Ultrasonic tomography with deep learning for detecting embedded components and internal damage of concrete structures Article Swipe
Inad Alqurashi
,
Mastour Alsulami
,
Ninel Alver
,
F. Necati Çatbaş
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.dibe.2025.100742
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.dibe.2025.100742
Ultrasonic tomography is a powerful nondestructive technique for evaluating internal defects in concrete structures. This study presents a deep learning–enhanced approach utilizing a nanoscale object detection model to automate the localization and quantification of internal defects and embedded structural components, including reinforcement bars and ducts. Controlled concrete samples containing artificial defects of varying shapes and depths, along with embedded rebars and ducts, were designed. Ultrasonic signals were collected using a MIRA A1040 tomograph and reconstructed into 3D volumes via Synthetic Aperture Focusing Technique (SAFT). These volumes were converted into 2D slices and segmented using Chan-Vese segmentation and morphological post-processing. A partial histogram matching procedure unified color scales across segmented slices, minimizing color-related biases before model training. Segmentation-assisted labeling provided robust ground truth annotations, resulting in 7220 labeled images. The trained AI model accurately detected delaminations, rebars, and ducts (both grouted and ungrouted), achieving a mean Average Precision ([email protected]) of 0.73 and an Average Intersection-over-Union (IoU) of 0.80. Testing on real-world bridge data demonstrated the model's generalization to unseen conditions. Key innovations include automated segmentation-based labeling, robust color standardization via histogram matching, and a lightweight deep learning model optimized for real-time deployment on resource-constrained devices. This integrated approach has the potential to reduce manual interpretation and subjective variability, providing an effective, scalable NDT/E solution for rapid assessment and monitoring of concrete infrastructure through advanced ultrasonic imaging combined with standardized, machine learning-based defect detection.
Related Topics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.dibe.2025.100742
- OA Status
- gold
- Cited By
- 1
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413738948
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4413738948Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.dibe.2025.100742Digital Object Identifier
- Title
-
Ultrasonic tomography with deep learning for detecting embedded components and internal damage of concrete structuresWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-27Full publication date if available
- Authors
-
Inad Alqurashi, Mastour Alsulami, Ninel Alver, F. Necati ÇatbaşList of authors in order
- Landing page
-
https://doi.org/10.1016/j.dibe.2025.100742Publisher 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.2025.100742Direct OA link when available
- Concepts
-
Ultrasonic sensor, Tomography, Materials science, Acoustics, Optics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
48Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4413738948 |
|---|---|
| doi | https://doi.org/10.1016/j.dibe.2025.100742 |
| ids.doi | https://doi.org/10.1016/j.dibe.2025.100742 |
| ids.openalex | https://openalex.org/W4413738948 |
| fwci | 3.06327829 |
| type | article |
| title | Ultrasonic tomography with deep learning for detecting embedded components and internal damage of concrete structures |
| biblio.issue | |
| biblio.volume | 23 |
| biblio.last_page | 100742 |
| biblio.first_page | 100742 |
| topics[0].id | https://openalex.org/T11609 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9998000264167786 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2212 |
| topics[0].subfield.display_name | Ocean Engineering |
| topics[0].display_name | Geophysical Methods and Applications |
| topics[1].id | https://openalex.org/T11606 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9994000196456909 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2205 |
