Title: Characterisation of a vascular self-healing cementitious material system - data


Citation
Selvarajoo T, Davies RE, Gardner DR, et al. (2020). Characterisation of a vascular self-healing cementitious material system - data. Cardiff University. http://doi.org/10.17035/d.2019.0081928129



This data is not currently available because: Intent to publish project results

Access Method: Click to email a request for this data to opendata@cardiff.ac.uk


Dataset Details

Publisher: Cardiff University

Date (year) of data becoming publicly available: 2020

Data format: .xlsx

Estimated total storage size of dataset: Less than 1 gigabyte

Number of Files In Dataset: 15

DOI : 10.17035/d.2019.0081928129

DOI URL: http://doi.org/10.17035/d.2019.0081928129

Related URL: http://rm4l.com/


Description

Self-healing cementitious materials have received significant attention from researchers in recent years. The motivation for this research effort arises from the durability problems, often resulting from concrete cracking, associated with concrete structures. This dataset contains the results of an experimental programme of work, undertaken to characterise the behaviour of a self-healing cementitious material system. A ‘model’ material system is selected comprising concrete with embedded channels filled with cyanoacrylate (CA). The programme considered both the mechanical damage-healing behaviour and the flow and curing aspects of the model material. The dataset contains 15 Excel files, which contain the measured experimental data, as well as the results of theoretical model predictions. The files correspond to: i) direct tension tests on notched concrete cubes, ii) flexural tests on notched prismatic concrete beams, iii) flexural tests on notched beams with offset reinforcement, iv) capillary flow tests of CA in a static natural crack, v) sorption tests of CA through cracks surface into concrete specimen, vi) curing front tests of CA adjacent to a concrete substrate and vii) dynamic contact angle tests of CA in capillary channels.

Research results based upon these data are published at https://doi.org/10.1016/j.conbuildmat.2020.119245


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Last updated on 2021-23-04 at 11:15