kth.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Experimental dataset to assess the structural performance of cracked reinforced concrete using Digital Image Correlation techniques with fixed and moving cameras
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Concrete Structures.ORCID iD: 0000-0001-8375-581X
Geodesy and Geomatics Division, Department of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, Rome, Italy.ORCID iD: 0000-0003-4765-0281
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Concrete Structures.ORCID iD: 0000-0003-1096-2177
Statik och Form AB, Stockholm, Sweden.ORCID iD: 0009-0004-6121-0965
2023 (English)In: Data in Brief, ISSN 2352-3409, Vol. 51, article id 109703Article in journal (Refereed) Published
Abstract [en]

The infrastructure is in many countries aging and continuous maintenance is required to ensure the safety of the structures. For concrete structures, cracks are a part of the structure's life cycle. However, assessing the structural impact of cracks in reinforced concrete is a complex task. The purpose of this paper is to present a dataset that can be used to verify and compare results of the measured crack propagation in concrete with the well-known Digital Image Correlation (DIC) technique and with Crack Monitoring from Motion (CMfM), a novel photogrammetric algorithm that enables high accurate measurements with a non-fixed camera. Moreover, the data can be used to investigate how existing cracks in reinforced concrete could be implemented in a numerical model.

The first potential area to use this dataset is structural engineering. The data can be used to verify non-linear material models used in a finite element (FE) software to simulate the structural response of reinforced concrete. In particular, the data can be used to investigate how existing cracks should be modelled in a FE model. The second potential area is within image processing techniques with a focus on DIC. Until recently, DIC suffered from one major disadvantage; the camera must be fixed during the entire period of data collection. Naturally, this decreases the flexibility and potential of using DIC outside the laboratory. In a recently published paper [1], an innovative photogrammetric algorithm (CMfM) that enables the use of a moving camera, i.e. a camera that is not fixed during data acquisition, was presented. The imagery of this dataset [2] was used to verify the potential of this algorithm and could be used to validate other approach for non-fixed cameras.

The dataset presented in this paper includes data collected from a three-point bending test performed in a laboratory environment on uncracked and pre-cracked reinforced concrete beams. Structural testing was performed using a displacement-controlled set-up, which continuously recorded the force and the vertical displacement of a centric-placed loading piston. First, the response of three uncracked beams was recorded. Thereafter, photos of the resulting cracks were taken, and a detailed mapping was presented. Material properties for the concrete, e.g., compressive strength, are presented together with testing of the tensile capacity of the reinforcement and a compressive test of the soft fiber boards used at the support to ensure good contact between steel and concrete. Then, the structural response of the pre-cracked beams was tested. During this test, four fixed cameras were used to monitor the crack propagation at different locations on the beam. Images are presented at the start of the load sequences and at pre-defined load stops during the testing. Hence, the crack opening captured in the images can be correlated to the force-displacement data. Moreover, a non-fixed camera was used to capture additional imagery at the location of each fixed camera.

Place, publisher, year, edition, pages
Elsevier BV , 2023. Vol. 51, article id 109703
Keywords [en]
Dataset for DIC and DIC-enhanced measurements of concrete cracks, Modelling of existing cracks in concrete, Dataset for material models of reinforced concrete, FE modelling of existing cracks, Structural assessment of cracked concrete
National Category
Infrastructure Engineering
Research subject
Civil and Architectural Engineering, Concrete Structures; Geodesy and Geoinformatics, Geoinformatics
Identifiers
URN: urn:nbn:se:kth:diva-338759DOI: 10.1016/j.dib.2023.109703ISI: 001105272000001Scopus ID: 2-s2.0-85175268040OAI: oai:DiVA.org:kth-338759DiVA, id: diva2:1808868
Projects
TACK - Tunnel Automatic CracK Detection
Funder
EU, Horizon 2020, 101012456Vinnova, InfraSweden 2030
Note

QC 20231101

Available from: 2023-11-01 Created: 2023-11-01 Last updated: 2023-12-11Bibliographically approved

Open Access in DiVA

fulltext(1644 kB)148 downloads
File information
File name FULLTEXT01.pdfFile size 1644 kBChecksum SHA-512
94614d7a07b6fc5000133670eff5f4465f45df32a0d06c5d59edbaf4f22f9303924a32a30b3af7987ee2f9e1529401222a15491b4a7a591c456e6fd195715559
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Sjölander, AndreasPeterson, Viktor

Search in DiVA

By author/editor
Sjölander, AndreasBelloni, ValeriaPeterson, ViktorLedin, Jonatan
By organisation
Concrete Structures
Infrastructure Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 148 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 480 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf