The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
A comprehensive review on self-healing of asphalt materials: Mechanism, model, characterization and enhancement
Abstract
Self-healing has great potential to extend the service life of asphalt pavement, and this capability has been regarded as an important strategy when designing a sustainable infrastructure. This review presents a comprehensive summary of the state-of-the-art investigations concerning the self-healing mechanism, model, characterization and enhancement, ranging from asphalt to asphalt pavement. Firstly, the self-healing phenomenon as a general concept in asphalt materials is analyzed including its definition and the differences among self-healing and some viscoelastic responses. Additionally, the development of self-healing in asphalt pavement design is introduced. Next, four kinds of possible self-healing mechanism and corresponding models are presented. It is pointed out that the continuum thermodynamic model, considering the whole process from damage initiation to healing recovery, can be a promising study field. Further, a set of self-healing multiscale characterization methods from microscale to macroscale as well as computational simulation scale, are summed up. Thereinto, the computational simulation shows great potential in simulating the self-healing behavior of asphalt materials from mechanical and molecular level. Moreover, the factors influencing self-healing capability are discussed, but the action mechanisms of some factors remain unclear and need to be investigated. Finally, two extrinsic self-healing technologies, induction heating and capsule healing, are recommended as preventive maintenance applications in asphalt pavement. In future, more effective energy-based healing systems or novel material-based healing systems are expected to be developed towards designing sustainable long-life asphalt pavement.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Computer Simulation of Liquids
Allen, M. P., & Tildesley, D. J. (2017) · doi:10.1093/oso/9780198803195.001.0001 · Cited by 20,945
Computer Simulation of Liquids
P, S., & J, K. (1988) · Journal of Molecular Liquids · doi:10.1016/0167-7322(88)80022-9 · Cited by 15,812
Autonomic healing of polymer composites
White, S. R., Sottos, N. R., Geubelle, P. H., Moore, J. S., Kessler, M. R., Sriram, S. R., Brown, E. N., & Viswanathan, S. (2001) · Nature · doi:10.1038/35057232 · Cited by 4,575
Reptation of a Polymer Chain in the Presence of Fixed Obstacles
de Gennes, P. G. (1971) · The Journal of Chemical Physics · doi:10.1063/1.1675789 · Cited by 4,284
Self-healing and thermoreversible rubber from supramolecular assembly
Cordier, P., Tournilhac, F., Soulié-Ziakovic, C., & Leibler, L. (2008) · Nature · doi:10.1038/nature06669 · Cited by 2,984
Understanding Molecular Simulation
(2002) · doi:10.1016/b978-0-12-267351-1.x5000-7 · Cited by 2,042
Introduction to continuum damage mechanics
Kachanov, L. M. (1986) · Mechanics of Elastic Stability · doi:10.1007/978-94-017-1957-5 · Cited by 1,879
Self-healing polymeric materials: A review of recent developments
Wu, D. Y., Meure, S., & Solomon, D. (2008) · Progress in Polymer Science · doi:10.1016/j.progpolymsci.2008.02.001 · Cited by 1,389
Hunter, R. N., Self, A., Read, J., Gerlis, R., Taylor, S. F. R., Hobson, E., & name., A. (2015) · Cited by 1,329
Hager, M. D., Greil, P., Leyens, C., van der Zwaag, S., & Schubert, U. S. (2010) · Advanced Materials · doi:10.1002/adma.201003036 · Cited by 1,238
Metadata
- Authors
- Sun, D., Sun, G., Zhu, X., Guarin, A., Li, B., Dai, Z., & Ling, J.
- Venue
- Advances in Colloid and Interface Science
- Publisher
- Elsevier BV
- Published
- 2018-06
- Volume
- 256
- Pages
- 65-93
- ISSN
- 0001-8686 (ISSN-L)
- OpenAlex
- W2801886770
- Cited by
- 283
- References
- 70
- FWCI
- 20.88
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A comprehensive review on self-healing of asphalt materials: Mechanism, model, characterization and enhancement. https://app.cochat.ai/papers/10.1016/j.cis.2018.05.003
- The paper
- Sun, D., Sun, G., Zhu, X., Guarin, A., Li, B., Dai, Z., & Ling, J. (2018). A comprehensive review on self-healing of asphalt materials: Mechanism, model, characterization and enhancement. Advances in Colloid and Interface Science. https://doi.org/10.1016/j.cis.2018.05.003
Data sources
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.
