmjkt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:28Z",
      "native_id": "10.1038/s41598-026-57424-2",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-57424-2"
    }
  ],
  "subjects": [],
  "title": "A RAG-enhanced multi-agent LLM/VLM framework for auditable and region-aware structural health monitoring of a historic masonry structure",
  "title_text": "A RAG-enhanced multi-agent LLM/VLM framework for auditable and region-aware structural health monitoring of a historic masonry structure",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:28Z",
  "url": "https://research.draconitic.com/paper/3cj03q1txmjkt",
  "venue": "Scientific Reports",
  "versions": []
}