| topics[1].subfield.display_name | Civil and Structural Engineering |
| topics[1].display_name | Infrastructure Maintenance and Monitoring |
| topics[2].id | https://openalex.org/T12169 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9973000288009644 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2210 |
| topics[2].subfield.display_name | Mechanical Engineering |
| topics[2].display_name | Non-Destructive Testing Techniques |
| is_xpac | False |
| apc_list.value | 2000 |
| apc_list.currency | USD |
| apc_list.value_usd | 2000 |
| apc_paid.value | 2000 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 2000 |
| concepts[0].id | https://openalex.org/C81288441 |
| concepts[0].level | 2 |
| concepts[0].score | 0.75025874376297 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q20736125 |
| concepts[0].display_name | Ultrasonic sensor |
| concepts[1].id | https://openalex.org/C163716698 |
| concepts[1].level | 2 |
| concepts[1].score | 0.4988889694213867 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q841267 |
| concepts[1].display_name | Tomography |
| concepts[2].id | https://openalex.org/C192562407 |
| concepts[2].level | 0 |
| concepts[2].score | 0.4675276279449463 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q228736 |
| concepts[2].display_name | Materials science |
| concepts[3].id | https://openalex.org/C24890656 |
| concepts[3].level | 1 |
| concepts[3].score | 0.36318379640579224 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q82811 |
| concepts[3].display_name | Acoustics |
| concepts[4].id | https://openalex.org/C120665830 |
| concepts[4].level | 1 |
| concepts[4].score | 0.15758827328681946 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q14620 |
| concepts[4].display_name | Optics |
| concepts[5].id | https://openalex.org/C121332964 |
| concepts[5].level | 0 |
| concepts[5].score | 0.0889403223991394 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[5].display_name | Physics |
| keywords[0].id | https://openalex.org/keywords/ultrasonic-sensor |
| keywords[0].score | 0.75025874376297 |
| keywords[0].display_name | Ultrasonic sensor |
| keywords[1].id | https://openalex.org/keywords/tomography |
| keywords[1].score | 0.4988889694213867 |
| keywords[1].display_name | Tomography |
| keywords[2].id | https://openalex.org/keywords/materials-science |
| keywords[2].score | 0.4675276279449463 |
| keywords[2].display_name | Materials science |
| keywords[3].id | https://openalex.org/keywords/acoustics |
| keywords[3].score | 0.36318379640579224 |
| keywords[3].display_name | Acoustics |
| keywords[4].id | https://openalex.org/keywords/optics |
| keywords[4].score | 0.15758827328681946 |
| keywords[4].display_name | Optics |
| keywords[5].id | https://openalex.org/keywords/physics |
| keywords[5].score | 0.0889403223991394 |
| keywords[5].display_name | Physics |
| language | en |
| locations[0].id | doi:10.1016/j.dibe.2025.100742 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210169634 |
| locations[0].source.issn | 2666-1659 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2666-1659 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Developments in the Built Environment |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_lineage_names | Elsevier BV |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Developments in the Built Environment |
| locations[0].landing_page_url | https://doi.org/10.1016/j.dibe.2025.100742 |
| locations[1].id | pmh:oai:doaj.org/article:6aaf80927cdc43ab8b9af019ba85d51f |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | cc-by-sa |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Developments in the Built Environment, Vol 23, Iss , Pp 100742- (2025) |
| locations[1].landing_page_url | https://doaj.org/article/6aaf80927cdc43ab8b9af019ba85d51f |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5107159680 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Inad Alqurashi |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Inad Alqurashi |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5119445861 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Mastour Alsulami |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Mastour Alsulami |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5026156217 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-7095-944X |
