{
  "abstract": "This study proposes an LLM/VLM-orchestrated multi-agent framework for multi-camera vision-based Structural Health Monitoring (SHM) of historic masonry minarets. The main novelty of the framework is its region-aware and auditable decision-support strategy: camera-derived displacement anomalies, degradation in inter-sensor relationships, and VLM-based visual observations are preserved as traceable evidence streams and integrated at the decision layer rather than being merged through opaque feature-level fusion. The framework was experimentally validated on a scaled masonry minaret subjected to controlled shaking-table excitation. Multi-camera optical-flow tracking provided displacement time series for global and relation-based anomaly analysis, while region-specific inspection images were interpreted by a VLM as qualitative visual evidence. The global reconstruction-error pathway showed more frequent and persistent anomaly behavior during the damage-candidate phases compared with the reference condition. The relation-based pathway identified non-uniform degradation in inter-sensor consistency, with the strongest localization cue associated with the sensor region corresponding to the experimentally damaged area. Most importantly, the fused region-level risk map showed qualitative spatial agreement with the damage observed on the minaret model after the shaking table tests. These findings indicate that the proposed multi-agent framework can transform heterogeneous SHM evidence into interpretable regional risk priorities and reviewable reporting outputs for historic masonry structures, while maintaining a conservative expert-in-the-loop interpretation strategy.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Civil Engineering, Faculty of Engineering, Karadeniz Technical University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Kemal Hacıefendioğlu"
    },
    {
      "affs": [
        0
      ],
      "name": "Tunahan Aslan"
    },
    {
      "affs": [
        0
      ],
      "name": "Hasan Basri Başağa"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bg6p7e866n9hd.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-61030-7",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-61030-7",
  "id": "bg6p7e866n9hd",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-61030-7",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "2205|Structural Health Monitoring Techniques"
    ]
  ],
  "openalex": "W7169869773",
  "pdf_url": "https://www.nature.com/articles/s41598-026-61030-7_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/bg6p7e866n9hd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:50Z",
      "native_id": "ffb4c21041104ddd8c6856f3161e6a57",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-61030-7"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Multi-camera vision-based structural health monitoring of historic masonry minarets with LLM/VLM-assisted damage interpretation",
  "title_text": "Multi-camera vision-based structural health monitoring of historic masonry minarets with LLM/VLM-assisted damage interpretation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:50Z",
  "url": "https://research.draconitic.com/paper/bg6p7e866n9hd",
  "venue": "Scientific Reports",
  "versions": []
}