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Self-Healing Capacity of Asphalt Mixtures Including By-Products Both as Aggregates and Heating Inductors

Vila-Cortavitarte, Marta, Jato-Espino, Daniel, Castro-Fresno, Daniel, Calzada-Pérez, Miguel Á.·2018·Materials
DOI 10.3390/ma11050800PMC PMC5978177
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Abstract

Major advances have been achieved in the field of self-healing by magnetic induction in which the addition of metallic particles into asphalt mixtures enables repairing their own cracks. This technology has already been proven to increase the life expectancy of roads. Nevertheless, its higher costs in comparison with conventional maintenance caused by the price of virgin metallic particles still makes it unattractive for investment. This research aimed at making this process economically accessible as well as environmentally efficient. To this end, an intense search for suitable industrial by-products to substitute both the virgin metal particles and the natural aggregates forming asphalt mixtures was conducted. The set of by-products used included sand blasting wastes, stainless shot wastes, and polished wastes as metallic particles and other inert by-products as aggregates. The results demonstrated that the by-products were adequately heated, which leads to satisfactory healing ratios in comparison with the reference mixture.

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Figures

Figure 1 Flowchart of the six stages forming the proposed methodology.
Figure 1 Flowchart of the six stages forming the proposed methodology.CC BY · PubMed Central
Figure 2 Textures of the set of by-products collected from local metal industries.
Figure 2 Textures of the set of by-products collected from local metal industries.CC BY · PubMed Central
Figure 3 (a) By-products heating test; (b) Thermographic image recorded with the infrared camera.
Figure 3 (a) By-products heating test; (b) Thermographic image recorded with the infrared camera.CC BY · PubMed Central
Figure 4 (a) Pre-notched sample; (b) Group of demolded specimens.
Figure 4 (a) Pre-notched sample; (b) Group of demolded specimens.CC BY · PubMed Central
Figure 5 Details of the (a) Cradle with 7 cm between supports; (b) Three point bending test.
Figure 5 Details of the (a) Cradle with 7 cm between supports; (b) Three point bending test.CC BY · PubMed Central
Figure 6 (a) Healing of specimens after magnetic induction; (b) Thermal images of the specimens.
Figure 6 (a) Healing of specimens after magnetic induction; (b) Thermal images of the specimens.CC BY · PubMed Central
Figure 7 Temperature achieved by the by-products tested as potential heating inductors.
Figure 7 Temperature achieved by the by-products tested as potential heating inductors.CC BY · PubMed Central
Figure 8 Healing ratios (%) achieved in relation to the time (s) and intensity (A) applied to test the different mixtures studied (a) REF_M; (b) SB3_M; (c) SB3_SB5_M; (d) SB4_SB5_M; (e) S1_SB3_M.
Figure 8 Healing ratios (%) achieved in relation to the time (s) and intensity (A) applied to test the different mixtures studied (a) REF_M; (b) SB3_M; (c) SB3_SB5_M; (d) SB4_SB5_M; (e) S1_SB3_M.CC BY · PubMed Central
Figure 9 Healing ratios (%) achieved by the different mixtures in relation to (a) Temperature (°C) and (b) The product of time by intensity (s·A); (c) Breaking loads applied to the mixtures before healing (L^bh).
Figure 9 Healing ratios (%) achieved by the different mixtures in relation to (a) Temperature (°C) and (b) The product of time by intensity (s·A); (c) Breaking loads applied to the mixtures before healing (L^bh).CC BY · PubMed Central

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Research works printed but not linked: 17

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Cited by

Metadata

Authors
Vila-Cortavitarte, M., Jato-Espino, D., Castro-Fresno, D., & Calzada-Pérez, M. Á.
Venue
Materials
Publisher
MDPI AG
Published
2018-05-15
Volume
11
Issue
5
Pages
800
ISSN
1996-1944
DOI
10.3390/ma11050800
PMC
PMC5978177
Cited by
35
References
0
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
Ministerio de Economía, Industria y Competitividad, Gobierno de España (BIA2016-77372-R)

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.