Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network Article Swipe
Ahmed R. Suleiman
,
Moncef L. Nehdi
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.3390/ma10020135
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.3390/ma10020135
This paper presents an approach to predicting the intrinsic self-healing in concrete using a hybrid genetic algorithm–artificial neural network (GA–ANN). A genetic algorithm was implemented in the network as a stochastic optimizing tool for the initial optimal weights and biases. This approach can assist the network in achieving a global optimum and avoid the possibility of the network getting trapped at local optima. The proposed model was trained and validated using an especially built database using various experimental studies retrieved from the open literature. The model inputs include the cement content, water-to-cement ratio (w/c), type and dosage of supplementary cementitious materials, bio-healing materials, and both expansive and crystalline additives. Self-healing indicated by means of crack width is the model output. The results showed that the proposed GA–ANN model is capable of capturing the complex effects of various self-healing agents (e.g., biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ma10020135
- https://www.mdpi.com/1996-1944/10/2/135/pdf?version=1486467360
- OA Status
- gold
- Cited By
- 89
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2586410984
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2586410984Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/ma10020135Digital Object Identifier
- Title
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Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural NetworkWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2017Year of publication
- Publication date
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2017-02-07Full publication date if available
- Authors
-
Ahmed R. Suleiman, Moncef L. NehdiList of authors in order
- Landing page
-
https://doi.org/10.3390/ma10020135Publisher landing page
- PDF URL
-
https://www.mdpi.com/1996-1944/10/2/135/pdf?version=1486467360Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1996-1944/10/2/135/pdf?version=1486467360Direct OA link when available
- Concepts
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Artificial neural network, Self-healing, Genetic algorithm, Computer science, Artificial intelligence, Algorithm, Machine learning, Medicine, Alternative medicine, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
89Total citation count in OpenAlex
- Citations by year (recent)
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2025: 13, 2024: 10, 2023: 20, 2022: 19, 2021: 10Per-year citation counts (last 5 years)
- References (count)
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68Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_date | 2017-02-07 |
| publication_year | 2017 |
| referenced_works | https://openalex.org/W2141131011, https://openalex.org/W3149377315, https://openalex.org/W2482648522, https://openalex.org/W2060124828, https://openalex.org/W2063922354, https://openalex.org/W2023273912, https://openalex.org/W2235332595, https://openalex.org/W1990402970, https://openalex.org/W2094096277, https://openalex.org/W1007754282, https://openalex.org/W2010152156, https://openalex.org/W2155839613, https://openalex.org/W6680646984, https://openalex.org/W2093261625, https://openalex.org/W2079888235, https://openalex.org/W2057966320, https://openalex.org/W2019100686, https://openalex.org/W2009545891, https://openalex.org/W1995339981, https://openalex.org/W1972637363, https://openalex.org/W2167152710, https://openalex.org/W2174971616, https://openalex.org/W1989266107, https://openalex.org/W2077559281, https://openalex.org/W6666463577, https://openalex.org/W1988374761, https://openalex.org/W2054780633, https://openalex.org/W1973072132, https://openalex.org/W2109563136, https://openalex.org/W2002276505, https://openalex.org/W2093234546, https://openalex.org/W2063014084, https://openalex.org/W2118024124, https://openalex.org/W2040543832, https://openalex.org/W2057420380, https://openalex.org/W2470917876, https://openalex.org/W2004566673, https://openalex.org/W2407463458, https://openalex.org/W6716197763, https://openalex.org/W6742174965, https://openalex.org/W2061933243, https://openalex.org/W2078339956, https://openalex.org/W2092709759, https://openalex.org/W1990257170, https://openalex.org/W2111874977, https://openalex.org/W2038398046, https://openalex.org/W2009727437, https://openalex.org/W2002379717, https://openalex.org/W2124939573, https://openalex.org/W2081175421, https://openalex.org/W1957134150, https://openalex.org/W2068328557, https://openalex.org/W2121215049, https://openalex.org/W2041557389, https://openalex.org/W1976049846, https://openalex.org/W2253338942, https://openalex.org/W2042607402, https://openalex.org/W2015634532, https://openalex.org/W2032035110, https://openalex.org/W2090922074, https://openalex.org/W2037545995, https://openalex.org/W6636745751, https://openalex.org/W2738765255, https://openalex.org/W2138051570, https://openalex.org/W2063082448, https://openalex.org/W1636475344, https://openalex.org/W2885340614, https://openalex.org/W2416423893 |
