Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Deep learning metasensor for crack-width assessment and self-healing evaluation in concrete
https://orcid.org/0000-0002-4736-4763
https://orcid.org/0000-0002-7661-9833
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.
Highlights Proposed deep CNN metasensor for crack width assessment from high-resolution images. Trained metasensor using considerable set of manual reference measurements. Achieved semi-automatic, multi-position and multi-stage self-healing evaluation. Shown differences in healing of exposed and deeper materials, explained by porosity. Demonstrated effective self-healing in mature high-strength concrete.
Deep learning metasensor for crack-width assessment and self-healing evaluation in concrete
https://orcid.org/0000-0002-4736-4763
https://orcid.org/0000-0002-7661-9833
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.
Highlights Proposed deep CNN metasensor for crack width assessment from high-resolution images. Trained metasensor using considerable set of manual reference measurements. Achieved semi-automatic, multi-position and multi-stage self-healing evaluation. Shown differences in healing of exposed and deeper materials, explained by porosity. Demonstrated effective self-healing in mature high-strength concrete.
Deep learning metasensor for crack-width assessment and self-healing evaluation in concrete
https://orcid.org/0000-0002-4736-4763
https://orcid.org/0000-0002-7661-9833
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.
Highlights Proposed deep CNN metasensor for crack width assessment from high-resolution images. Trained metasensor using considerable set of manual reference measurements. Achieved semi-automatic, multi-position and multi-stage self-healing evaluation. Shown differences in healing of exposed and deeper materials, explained by porosity. Demonstrated effective self-healing in mature high-strength concrete.
Deep learning metasensor for crack-width assessment and self-healing evaluation in concrete
Jakubowski, Jacek (Autor:in) / Tomczak, Kamil (Autor:in)
06.03.2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Permeability and self-healing of cracked concrete as a function of temperature and crack width
| British Library Online Contents | 2003