| authorships[2].author.display_name | Ninel Alver |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Ninel Alver |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5071812268 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-9255-9976 |
| authorships[3].author.display_name | F. Necati Çatbaş |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Necati Catbas |
| authorships[3].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1016/j.dibe.2025.100742 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Ultrasonic tomography with deep learning for detecting embedded components and internal damage of concrete structures |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11609 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9998000264167786 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2212 |
| primary_topic.subfield.display_name | Ocean Engineering |
| primary_topic.display_name | Geophysical Methods and Applications |
| related_works | https://openalex.org/W2899084033, https://openalex.org/W4404995717, https://openalex.org/W2016187641, https://openalex.org/W4404725684, https://openalex.org/W4413159334, https://openalex.org/W2380594826, https://openalex.org/W2031629218, https://openalex.org/W4238525810, https://openalex.org/W4247875078, https://openalex.org/W4238161731 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1016/j.dibe.2025.100742 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210169634 |
| best_oa_location.source.issn | 2666-1659 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2666-1659 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Developments in the Built Environment |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_lineage_names | Elsevier BV |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Developments in the Built Environment |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.dibe.2025.100742 |
| primary_location.id | doi:10.1016/j.dibe.2025.100742 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210169634 |
| primary_location.source.issn | 2666-1659 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2666-1659 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Developments in the Built Environment |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_lineage_names | Elsevier BV |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Developments in the Built Environment |
| primary_location.landing_page_url | https://doi.org/10.1016/j.dibe.2025.100742 |
| publication_date | 2025-08-27 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4410153631, https://openalex.org/W4405425805, https://openalex.org/W3016123475, https://openalex.org/W4285404487, https://openalex.org/W2598457882, https://openalex.org/W3210379830, https://openalex.org/W2213867446, https://openalex.org/W4367059347, https://openalex.org/W3133648007, https://openalex.org/W2905163589, https://openalex.org/W4406326720, https://openalex.org/W1982823792, https://openalex.org/W3045954275, https://openalex.org/W2758611534, https://openalex.org/W2623486883, https://openalex.org/W1617509827, https://openalex.org/W4389753519, https://openalex.org/W4389488334, https://openalex.org/W3129302208, https://openalex.org/W2888803582, https://openalex.org/W2802510928, https://openalex.org/W1861492603, https://openalex.org/W2009767800, https://openalex.org/W4367625539, https://openalex.org/W4396986275, https://openalex.org/W4214668084, https://openalex.org/W3172429562, https://openalex.org/W2115325793, https://openalex.org/W1997544222, https://openalex.org/W3196165062, https://openalex.org/W3013670837, https://openalex.org/W2742412886, https://openalex.org/W4407129904, https://openalex.org/W3118577511, https://openalex.org/W4400977168, https://openalex.org/W4386070914, https://openalex.org/W4409566014, https://openalex.org/W4220983834, https://openalex.org/W3018757597, https://openalex.org/W3138516171, https://openalex.org/W4402481577, https://openalex.org/W4403651355, https://openalex.org/W2787741374, https://openalex.org/W4389195725, https://openalex.org/W4390889814, https://openalex.org/W3096609285, https://openalex.org/W2980351136, https://openalex.org/W4408062356 |
| referenced_works_count | 48 |
| abstract_inverted_index.A | 99 |
| abstract_inverted_index.a | 3, 17, 22, 69, 143, 182 |
| abstract_inverted_index.2D | 89 |
| abstract_inverted_index.3D | 76 |
| abstract_inverted_index.AI | 130 |
| abstract_inverted_index.an | 151, 208 |
| abstract_inverted_index.in | 11, 124 |
| abstract_inverted_index.is | 2 |
| abstract_inverted_index.of | 33, 51, 148, 155, 218 |
| abstract_inverted_index.on | 158, 191 |
| abstract_inverted_index.to | 27, 166, 200 |
| abstract_inverted_index.Key | 169 |
| abstract_inverted_index.The | 128 |
| abstract_inverted_index.and | 31, 36, 43, 54, 60, 73, 91, 96, 136, 140, 150, 181, 204, 216 |
| abstract_inverted_index.for | 7, 188, 213 |
| abstract_inverted_index.has | 197 |
| abstract_inverted_index.the | 29, 163, 198 |
| abstract_inverted_index.via | 78, 178 |
| abstract_inverted_index.0.73 | 149 |
| abstract_inverted_index.7220 | 125 |
| abstract_inverted_index.MIRA | 70 |
| abstract_inverted_index.This | 14, 194 |
| abstract_inverted_index.bars | 42 |
| abstract_inverted_index.data | 161 |
| abstract_inverted_index.deep | 18, 184 |
| abstract_inverted_index.into | 75, 88 |
| abstract_inverted_index.mean | 144 |
| abstract_inverted_index.were | 62, 66, 86 |
| abstract_inverted_index.with | 57, 226 |
| abstract_inverted_index.(IoU) | 154 |
| abstract_inverted_index.(both | 138 |
| abstract_inverted_index.0.80. | 156 |
| abstract_inverted_index.A1040 | 71 |
| abstract_inverted_index.NDT/E | 211 |
| abstract_inverted_index.These | 84 |
| abstract_inverted_index.along | 56 |
| abstract_inverted_index.color | 105, 176 |
| abstract_inverted_index.ducts | 137 |
| abstract_inverted_index.model | 26, 114, 131, 186 |
| abstract_inverted_index.rapid | 214 |
| abstract_inverted_index.study | 15 |
| abstract_inverted_index.truth | 121 |
| abstract_inverted_index.using | 68, 93 |
| abstract_inverted_index.across | 107 |
| abstract_inverted_index.before | 113 |
| abstract_inverted_index.biases | 112 |
| abstract_inverted_index.bridge | 160 |
| abstract_inverted_index.defect | 230 |
| abstract_inverted_index.ducts, | 61 |
| abstract_inverted_index.ducts. | 44 |
| abstract_inverted_index.ground | 120 |
| abstract_inverted_index.manual | 202 |
| abstract_inverted_index.object | 24 |
| abstract_inverted_index.rebars | 59 |
| abstract_inverted_index.reduce | 201 |
| abstract_inverted_index.robust | 119, 175 |
| abstract_inverted_index.scales | 106 |
| abstract_inverted_index.shapes | 53 |
| abstract_inverted_index.slices | 90 |
| abstract_inverted_index.unseen | 167 |
| abstract_inverted_index.(SAFT). | 83 |
| abstract_inverted_index.Average | 145, 152 |
| abstract_inverted_index.Testing | 157 |
| abstract_inverted_index.defects | 10, 35, 50 |
| abstract_inverted_index.depths, | 55 |
| abstract_inverted_index.grouted | 139 |
| abstract_inverted_index.images. | 127 |
| abstract_inverted_index.imaging | 224 |
| abstract_inverted_index.include | 171 |
| abstract_inverted_index.labeled | 126 |
| abstract_inverted_index.machine | 228 |
| abstract_inverted_index.model's | 164 |
| abstract_inverted_index.partial | 100 |
| abstract_inverted_index.rebars, | 135 |
| abstract_inverted_index.samples | 47 |
| abstract_inverted_index.signals | 65 |
| abstract_inverted_index.slices, | 109 |
| abstract_inverted_index.through | 221 |
| abstract_inverted_index.trained | 129 |
| abstract_inverted_index.unified | 104 |
| abstract_inverted_index.varying | 52 |
| abstract_inverted_index.volumes | 77, 85 |
| abstract_inverted_index.Aperture | 80 |
| abstract_inverted_index.Focusing | 81 |
| abstract_inverted_index.advanced | 222 |
| abstract_inverted_index.approach | 20, 196 |
| abstract_inverted_index.automate | 28 |
| abstract_inverted_index.combined | 225 |
| abstract_inverted_index.concrete | 12, 46, 219 |
| abstract_inverted_index.detected | 133 |
| abstract_inverted_index.devices. | 193 |
| abstract_inverted_index.embedded | 37, 58 |
| abstract_inverted_index.internal | 9, 34 |
| abstract_inverted_index.labeling | 117 |
| abstract_inverted_index.learning | 185 |
| abstract_inverted_index.matching | 102 |
| abstract_inverted_index.powerful | 4 |
| abstract_inverted_index.presents | 16 |
| abstract_inverted_index.provided | 118 |
| abstract_inverted_index.scalable | 210 |
| abstract_inverted_index.solution | 212 |
| abstract_inverted_index.([email protected]) | 147 |
| abstract_inverted_index.Chan-Vese | 94 |
| abstract_inverted_index.Precision | 146 |
| abstract_inverted_index.Synthetic | 79 |
| abstract_inverted_index.Technique | 82 |
| abstract_inverted_index.achieving | 142 |
| abstract_inverted_index.automated | 172 |
| abstract_inverted_index.collected | 67 |
| abstract_inverted_index.converted | 87 |
| abstract_inverted_index.designed. | 63 |
| abstract_inverted_index.detection | 25 |
| abstract_inverted_index.histogram | 101, 179 |
| abstract_inverted_index.including | 40 |
| abstract_inverted_index.labeling, | 174 |
| abstract_inverted_index.matching, | 180 |
| abstract_inverted_index.nanoscale | 23 |
| abstract_inverted_index.optimized | 187 |
| abstract_inverted_index.potential | 199 |
| abstract_inverted_index.procedure | 103 |
| abstract_inverted_index.providing | 207 |
| abstract_inverted_index.real-time | 189 |
| abstract_inverted_index.resulting | 123 |
| abstract_inverted_index.segmented | 92, 108 |
| abstract_inverted_index.technique | 6 |
| abstract_inverted_index.tomograph | 72 |
| abstract_inverted_index.training. | 115 |
| abstract_inverted_index.utilizing | 21 |
| abstract_inverted_index.Controlled | 45 |
| abstract_inverted_index.Ultrasonic | 0, 64 |
| abstract_inverted_index.accurately | 132 |
| abstract_inverted_index.artificial | 49 |
| abstract_inverted_index.assessment | 215 |
| abstract_inverted_index.containing | 48 |
| abstract_inverted_index.deployment | 190 |
| abstract_inverted_index.detection. | 231 |
| abstract_inverted_index.effective, | 209 |
| abstract_inverted_index.evaluating | 8 |
| abstract_inverted_index.integrated | 195 |
| abstract_inverted_index.minimizing | 110 |
| abstract_inverted_index.monitoring | 217 |
| abstract_inverted_index.real-world | 159 |
| abstract_inverted_index.structural | 38 |
| abstract_inverted_index.subjective | 205 |
| abstract_inverted_index.tomography | 1 |
| abstract_inverted_index.ultrasonic | 223 |
| abstract_inverted_index.components, | 39 |
| abstract_inverted_index.conditions. | 168 |
| abstract_inverted_index.innovations | 170 |
| abstract_inverted_index.lightweight | 183 |
| abstract_inverted_index.structures. | 13 |
| abstract_inverted_index.ungrouted), | 141 |
| abstract_inverted_index.annotations, | 122 |
| abstract_inverted_index.demonstrated | 162 |
| abstract_inverted_index.localization | 30 |
| abstract_inverted_index.segmentation | 95 |
| abstract_inverted_index.variability, | 206 |
| abstract_inverted_index.color-related | 111 |
| abstract_inverted_index.morphological | 97 |
| abstract_inverted_index.reconstructed | 74 |
| abstract_inverted_index.reinforcement | 41 |
| abstract_inverted_index.standardized, | 227 |
| abstract_inverted_index.delaminations, | 134 |
| abstract_inverted_index.generalization | 165 |
| abstract_inverted_index.infrastructure | 220 |
| abstract_inverted_index.interpretation | 203 |
| abstract_inverted_index.learning-based | 229 |
| abstract_inverted_index.nondestructive | 5 |
| abstract_inverted_index.quantification | 32 |
| abstract_inverted_index.standardization | 177 |
| abstract_inverted_index.post-processing. | 98 |
| abstract_inverted_index.segmentation-based | 173 |
| abstract_inverted_index.learning–enhanced | 19 |
| abstract_inverted_index.resource-constrained | 192 |
| abstract_inverted_index.Segmentation-assisted | 116 |
| abstract_inverted_index.Intersection-over-Union | 153 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.87936602 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |