Szczegóły publikacji
Opis bibliograficzny
Deep learning metasensor for crack-width assessment and self-healing evaluation in concrete / Jacek JAKUBOWSKI, Kamil TOMCZAK // Construction and Building Materials ; ISSN 0950-0618. — 2024 — vol. 422 art. no. 135768, s. 1–17. — Bibliogr. s. 16–17, Abstr. — Publikacja dostępna online od: 2024-03-13
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 152375 |
|---|---|
| Data dodania do BaDAP | 2024-04-02 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.conbuildmat.2024.135768 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Construction and Building Materials |
Abstract
This study presents a deep convolutional neural network metasensor for measuring crack width from high-resolution images and brightness profiles. Procedures of training, testing, data drift evaluation and fine-tuning were proposed. The metasensor enables repeated, semi-automated crack measurements in the same set of multiple locations over subsequent multi-stage observations, a unique and important feature for accurately assessing the progress of self-healing. It was employed to investigate the autogenous self-healing of 75-month-old high-strength concrete samples extracted from a structural member. The crack-width measurements, aided by X-ray computed tomography and electron microscopy data, helped to identify differences between the self-healing of externally exposed and deep-lying materials as a result of the natural local porosity differentiation.