| referenced_works_count | 68 |
| abstract_inverted_index.A | 20 |
| abstract_inverted_index.a | 13, 29, 48 |
| abstract_inverted_index.an | 3, 71 |
| abstract_inverted_index.as | 28 |
| abstract_inverted_index.at | 60 |
| abstract_inverted_index.by | 111 |
| abstract_inverted_index.in | 10, 25, 46, 152 |
| abstract_inverted_index.is | 116, 128 |
| abstract_inverted_index.of | 55, 97, 113, 130, 135 |
| abstract_inverted_index.on | 148 |
| abstract_inverted_index.to | 5 |
| abstract_inverted_index.The | 63, 84, 120 |
| abstract_inverted_index.and | 38, 51, 68, 95, 103, 106, 145 |
| abstract_inverted_index.can | 42 |
| abstract_inverted_index.for | 33 |
| abstract_inverted_index.the | 7, 26, 34, 44, 53, 56, 81, 88, 117, 124, 132, 149 |
| abstract_inverted_index.was | 23, 66 |
| abstract_inverted_index.This | 0, 40 |
| abstract_inverted_index.both | 104 |
| abstract_inverted_index.from | 80 |
| abstract_inverted_index.open | 82 |
| abstract_inverted_index.that | 123 |
| abstract_inverted_index.tool | 32 |
| abstract_inverted_index.type | 94 |
| abstract_inverted_index.avoid | 52 |
| abstract_inverted_index.built | 73 |
| abstract_inverted_index.crack | 114 |
| abstract_inverted_index.local | 61 |
| abstract_inverted_index.means | 112 |
| abstract_inverted_index.model | 65, 85, 118, 127 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.ratio | 92 |
| abstract_inverted_index.using | 12, 70, 75 |
| abstract_inverted_index.width | 115 |
| abstract_inverted_index.(e.g., | 139 |
| abstract_inverted_index.(w/c), | 93 |
| abstract_inverted_index.agents | 138 |
| abstract_inverted_index.assist | 43 |
| abstract_inverted_index.cement | 89 |
| abstract_inverted_index.dosage | 96 |
| abstract_inverted_index.global | 49 |
| abstract_inverted_index.hybrid | 14 |
| abstract_inverted_index.inputs | 86 |
| abstract_inverted_index.neural | 17 |
| abstract_inverted_index.showed | 122 |
| abstract_inverted_index.biases. | 39 |
| abstract_inverted_index.capable | 129 |
| abstract_inverted_index.complex | 133 |
| abstract_inverted_index.effects | 134 |
| abstract_inverted_index.genetic | 15, 21 |
| abstract_inverted_index.getting | 58 |
| abstract_inverted_index.include | 87 |
| abstract_inverted_index.initial | 35 |
| abstract_inverted_index.network | 18, 27, 45, 57 |
| abstract_inverted_index.optima. | 62 |
| abstract_inverted_index.optimal | 36 |
| abstract_inverted_index.optimum | 50 |
| abstract_inverted_index.output. | 119 |
| abstract_inverted_index.results | 121 |
| abstract_inverted_index.studies | 78 |
| abstract_inverted_index.trained | 67 |
| abstract_inverted_index.trapped | 59 |
| abstract_inverted_index.various | 76, 136 |
| abstract_inverted_index.weights | 37 |
| abstract_inverted_index.GA–ANN | 126 |
| abstract_inverted_index.approach | 4, 41 |
| abstract_inverted_index.concrete | 11 |
| abstract_inverted_index.content, | 90 |
| abstract_inverted_index.database | 74 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.proposed | 64, 125 |
| abstract_inverted_index.achieving | 47 |
| abstract_inverted_index.additive, | 143 |
| abstract_inverted_index.algorithm | 22 |
| abstract_inverted_index.capturing | 131 |
| abstract_inverted_index.expansive | 105, 144 |
| abstract_inverted_index.indicated | 110 |
| abstract_inverted_index.intrinsic | 8 |
| abstract_inverted_index.material, | 141 |
| abstract_inverted_index.retrieved | 79 |
| abstract_inverted_index.validated | 69 |
| abstract_inverted_index.additives. | 108 |
| abstract_inverted_index.especially | 72 |
| abstract_inverted_index.materials, | 100, 102 |
| abstract_inverted_index.materials. | 154 |
| abstract_inverted_index.optimizing | 31 |
| abstract_inverted_index.predicting | 6 |
| abstract_inverted_index.stochastic | 30 |
| abstract_inverted_index.(GA–ANN). | 19 |
| abstract_inverted_index.bio-healing | 101 |
| abstract_inverted_index.biochemical | 140 |
| abstract_inverted_index.components) | 147 |
| abstract_inverted_index.crystalline | 107, 146 |
| abstract_inverted_index.implemented | 24 |
| abstract_inverted_index.literature. | 83 |
| abstract_inverted_index.performance | 151 |
| abstract_inverted_index.possibility | 54 |
| abstract_inverted_index.Self-healing | 109 |
| abstract_inverted_index.cement-based | 153 |
| abstract_inverted_index.cementitious | 99 |
| abstract_inverted_index.experimental | 77 |
| abstract_inverted_index.self-healing | 9, 137, 150 |
| abstract_inverted_index.silica-based | 142 |
| abstract_inverted_index.supplementary | 98 |
| abstract_inverted_index.water-to-cement | 91 |
| abstract_inverted_index.algorithm–artificial | 16 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5076326557 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I125749732 |
| citation_normalized_percentile.value | 0.87892293 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |