{
  "abstract": "Deep-learning semantic segmentation and morphological analysis offer a non-contact route to characterizing microcracks in cementitious composites, yet linking pixel-level masks to physical geometry and tensile response remains challenging. This study establishes an integrated lightweight segmentation and geometric-quantification workflow for rubberized engineered cementitious composites (ECCs) incorporating 0–50% powdered rubber. We propose EfficientSegLite, a supervised binary segmentation architecture combining a hierarchical SegFormer-B0 encoder, a channel-compressed U-Net-style decoder, lightweight atrous spatial pyramid pooling, and depthwise separable convolutions, trained via a two-stage Dice–OHEM–Edge strategy. On 1440 ECC tensile images, it achieved 93.40% mIoU and 93.24% F1 score with only 3.96 M parameters and 734.9 M FLOPs. Deterministic morphological and distance-transform algorithms then convert the masks into calibrated physical descriptors, achieving a mean relative crack-width error of 3.65% against optical microscopy across eight benchmark cracks. A crack-area-based visual equivalent strain (ε_v) correlated strongly with extensometer strain (Pearson’s r > 0.97, R2 > 0.94); however, regression slopes of 0.368–1.046 and uncalibrated mean absolute errors of 0.03–2.04 percentage points indicate that quantitative strain estimation requires mixture-specific calibration. A tool-calling large language model agent parses user instructions and orchestrates the deterministic computation tools, generating traceable inspection reports.",
  "abstract_shown": true,
  "affiliations": [
    "Guizhou Power Grid Co., Ltd., Guiyang 550029, China",
    "School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Junqing Jia",
      "orcid": "0009-0003-3741-096X"
    },
    {
      "affs": [
        0
      ],
      "name": "Tengxiang Su"
    },
    {
      "affs": [
        1
      ],
      "name": "Jinghao Huang"
    },
    {
      "affs": [
        1
      ],
      "name": "Ge Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Jiaxiang Lin",
      "orcid": "0000-0001-7627-1948"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bm7jeya75gybq.bib",
  "cited_by": 0,
  "doi": "10.3390/polym18212657",
  "fulltext_url": null,
  "id": "bm7jeya75gybq",
  "issns": [
    "2073-4360"
  ],
  "landing_url": "https://www.mdpi.com/2073-4360/18/21/2657",
  "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|Infrastructure Maintenance and Monitoring"
    ]
  ],
  "openalex": "W7223933259",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/bm7jeya75gybq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:44Z",
      "native_id": "10.3390/polym18212657",
      "source": "crossref",
      "url": "https://www.mdpi.com/2073-4360/18/21/2657"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:2073-4360",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Integrating Transformer Segmentation and Morphological Analysis for Multi-Crack Characterization and Visual Equivalent Strain Assessment in Rubberized ECC",
  "title_text": "Integrating Transformer Segmentation and Morphological Analysis for Multi-Crack Characterization and Visual Equivalent Strain Assessment in Rubberized ECC",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:44Z",
  "url": "https://research.draconitic.com/paper/bm7jeya75gybq",
  "venue": "Polymers",
  "versions": []
}