Strain patterns of short span reinforced concrete beams under flexural loading: A comparison between distributed sensing and concrete damaged plasticity modelling

Author:

Jayawickrema Minol123ORCID,Kumar Arjun1,Herath Madhubhashitha13ORCID,Hettiarachchi Nandita4,Sooriyaarachchi Harsha5ORCID,Epaarachchi Jayantha12

Affiliation:

1. Centre for Future Materials, University of Southern Queensland, Toowoomba, QLD, Australia

2. School of Mechanical and Electrical Engineering Faculty of Health, Engineering and Sciences University of Southern Queensland, Toowoomba, QLD, Australia

3. Department of Engineering Technology, Faculty of Technological Studies, Uva Wellassa University, Badulla, Sri Lanka

4. Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, University of Ruhuna, Hapugala, Galle, Sri Lanka

5. Department of Civil and Environmental Engineering, Faculty of Engineering, University of Ruhuna, Hapugala, Galle, Sri Lanka

Abstract

Strains beyond the elastic limit of concrete cause cracks in concrete structures. This study analysed distributed optical fibre sensor (DOFS) network strain data to identify excessive strain readings, locations and strain patterns of four reinforced concrete (RC) beams. The strain variation inside the RC beams was investigated using a concrete damaged plasticity (CDP) based finite element analysis (FEA) model. The measured bottom surface and bottom rebar strain were considered for investigation. A three-point bending arrangement was used for the loading, and an optical backscatter reflectometer was used to measure the strain. By visual inspections, it has been found that the onset of hairline cracks at the locations of the higher strain readings. The maximum load applied was 16 kN and has not shown any yielding in strain reading acquired from bottom rebars. FEA results showed admissible agreement with the measured lower surface strain and lower rebar strain. Therefore, when developing a robust SHM system, a CDP-based FEA simulation is a valuable tool for data mining of strain data. Furthermore, the strain data extracted from CDP models can be used to train artificial intelligence based SHM models.

Publisher

SAGE Publications

Subject

Mechanical Engineering,General Materials Science

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