Using Ground-Penetrating Radar and Deep Learning to Rapidly Detect Voids and Rebar Defects in Linings Article Swipe
Peng Liu
,
Zude Ding
,
Wanping Zhang
,
Zhihua Ren
,
Xuxiang Yang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/su151511855
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/su151511855
The geological radar method has found widespread use in evaluating the quality of tunnel lining. However, relying on manual experience to interpret geological radar data may cause identification errors and reduce efficiency when dealing with large numbers of data. This paper proposes a method for identifying internal quality defects in tunnel lining using deep learning and transfer learning techniques. An experimental physical model for detecting the quality of tunnel lining radars was developed to identify the typical radar image features of internal quality defects. Using the geological radar method, a large volume of lining quality detection radar image data was collected, in conjunction with several examples of tunnel engineering. The preprocessing of geological radar data was performed, including gain and normalization, and a set of data samples exhibiting typical lining quality defects was prepared with 6236 detection targets in 4246 images. The intelligent recognition models for tunnel lining quality defects were established using a combination of geological radar image datasets and transfer learning concepts, based on the SSD and YOLOv4 models. The accuracy of the SSD algorithm for cavity defect recognition is 86.58%, with the YOLOv4 algorithm achieving slightly lower accuracy at 86.05%. For steel bar missing recognition, the SSD algorithm has an accuracy of 97.7%, compared to 98.18% accuracy for the YOLOv4 algorithm. This indicates that deep learning-based models are practical for tunnel quality defect detection.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su151511855
- https://www.mdpi.com/2071-1050/15/15/11855/pdf?version=1690902713
- OA Status
- gold
- Cited By
- 17
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385491110
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4385491110Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/su151511855Digital Object Identifier
- Title
-
Using Ground-Penetrating Radar and Deep Learning to Rapidly Detect Voids and Rebar Defects in LiningsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-01Full publication date if available
- Authors
-
Peng Liu, Zude Ding, Wanping Zhang, Zhihua Ren, Xuxiang YangList of authors in order
- Landing page
-
https://doi.org/10.3390/su151511855Publisher landing page
- PDF URL
-
https://www.mdpi.com/2071-1050/15/15/11855/pdf?version=1690902713Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2071-1050/15/15/11855/pdf?version=1690902713Direct OA link when available
- Concepts
-
Radar, Computer science, Artificial intelligence, Preprocessor, Ground-penetrating radar, Transfer of learning, Deep learning, Data pre-processing, Rebar, Computer vision, Remote sensing, Geology, Pattern recognition (psychology), Engineering, Structural engineering, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
17Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 10, 2024: 6, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4385491110 |
|---|---|
| doi | https://doi.org/10.3390/su151511855 |
| ids.doi | https://doi.org/10.3390/su151511855 |
| ids.openalex | https://openalex.org/W4385491110 |
| fwci | 4.63198797 |
| type | article |
| title | Using Ground-Penetrating Radar and Deep Learning to Rapidly Detect Voids and Rebar Defects in Linings |
| awards[0].id | https://openalex.org/G4545502998 |
| awards[0].funder_id | https://openalex.org/F4320321001 |
| awards[0].display_name | |
| awards[0].funder_award_id | 52168057 |
| awards[0].funder_display_name | National Natural Science Foundation of China |
| biblio.issue | 15 |
| biblio.volume | 15 |
| biblio.last_page | 11855 |
| biblio.first_page | 11855 |
| 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 | 0.9998999834060669 |
| 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/T10161 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9919000267982483 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2211 |
| topics[1].subfield.display_name | Mechanics of Materials |
| topics[1].display_name | Rock Mechanics and Modeling |
| topics[2].id | https://openalex.org/T12482 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9879999756813049 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2205 |
| topics[2].subfield.display_name | Civil and Structural Engineering |
| topics[2].display_name | Tunneling and Rock Mechanics |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| is_xpac | False |
| apc_list.value | 2200 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2382 |
| apc_paid.value | 2200 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2382 |
| concepts[0].id | https://openalex.org/C554190296 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6573503017425537 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q47528 |
| concepts[0].display_name | Radar |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6202067732810974 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5710979104042053 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C34736171 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5642727017402649 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q918333 |
| concepts[3].display_name | Preprocessor |
| concepts[4].id | https://openalex.org/C71813955 |
| concepts[4].level | 3 |
| concepts[4].score | 0.5243825316429138 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q503560 |
| concepts[4].display_name | Ground-penetrating radar |
| concepts[5].id | https://openalex.org/C150899416 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4807031750679016 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1820378 |
| concepts[5].display_name | Transfer of learning |
| concepts[6].id | https://openalex.org/C108583219 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4779527187347412 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[6].display_name | Deep learning |
| concepts[7].id | https://openalex.org/C10551718 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4745284616947174 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q5227332 |
| concepts[7].display_name | Data pre-processing |
| concepts[8].id | https://openalex.org/C2778664469 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4187707304954529 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q150425 |
| concepts[8].display_name | Rebar |
| concepts[9].id | https://openalex.org/C31972630 |
| concepts[9].level | 1 |
| concepts[9].score | 0.3607410490512848 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[9].display_name | Computer vision |
| concepts[10].id | https://openalex.org/C62649853 |
| concepts[10].level | 1 |
| concepts[10].score | 0.34179919958114624 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[10].display_name | Remote sensing |
| concepts[11].id | https://openalex.org/C127313418 |
| concepts[11].level | 0 |
| concepts[11].score | 0.33841484785079956 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[11].display_name | Geology |
| concepts[12].id | https://openalex.org/C153180895 |
| concepts[12].level | 2 |
| concepts[12].score | 0.334988534450531 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[12].display_name | Pattern recognition (psychology) |
| concepts[13].id | https://openalex.org/C127413603 |
| concepts[13].level | 0 |
| concepts[13].score | 0.16835492849349976 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[13].display_name | Engineering |
| concepts[14].id | https://openalex.org/C66938386 |
| concepts[14].level | 1 |
| concepts[14].score | 0.06640475988388062 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q633538 |
| concepts[14].display_name | Structural engineering |
| concepts[15].id | https://openalex.org/C76155785 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[15].display_name | Telecommunications |
| keywords[0].id | https://openalex.org/keywords/radar |
| keywords[0].score | 0.6573503017425537 |
| keywords[0].display_name | Radar |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6202067732810974 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.5710979104042053 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/preprocessor |
| keywords[3].score | 0.5642727017402649 |
| keywords[3].display_name | Preprocessor |
| keywords[4].id | https://openalex.org/keywords/ground-penetrating-radar |
| keywords[4].score | 0.5243825316429138 |
| keywords[4].display_name | Ground-penetrating radar |
| keywords[5].id | https://openalex.org/keywords/transfer-of-learning |
| keywords[5].score | 0.4807031750679016 |
| keywords[5].display_name | Transfer of learning |
| keywords[6].id | https://openalex.org/keywords/deep-learning |
| keywords[6].score | 0.4779527187347412 |
| keywords[6].display_name | Deep learning |
| keywords[7].id | https://openalex.org/keywords/data-pre-processing |
| keywords[7].score | 0.4745284616947174 |
| keywords[7].display_name | Data pre-processing |
| keywords[8].id | https://openalex.org/keywords/rebar |
| keywords[8].score | 0.4187707304954529 |
| keywords[8].display_name | Rebar |
| keywords[9].id | https://openalex.org/keywords/computer-vision |
| keywords[9].score | 0.3607410490512848 |
| keywords[9].display_name | Computer vision |
| keywords[10].id | https://openalex.org/keywords/remote-sensing |
| keywords[10].score | 0.34179919958114624 |
| keywords[10].display_name | Remote sensing |
| keywords[11].id | https://openalex.org/keywords/geology |
| keywords[11].score | 0.33841484785079956 |
| keywords[11].display_name | Geology |
| keywords[12].id | https://openalex.org/keywords/pattern-recognition |
| keywords[12].score | 0.334988534450531 |
| keywords[12].display_name | Pattern recognition (psychology) |
| keywords[13].id | https://openalex.org/keywords/engineering |
| keywords[13].score | 0.16835492849349976 |
| keywords[13].display_name | Engineering |
| keywords[14].id | https://openalex.org/keywords/structural-engineering |
| keywords[14].score | 0.06640475988388062 |
| keywords[14].display_name | Structural engineering |
| language | en |
| locations[0].id | doi:10.3390/su151511855 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S10134376 |
| locations[0].source.issn | 2071-1050 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2071-1050 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Sustainability |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/2071-1050/15/15/11855/pdf?version=1690902713 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Sustainability |
| locations[0].landing_page_url | https://doi.org/10.3390/su151511855 |
| locations[1].id | pmh:oai:mdpi.com:/2071-1050/15/15/11855/ |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400947 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | MDPI (MDPI AG) |
| locations[1].source.host_organization | https://openalex.org/I4210097602 |
| locations[1].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[1].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | Text |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Sustainability; Volume 15; Issue 15; Pages: 11855 |
| locations[1].landing_page_url | https://dx.doi.org/10.3390/su151511855 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5100346853 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-7732-2687 |
| authorships[0].author.display_name | Peng Liu |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I10660446 |
| authorships[0].affiliations[0].raw_affiliation_string | Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China |
| authorships[0].institutions[0].id | https://openalex.org/I10660446 |
| authorships[0].institutions[0].ror | https://ror.org/00xyeez13 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I10660446 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Kunming University of Science and Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Peng Liu |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China |
| authorships[1].author.id | https://openalex.org/A5025537838 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-6140-1885 |
| authorships[1].author.display_name | Zude Ding |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I10660446 |
| authorships[1].affiliations[0].raw_affiliation_string | Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China |
| authorships[1].institutions[0].id | https://openalex.org/I10660446 |
| authorships[1].institutions[0].ror | https://ror.org/00xyeez13 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I10660446 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Kunming University of Science and Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Zude Ding |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China |
| authorships[2].author.id | https://openalex.org/A5101894198 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-9134-492X |
| authorships[2].author.display_name | Wanping Zhang |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I10660446 |
| authorships[2].affiliations[0].raw_affiliation_string | Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China |
| authorships[2].institutions[0].id | https://openalex.org/I10660446 |
| authorships[2].institutions[0].ror | https://ror.org/00xyeez13 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I10660446 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Kunming University of Science and Technology |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Wanping Zhang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China |
| authorships[3].author.id | https://openalex.org/A5111686738 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Zhihua Ren |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210139553 |
| authorships[3].affiliations[0].raw_affiliation_string | Yunnan Institute of Highway Science and Technology, Kunming 650051, China |
| authorships[3].institutions[0].id | https://openalex.org/I4210139553 |
| authorships[3].institutions[0].ror | https://ror.org/0335kqk33 |
| authorships[3].institutions[0].type | government |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210127216, https://openalex.org/I4210139553 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Research Institute of Highway |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Zhihua Ren |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Yunnan Institute of Highway Science and Technology, Kunming 650051, China |
| authorships[4].author.id | https://openalex.org/A5111018609 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Xuxiang Yang |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210139553 |
| authorships[4].affiliations[0].raw_affiliation_string | Yunnan Institute of Highway Science and Technology, Kunming 650051, China |
| authorships[4].institutions[0].id | https://openalex.org/I4210139553 |
| authorships[4].institutions[0].ror | https://ror.org/0335kqk33 |
| authorships[4].institutions[0].type | government |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210127216, https://openalex.org/I4210139553 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Research Institute of Highway |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Xuxiang Yang |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Yunnan Institute of Highway Science and Technology, Kunming 650051, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/2071-1050/15/15/11855/pdf?version=1690902713 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2023-08-03T00:00:00 |
| display_name | Using Ground-Penetrating Radar and Deep Learning to Rapidly Detect Voids and Rebar Defects in Linings |
| has_fulltext | True |
| 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 | 0.9998999834060669 |
| 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/W3210819266, https://openalex.org/W2989490741, https://openalex.org/W3092506759, https://openalex.org/W2367545121, https://openalex.org/W4248881655, https://openalex.org/W2482165163, https://openalex.org/W3010890513, https://openalex.org/W120741642, https://openalex.org/W138569904, https://openalex.org/W2390914021 |
| cited_by_count | 17 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 10 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 6 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.3390/su151511855 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S10134376 |
| best_oa_location.source.issn | 2071-1050 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2071-1050 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Sustainability |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/2071-1050/15/15/11855/pdf?version=1690902713 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Sustainability |
| best_oa_location.landing_page_url | https://doi.org/10.3390/su151511855 |
| primary_location.id | doi:10.3390/su151511855 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S10134376 |
| primary_location.source.issn | 2071-1050 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2071-1050 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Sustainability |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2071-1050/15/15/11855/pdf?version=1690902713 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Sustainability |
| primary_location.landing_page_url | https://doi.org/10.3390/su151511855 |
| publication_date | 2023-08-01 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W3199753892, https://openalex.org/W4367052830, https://openalex.org/W2004590390, https://openalex.org/W2028617483, https://openalex.org/W2035660147, https://openalex.org/W3048712411, https://openalex.org/W6904392737, https://openalex.org/W2791732466, https://openalex.org/W3123843678, https://openalex.org/W2730726911, https://openalex.org/W2395626468, https://openalex.org/W2401477020, https://openalex.org/W3030042352, https://openalex.org/W2087394381, https://openalex.org/W1903029394, https://openalex.org/W1901129140, https://openalex.org/W2963881378, https://openalex.org/W2050418754, https://openalex.org/W2937350470, https://openalex.org/W2796506861, https://openalex.org/W2983902176, https://openalex.org/W3013585766, https://openalex.org/W3192673362, https://openalex.org/W2944671768, https://openalex.org/W2193145675, https://openalex.org/W3104564825, https://openalex.org/W3106250896 |
| referenced_works_count | 27 |
| abstract_inverted_index.a | 42, 89, 122, 153 |
| abstract_inverted_index.An | 59 |
| abstract_inverted_index.an | 202 |
| abstract_inverted_index.at | 191 |
| abstract_inverted_index.in | 8, 49, 101, 138 |
| abstract_inverted_index.is | 181 |
| abstract_inverted_index.of | 12, 37, 67, 80, 92, 106, 111, 124, 155, 173, 204 |
| abstract_inverted_index.on | 17, 165 |
| abstract_inverted_index.to | 20, 73, 207 |
| abstract_inverted_index.For | 193 |
| abstract_inverted_index.SSD | 167, 175, 199 |
| abstract_inverted_index.The | 0, 109, 141, 171 |
| abstract_inverted_index.and | 29, 55, 119, 121, 160, 168 |
| abstract_inverted_index.are | 220 |
| abstract_inverted_index.bar | 195 |
| abstract_inverted_index.for | 44, 63, 145, 177, 210, 222 |
| abstract_inverted_index.has | 4, 201 |
| abstract_inverted_index.may | 25 |
| abstract_inverted_index.set | 123 |
| abstract_inverted_index.the | 10, 65, 75, 85, 166, 174, 184, 198, 211 |
| abstract_inverted_index.use | 7 |
| abstract_inverted_index.was | 71, 99, 115, 132 |
| abstract_inverted_index.4246 | 139 |
| abstract_inverted_index.6236 | 135 |
| abstract_inverted_index.This | 39, 214 |
| abstract_inverted_index.data | 24, 98, 114, 125 |
| abstract_inverted_index.deep | 53, 217 |
| abstract_inverted_index.gain | 118 |
| abstract_inverted_index.that | 216 |
| abstract_inverted_index.were | 150 |
| abstract_inverted_index.when | 32 |
| abstract_inverted_index.with | 34, 103, 134, 183 |
| abstract_inverted_index.Using | 84 |
| abstract_inverted_index.based | 164 |
| abstract_inverted_index.cause | 26 |
| abstract_inverted_index.data. | 38 |
| abstract_inverted_index.found | 5 |
| abstract_inverted_index.image | 78, 97, 158 |
| abstract_inverted_index.large | 35, 90 |
| abstract_inverted_index.lower | 189 |
| abstract_inverted_index.model | 62 |
| abstract_inverted_index.paper | 40 |
| abstract_inverted_index.radar | 2, 23, 77, 87, 96, 113, 157 |
| abstract_inverted_index.steel | 194 |
| abstract_inverted_index.using | 52, 152 |
| abstract_inverted_index.97.7%, | 205 |
| abstract_inverted_index.98.18% | 208 |
| abstract_inverted_index.YOLOv4 | 169, 185, 212 |
| abstract_inverted_index.cavity | 178 |
| abstract_inverted_index.defect | 179, 225 |
| abstract_inverted_index.errors | 28 |
| abstract_inverted_index.lining | 51, 69, 93, 129, 147 |
| abstract_inverted_index.manual | 18 |
| abstract_inverted_index.method | 3, 43 |
| abstract_inverted_index.models | 144, 219 |
| abstract_inverted_index.radars | 70 |
| abstract_inverted_index.reduce | 30 |
| abstract_inverted_index.tunnel | 13, 50, 68, 107, 146, 223 |
| abstract_inverted_index.volume | 91 |
| abstract_inverted_index.86.05%. | 192 |
| abstract_inverted_index.86.58%, | 182 |
| abstract_inverted_index.dealing | 33 |
| abstract_inverted_index.defects | 48, 131, 149 |
| abstract_inverted_index.images. | 140 |
| abstract_inverted_index.lining. | 14 |
| abstract_inverted_index.method, | 88 |
| abstract_inverted_index.missing | 196 |
| abstract_inverted_index.models. | 170 |
| abstract_inverted_index.numbers | 36 |
| abstract_inverted_index.quality | 11, 47, 66, 82, 94, 130, 148, 224 |
| abstract_inverted_index.relying | 16 |
| abstract_inverted_index.samples | 126 |
| abstract_inverted_index.several | 104 |
| abstract_inverted_index.targets | 137 |
| abstract_inverted_index.typical | 76, 128 |
| abstract_inverted_index.However, | 15 |
| abstract_inverted_index.accuracy | 172, 190, 203, 209 |
| abstract_inverted_index.compared | 206 |
| abstract_inverted_index.datasets | 159 |
| abstract_inverted_index.defects. | 83 |
| abstract_inverted_index.examples | 105 |
| abstract_inverted_index.features | 79 |
| abstract_inverted_index.identify | 74 |
| abstract_inverted_index.internal | 46, 81 |
| abstract_inverted_index.learning | 54, 57, 162 |
| abstract_inverted_index.physical | 61 |
| abstract_inverted_index.prepared | 133 |
| abstract_inverted_index.proposes | 41 |
| abstract_inverted_index.slightly | 188 |
| abstract_inverted_index.transfer | 56, 161 |
| abstract_inverted_index.achieving | 187 |
| abstract_inverted_index.algorithm | 176, 186, 200 |
| abstract_inverted_index.concepts, | 163 |
| abstract_inverted_index.detecting | 64 |
| abstract_inverted_index.detection | 95, 136 |
| abstract_inverted_index.developed | 72 |
| abstract_inverted_index.including | 117 |
| abstract_inverted_index.indicates | 215 |
| abstract_inverted_index.interpret | 21 |
| abstract_inverted_index.practical | 221 |
| abstract_inverted_index.algorithm. | 213 |
| abstract_inverted_index.collected, | 100 |
| abstract_inverted_index.detection. | 226 |
| abstract_inverted_index.efficiency | 31 |
| abstract_inverted_index.evaluating | 9 |
| abstract_inverted_index.exhibiting | 127 |
| abstract_inverted_index.experience | 19 |
| abstract_inverted_index.geological | 1, 22, 86, 112, 156 |
| abstract_inverted_index.performed, | 116 |
| abstract_inverted_index.widespread | 6 |
| abstract_inverted_index.combination | 154 |
| abstract_inverted_index.conjunction | 102 |
| abstract_inverted_index.established | 151 |
| abstract_inverted_index.identifying | 45 |
| abstract_inverted_index.intelligent | 142 |
| abstract_inverted_index.recognition | 143, 180 |
| abstract_inverted_index.techniques. | 58 |
| abstract_inverted_index.engineering. | 108 |
| abstract_inverted_index.experimental | 60 |
| abstract_inverted_index.recognition, | 197 |
| abstract_inverted_index.preprocessing | 110 |
| abstract_inverted_index.identification | 27 |
| abstract_inverted_index.learning-based | 218 |
| abstract_inverted_index.normalization, | 120 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5025537838 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I10660446 |
| citation_normalized_percentile.value | 0.93861943 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |