Multi-level optimisation of feature extraction networks for concrete surface crack detection Article Swipe
Faris Elghaish
,
Sandra Matarneh
,
Farzad Pour Rahimian
,
Essam Abdellatef
,
David J. Edwards
,
Obuks Ejohwomu
,
Mohammed Abdelmegid
,
Chansik Park
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.dibe.2024.100587
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.dibe.2024.100587
With the increasing utilisation of deep learning (DL) for detecting and classifying distress in concrete surfaces, the demand for accurate and precise models has risen. This study proposes a novel empirical approach of multilayer optimisation for two prominent DL models, namely ResNet101 and Xception, to classify distress in concrete surfaces. Both models were trained using 20,000 images depicting various types of cracks and tested with another set of 20,000 images. Four algorithms (Sequential Motion Optimisation (SMO), shuffled frog-leaping algorithm (SFLA), grey wolf optimisation (GWO), walrus optimisation (WO)) were then applied to enhance classification accuracy. After evaluating the DL models’ overall performance, the four algorithms were grouped into two layers. The first layer comprised SMO, SFLA, GWO and their combined application. Subsequently, the second stage implemented the WO optimiser to enhance performance further. The outcomes demonstrated a substantial positive impact on the accuracy of both CNN models. Specifically, ResNet101 achieved 98.9% accuracy and Xception reached 99.2% accuracy. In the accuracy breakdown, ResNet101 achieved 97.6% accuracy and Xception achieved 98.3% accuracy in the first stage, compared to 87.4% for Xception and 83.1% for ResNet101 before optimisation. Given that this approach achieves over 99% accuracy in detecting cracks on concrete surfaces, it offers a significant improvement in the efficiency and cost-effectiveness of structural health surveys for large buildings. Furthermore, it provides structural engineers with precise data to accurately determine and implement the required maintenance actions.
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Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.dibe.2024.100587
- OA Status
- gold
- Cited By
- 7
- References
- 80
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405414039
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405414039Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.dibe.2024.100587Digital Object Identifier
- Title
-
Multi-level optimisation of feature extraction networks for concrete surface crack detectionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-12-15Full publication date if available
- Authors
-
Faris Elghaish, Sandra Matarneh, Farzad Pour Rahimian, Essam Abdellatef, David J. Edwards, Obuks Ejohwomu, Mohammed Abdelmegid, Chansik ParkList of authors in order
- Landing page
-
https://doi.org/10.1016/j.dibe.2024.100587Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.dibe.2024.100587Direct OA link when available
- Concepts
-
Surface (topology), Feature (linguistics), Extraction (chemistry), Feature extraction, Computer science, Artificial intelligence, Structural engineering, Engineering, Mathematics, Geometry, Chromatography, Philosophy, Linguistics, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7Per-year citation counts (last 5 years)
- References (count)
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80Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.DL | 38, 97 |
| abstract_inverted_index.In | 156 |
| abstract_inverted_index.WO | 126 |
| abstract_inverted_index.in | 13, 47, 169, 192, 203 |
| abstract_inverted_index.it | 198, 216 |
| abstract_inverted_index.of | 4, 32, 60, 67, 142, 208 |
| abstract_inverted_index.on | 139, 195 |
| abstract_inverted_index.to | 44, 90, 128, 174, 223 |
| abstract_inverted_index.99% | 190 |
| abstract_inverted_index.CNN | 144 |
| abstract_inverted_index.GWO | 115 |
| abstract_inverted_index.The | 109, 132 |
| abstract_inverted_index.and | 10, 20, 42, 62, 116, 151, 164, 178, 206, 226 |
| abstract_inverted_index.for | 8, 18, 35, 176, 180, 212 |
| abstract_inverted_index.has | 23 |
| abstract_inverted_index.set | 66 |
| abstract_inverted_index.the | 1, 16, 96, 101, 121, 125, 140, 157, 170, 204, 228 |
| abstract_inverted_index.two | 36, 107 |
| abstract_inverted_index.(DL) | 7 |
| abstract_inverted_index.Both | 50 |
| abstract_inverted_index.Four | 70 |
| abstract_inverted_index.SMO, | 113 |
| abstract_inverted_index.This | 25 |
| abstract_inverted_index.With | 0 |
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| abstract_inverted_index.then | 88 |
| abstract_inverted_index.this | 186 |
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| abstract_inverted_index.with | 64, 220 |
| abstract_inverted_index.wolf | 81 |
| abstract_inverted_index.(WO)) | 86 |
| abstract_inverted_index.83.1% | 179 |
| abstract_inverted_index.87.4% | 175 |
| abstract_inverted_index.97.6% | 162 |
| abstract_inverted_index.98.3% | 167 |
| abstract_inverted_index.98.9% | 149 |
| abstract_inverted_index.99.2% | 154 |
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| abstract_inverted_index.Given | 184 |
| abstract_inverted_index.SFLA, | 114 |
| abstract_inverted_index.first | 110, 171 |
| abstract_inverted_index.large | 213 |
| abstract_inverted_index.layer | 111 |
| abstract_inverted_index.novel | 29 |
| abstract_inverted_index.stage | 123 |
| abstract_inverted_index.study | 26 |
| abstract_inverted_index.their | 117 |
| abstract_inverted_index.types | 59 |
| abstract_inverted_index.using | 54 |
| abstract_inverted_index.(GWO), | 83 |
| abstract_inverted_index.(SMO), | 75 |
| abstract_inverted_index.20,000 | 55, 68 |
| abstract_inverted_index.Motion | 73 |
| abstract_inverted_index.before | 182 |
| abstract_inverted_index.cracks | 61, 194 |
| abstract_inverted_index.demand | 17 |
| abstract_inverted_index.health | 210 |
| abstract_inverted_index.images | 56 |
| abstract_inverted_index.impact | 138 |
| abstract_inverted_index.models | 22, 51 |
| abstract_inverted_index.namely | 40 |
| abstract_inverted_index.offers | 199 |
| abstract_inverted_index.risen. | 24 |
| abstract_inverted_index.second | 122 |
| abstract_inverted_index.stage, | 172 |
| abstract_inverted_index.tested | 63 |
| abstract_inverted_index.walrus | 84 |
| abstract_inverted_index.(SFLA), | 79 |
| abstract_inverted_index.another | 65 |
| abstract_inverted_index.applied | 89 |
| abstract_inverted_index.enhance | 91, 129 |
| abstract_inverted_index.grouped | 105 |
| abstract_inverted_index.images. | 69 |
| abstract_inverted_index.layers. | 108 |
| abstract_inverted_index.models, | 39 |
| abstract_inverted_index.models. | 145 |
| abstract_inverted_index.overall | 99 |
| abstract_inverted_index.precise | 21, 221 |
| abstract_inverted_index.reached | 153 |
| abstract_inverted_index.surveys | 211 |
| abstract_inverted_index.trained | 53 |
| abstract_inverted_index.various | 58 |
| abstract_inverted_index.Xception | 152, 165, 177 |
| abstract_inverted_index.accuracy | 141, 150, 158, 163, 168, 191 |
| abstract_inverted_index.accurate | 19 |
| abstract_inverted_index.achieved | 148, 161, 166 |
| abstract_inverted_index.achieves | 188 |
| abstract_inverted_index.actions. | 231 |
| abstract_inverted_index.approach | 31, 187 |
| abstract_inverted_index.classify | 45 |
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| abstract_inverted_index.compared | 173 |
| abstract_inverted_index.concrete | 14, 48, 196 |
| abstract_inverted_index.distress | 12, 46 |
| abstract_inverted_index.further. | 131 |
| abstract_inverted_index.learning | 6 |
| abstract_inverted_index.outcomes | 133 |
| abstract_inverted_index.positive | 137 |
| abstract_inverted_index.proposes | 27 |
| abstract_inverted_index.provides | 217 |
| abstract_inverted_index.required | 229 |
| abstract_inverted_index.shuffled | 76 |
| abstract_inverted_index.ResNet101 | 41, 147, 160, 181 |
| abstract_inverted_index.Xception, | 43 |
| abstract_inverted_index.accuracy. | 93, 155 |
| abstract_inverted_index.algorithm | 78 |
| abstract_inverted_index.comprised | 112 |
| abstract_inverted_index.depicting | 57 |
| abstract_inverted_index.detecting | 9, 193 |
| abstract_inverted_index.determine | 225 |
| abstract_inverted_index.empirical | 30 |
| abstract_inverted_index.engineers | 219 |
| abstract_inverted_index.implement | 227 |
| abstract_inverted_index.models’ | 98 |
| abstract_inverted_index.optimiser | 127 |
| abstract_inverted_index.prominent | 37 |
| abstract_inverted_index.surfaces, | 15, 197 |
| abstract_inverted_index.surfaces. | 49 |
| abstract_inverted_index.accurately | 224 |
| abstract_inverted_index.algorithms | 71, 103 |
| abstract_inverted_index.breakdown, | 159 |
| abstract_inverted_index.buildings. | 214 |
| abstract_inverted_index.efficiency | 205 |
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| abstract_inverted_index.increasing | 2 |
| abstract_inverted_index.multilayer | 33 |
| abstract_inverted_index.structural | 209, 218 |
| abstract_inverted_index.(Sequential | 72 |
| abstract_inverted_index.classifying | 11 |
| abstract_inverted_index.implemented | 124 |
| abstract_inverted_index.improvement | 202 |
| abstract_inverted_index.maintenance | 230 |
| abstract_inverted_index.performance | 130 |
| abstract_inverted_index.significant | 201 |
| abstract_inverted_index.substantial | 136 |
| abstract_inverted_index.utilisation | 3 |
| abstract_inverted_index.Furthermore, | 215 |
| abstract_inverted_index.Optimisation | 74 |
| abstract_inverted_index.application. | 119 |
| abstract_inverted_index.demonstrated | 134 |
| abstract_inverted_index.frog-leaping | 77 |
| abstract_inverted_index.optimisation | 34, 82, 85 |
| abstract_inverted_index.performance, | 100 |
| abstract_inverted_index.Specifically, | 146 |
| abstract_inverted_index.Subsequently, | 120 |
| abstract_inverted_index.optimisation. | 183 |
| abstract_inverted_index.classification | 92 |
| abstract_inverted_index.cost-effectiveness | 207 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.88598335 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |