Solution to the problem of bridge structure damage identification by a response surface method and an imperialist competitive algorithm Article Swipe
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-022-17457-9
To increase the efficiency of structural damage identification (SDI) methods and timeously and accurately detect initial structural damage, this research develops an SDI method based on a response surface method (RSM) and an imperialist competitive algorithm (ICA). At first, a Latin hypercube design method is used for experimental design and selection of sample points based on RSM. Then, a high-order response surface surrogate model for the target frequency response and stiffness reduction factor is established. Finally, analysis of variance is performed to assess the overall goodness-of-fit and prediction accuracy of the established model. Then the results obtained are combined with structural dynamic response data to construct objective functions; furthermore, the optimal solution of parameter vector in the objective function is solved based on the ICA. Then damage positioning and quantification can be achieved according to location and degree of change in each parameter; finally, the RSM-ICA-based SDI method proposed is applied to damage identification of high-dimensional damaged simply-supported beam models. To verify the effectiveness of the proposed method, the damage identification results are compared with the results obtained from traditional optimization algorithms. The results indicate that: average errors in the structural stiffness parameters and natural frequency that are identified by the proposed method are 6.104% and 0.134% respectively. The RSM-ICA-based SDI method can more accurately identify the location and degree of damages with more significantly increased identification efficiency and better precision compared to traditional algorithms. This approach provides a novel means of solving SDI problems.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-022-17457-9
- https://www.nature.com/articles/s41598-022-17457-9.pdf
- OA Status
- gold
- Cited By
- 7
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4300817206
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4300817206Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41598-022-17457-9Digital Object Identifier
- Title
-
Solution to the problem of bridge structure damage identification by a response surface method and an imperialist competitive algorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-03Full publication date if available
- Authors
-
Dan Ye, Zhe Xu, Yangqing LiuList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-022-17457-9Publisher landing page
- PDF URL
-
https://www.nature.com/articles/s41598-022-17457-9.pdfDirect 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.nature.com/articles/s41598-022-17457-9.pdfDirect OA link when available
- Concepts
-
Response surface methodology, Latin hypercube sampling, Computer science, Identification (biology), Algorithm, Stiffness, Reduction (mathematics), Mathematical optimization, Mathematics, Statistics, Machine learning, Structural engineering, Engineering, Monte Carlo method, Geometry, Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 1, 2023: 3Per-year citation counts (last 5 years)
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4300817206 |
|---|---|
| doi | https://doi.org/10.1038/s41598-022-17457-9 |
| ids.doi | https://doi.org/10.1038/s41598-022-17457-9 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/36192491 |
| ids.openalex | https://openalex.org/W4300817206 |
| fwci | 1.01122889 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D000465 |
| mesh[0].is_major_topic | True |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Algorithms |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D008962 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Models, Theoretical |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D012107 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Research Design |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D000465 |
| mesh[3].is_major_topic | True |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Algorithms |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D008962 |
| mesh[4].is_major_topic | True |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Models, Theoretical |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D012107 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Research Design |
| type | article |
| title | Solution to the problem of bridge structure damage identification by a response surface method and an imperialist competitive algorithm |
| biblio.issue | 1 |
| biblio.volume | 12 |
| biblio.last_page | 16495 |
| biblio.first_page | 16495 |
| topics[0].id | https://openalex.org/T10534 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2205 |
| topics[0].subfield.display_name | Civil and Structural Engineering |
| topics[0].display_name | Structural Health Monitoring Techniques |
| topics[1].id | https://openalex.org/T10662 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9986000061035156 |
| 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 | Ultrasonics and Acoustic Wave Propagation |
| topics[2].id | https://openalex.org/T11606 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9977999925613403 |
| 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 | Infrastructure Maintenance and Monitoring |
| is_xpac | False |
| apc_list.value | 1890 |
| apc_list.currency | EUR |
| apc_list.value_usd | 2190 |
| apc_paid.value | 1890 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 2190 |
| concepts[0].id | https://openalex.org/C150077022 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6659719347953796 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q3136137 |
| concepts[0].display_name | Response surface methodology |
| concepts[1].id | https://openalex.org/C20820323 |
| concepts[1].level | 3 |
| concepts[1].score | 0.6217470169067383 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q6496514 |
| concepts[1].display_name | Latin hypercube sampling |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.5693612098693848 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C116834253 |
| concepts[3].level | 2 |
| concepts[3].score | 0.49232637882232666 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2039217 |
| concepts[3].display_name | Identification (biology) |
| concepts[4].id | https://openalex.org/C11413529 |
| concepts[4].level | 1 |
| concepts[4].score | 0.4661276340484619 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[4].display_name | Algorithm |
| concepts[5].id | https://openalex.org/C2779372316 |
| concepts[5].level | 2 |
| concepts[5].score | 0.46347910165786743 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q569057 |
| concepts[5].display_name | Stiffness |
| concepts[6].id | https://openalex.org/C111335779 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4520230293273926 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q3454686 |
| concepts[6].display_name | Reduction (mathematics) |
| concepts[7].id | https://openalex.org/C126255220 |
| concepts[7].level | 1 |
| concepts[7].score | 0.32024306058883667 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q141495 |
| concepts[7].display_name | Mathematical optimization |
| concepts[8].id | https://openalex.org/C33923547 |
| concepts[8].level | 0 |
| concepts[8].score | 0.2745274305343628 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[8].display_name | Mathematics |
| concepts[9].id | https://openalex.org/C105795698 |
| concepts[9].level | 1 |
| concepts[9].score | 0.1977424919605255 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[9].display_name | Statistics |
| concepts[10].id | https://openalex.org/C119857082 |
| concepts[10].level | 1 |
| concepts[10].score | 0.156376451253891 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[10].display_name | Machine learning |
| concepts[11].id | https://openalex.org/C66938386 |
| concepts[11].level | 1 |
| concepts[11].score | 0.14171665906906128 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q633538 |
| concepts[11].display_name | Structural engineering |
| concepts[12].id | https://openalex.org/C127413603 |
| concepts[12].level | 0 |
| concepts[12].score | 0.12244528532028198 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[12].display_name | Engineering |
| concepts[13].id | https://openalex.org/C19499675 |
| concepts[13].level | 2 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q232207 |
| concepts[13].display_name | Monte Carlo method |
| concepts[14].id | https://openalex.org/C2524010 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[14].display_name | Geometry |
| concepts[15].id | https://openalex.org/C86803240 |
| concepts[15].level | 0 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[15].display_name | Biology |
| concepts[16].id | https://openalex.org/C59822182 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q441 |
| concepts[16].display_name | Botany |
| keywords[0].id | https://openalex.org/keywords/response-surface-methodology |
| keywords[0].score | 0.6659719347953796 |
| keywords[0].display_name | Response surface methodology |
| keywords[1].id | https://openalex.org/keywords/latin-hypercube-sampling |
| keywords[1].score | 0.6217470169067383 |
| keywords[1].display_name | Latin hypercube sampling |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.5693612098693848 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/identification |
| keywords[3].score | 0.49232637882232666 |
| keywords[3].display_name | Identification (biology) |
| keywords[4].id | https://openalex.org/keywords/algorithm |
| keywords[4].score | 0.4661276340484619 |
| keywords[4].display_name | Algorithm |
| keywords[5].id | https://openalex.org/keywords/stiffness |
| keywords[5].score | 0.46347910165786743 |
| keywords[5].display_name | Stiffness |
| keywords[6].id | https://openalex.org/keywords/reduction |
| keywords[6].score | 0.4520230293273926 |
| keywords[6].display_name | Reduction (mathematics) |
| keywords[7].id | https://openalex.org/keywords/mathematical-optimization |
| keywords[7].score | 0.32024306058883667 |
| keywords[7].display_name | Mathematical optimization |
| keywords[8].id | https://openalex.org/keywords/mathematics |
| keywords[8].score | 0.2745274305343628 |
| keywords[8].display_name | Mathematics |
| keywords[9].id | https://openalex.org/keywords/statistics |
| keywords[9].score | 0.1977424919605255 |
| keywords[9].display_name | Statistics |
| keywords[10].id | https://openalex.org/keywords/machine-learning |
| keywords[10].score | 0.156376451253891 |
| keywords[10].display_name | Machine learning |
| keywords[11].id | https://openalex.org/keywords/structural-engineering |
| keywords[11].score | 0.14171665906906128 |
| keywords[11].display_name | Structural engineering |
| keywords[12].id | https://openalex.org/keywords/engineering |
| keywords[12].score | 0.12244528532028198 |
| keywords[12].display_name | Engineering |
| language | en |
| locations[0].id | doi:10.1038/s41598-022-17457-9 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S196734849 |
| locations[0].source.issn | 2045-2322 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2045-2322 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Scientific Reports |
| locations[0].source.host_organization | https://openalex.org/P4310319908 |
| locations[0].source.host_organization_name | Nature Portfolio |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| locations[0].source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.nature.com/articles/s41598-022-17457-9.pdf |
| 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 | Scientific Reports |
| locations[0].landing_page_url | https://doi.org/10.1038/s41598-022-17457-9 |
| locations[1].id | pmid:36192491 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| 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 | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Scientific reports |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/36192491 |
| locations[2].id | pmh:oai:doaj.org/article:cd832b8c380849cc89b71838e3f8eb2b |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | cc-by-sa |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Scientific Reports, Vol 12, Iss 1, Pp 1-13 (2022) |
| locations[2].landing_page_url | https://doaj.org/article/cd832b8c380849cc89b71838e3f8eb2b |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:9530241 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Sci Rep |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/9530241 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5100698326 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Dan Ye |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210116144 |
| authorships[0].affiliations[0].raw_affiliation_string | Chongqing University of Education, Chongqing, 400065, China |
| authorships[0].institutions[0].id | https://openalex.org/I4210116144 |
| authorships[0].institutions[0].ror | https://ror.org/02d06s578 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210116144 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Chongqing University of Education |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Dan Ye |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Chongqing University of Education, Chongqing, 400065, China |
| authorships[1].author.id | https://openalex.org/A5101459711 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-5157-570X |
| authorships[1].author.display_name | Zhe Xu |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210130035 |
| authorships[1].affiliations[0].raw_affiliation_string | Guangxi Communications Investiment Group Co., Ltd., Nanning, 530022, China |
| authorships[1].institutions[0].id | https://openalex.org/I4210130035 |
| authorships[1].institutions[0].ror | https://ror.org/03q3een69 |
| authorships[1].institutions[0].type | company |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210130035 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | China Communications Construction Company (China) |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Zhe Xu |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Guangxi Communications Investiment Group Co., Ltd., Nanning, 530022, China |
| authorships[2].author.id | https://openalex.org/A5008601516 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-2639-6467 |
| authorships[2].author.display_name | Yangqing Liu |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I63371133 |
| authorships[2].affiliations[0].raw_affiliation_string | State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing, 400074, China |
| authorships[2].institutions[0].id | https://openalex.org/I63371133 |
| authorships[2].institutions[0].ror | https://ror.org/01t001k65 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I63371133 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Chongqing Jiaotong University |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Yangqing Liu |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing, 400074, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.nature.com/articles/s41598-022-17457-9.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Solution to the problem of bridge structure damage identification by a response surface method and an imperialist competitive algorithm |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10534 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2205 |
| primary_topic.subfield.display_name | Civil and Structural Engineering |
| primary_topic.display_name | Structural Health Monitoring Techniques |
| related_works | https://openalex.org/W2364245233, https://openalex.org/W2154735538, https://openalex.org/W2218563287, https://openalex.org/W2980066635, https://openalex.org/W2091934641, https://openalex.org/W3147453129, https://openalex.org/W2068645708, https://openalex.org/W2771350702, https://openalex.org/W4300779338, https://openalex.org/W2132915183 |
| cited_by_count | 7 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 3 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 3 |
| locations_count | 4 |
| best_oa_location.id | doi:10.1038/s41598-022-17457-9 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S196734849 |
| best_oa_location.source.issn | 2045-2322 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2045-2322 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Scientific Reports |
| best_oa_location.source.host_organization | https://openalex.org/P4310319908 |
| best_oa_location.source.host_organization_name | Nature Portfolio |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.nature.com/articles/s41598-022-17457-9.pdf |
| 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 | Scientific Reports |
| best_oa_location.landing_page_url | https://doi.org/10.1038/s41598-022-17457-9 |
| primary_location.id | doi:10.1038/s41598-022-17457-9 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S196734849 |
| primary_location.source.issn | 2045-2322 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2045-2322 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Scientific Reports |
| primary_location.source.host_organization | https://openalex.org/P4310319908 |
| primary_location.source.host_organization_name | Nature Portfolio |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.nature.com/articles/s41598-022-17457-9.pdf |
| 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 | Scientific Reports |
| primary_location.landing_page_url | https://doi.org/10.1038/s41598-022-17457-9 |
| publication_date | 2022-10-03 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2955262734, https://openalex.org/W3171747289, https://openalex.org/W1968305192, https://openalex.org/W2043108941, https://openalex.org/W1965681560, https://openalex.org/W2810934605, https://openalex.org/W2944441070, https://openalex.org/W2560670234, https://openalex.org/W2301068158, https://openalex.org/W1968735445, https://openalex.org/W2005501515, https://openalex.org/W61573146, https://openalex.org/W2060203194, https://openalex.org/W1536597761, https://openalex.org/W1992529785, https://openalex.org/W1923144330, https://openalex.org/W2608821265, https://openalex.org/W4242186041, https://openalex.org/W2008034786, https://openalex.org/W1996226468, https://openalex.org/W1995958566, https://openalex.org/W4243858847, https://openalex.org/W2038796583, https://openalex.org/W2044555821, https://openalex.org/W2062516874, https://openalex.org/W2087347434, https://openalex.org/W2053847073, https://openalex.org/W2085404581, https://openalex.org/W4252576103, https://openalex.org/W2050964107, https://openalex.org/W2017818133, https://openalex.org/W2007535697, https://openalex.org/W2072256963, https://openalex.org/W2729689677, https://openalex.org/W3042464100, https://openalex.org/W2560769738, https://openalex.org/W2033011996, https://openalex.org/W2529659779, https://openalex.org/W2195747752, https://openalex.org/W3121254450 |
| referenced_works_count | 40 |
| abstract_inverted_index.a | 27, 40, 59, 239 |
| abstract_inverted_index.At | 38 |
| abstract_inverted_index.To | 1, 161 |
| abstract_inverted_index.an | 22, 33 |
| abstract_inverted_index.be | 132 |
| abstract_inverted_index.by | 200 |
| abstract_inverted_index.in | 116, 141, 189 |
| abstract_inverted_index.is | 45, 74, 80, 120, 150 |
| abstract_inverted_index.of | 5, 52, 78, 90, 113, 139, 155, 165, 221, 242 |
| abstract_inverted_index.on | 26, 56, 123 |
| abstract_inverted_index.to | 82, 105, 135, 152, 233 |
| abstract_inverted_index.SDI | 23, 147, 211, 244 |
| abstract_inverted_index.The | 183, 209 |
| abstract_inverted_index.and | 11, 13, 32, 50, 70, 87, 129, 137, 194, 206, 219, 229 |
| abstract_inverted_index.are | 98, 173, 198, 204 |
| abstract_inverted_index.can | 131, 213 |
| abstract_inverted_index.for | 47, 65 |
| abstract_inverted_index.the | 3, 66, 84, 91, 95, 110, 117, 124, 145, 163, 166, 169, 176, 190, 201, 217 |
| abstract_inverted_index.ICA. | 125 |
| abstract_inverted_index.RSM. | 57 |
| abstract_inverted_index.Then | 94, 126 |
| abstract_inverted_index.This | 236 |
| abstract_inverted_index.beam | 159 |
| abstract_inverted_index.data | 104 |
| abstract_inverted_index.each | 142 |
| abstract_inverted_index.from | 179 |
| abstract_inverted_index.more | 214, 224 |
| abstract_inverted_index.that | 197 |
| abstract_inverted_index.this | 19 |
| abstract_inverted_index.used | 46 |
| abstract_inverted_index.with | 100, 175, 223 |
| abstract_inverted_index.(RSM) | 31 |
| abstract_inverted_index.(SDI) | 9 |
| abstract_inverted_index.Latin | 41 |
| abstract_inverted_index.Then, | 58 |
| abstract_inverted_index.based | 25, 55, 122 |
| abstract_inverted_index.means | 241 |
| abstract_inverted_index.model | 64 |
| abstract_inverted_index.novel | 240 |
| abstract_inverted_index.that: | 186 |
| abstract_inverted_index.(ICA). | 37 |
| abstract_inverted_index.0.134% | 207 |
| abstract_inverted_index.6.104% | 205 |
| abstract_inverted_index.assess | 83 |
| abstract_inverted_index.better | 230 |
| abstract_inverted_index.change | 140 |
| abstract_inverted_index.damage | 7, 127, 153, 170 |
| abstract_inverted_index.degree | 138, 220 |
| abstract_inverted_index.design | 43, 49 |
| abstract_inverted_index.detect | 15 |
| abstract_inverted_index.errors | 188 |
| abstract_inverted_index.factor | 73 |
| abstract_inverted_index.first, | 39 |
| abstract_inverted_index.method | 24, 30, 44, 148, 203, 212 |
| abstract_inverted_index.model. | 93 |
| abstract_inverted_index.points | 54 |
| abstract_inverted_index.sample | 53 |
| abstract_inverted_index.solved | 121 |
| abstract_inverted_index.target | 67 |
| abstract_inverted_index.vector | 115 |
| abstract_inverted_index.verify | 162 |
| abstract_inverted_index.applied | 151 |
| abstract_inverted_index.average | 187 |
| abstract_inverted_index.damage, | 18 |
| abstract_inverted_index.damaged | 157 |
| abstract_inverted_index.damages | 222 |
| abstract_inverted_index.dynamic | 102 |
| abstract_inverted_index.initial | 16 |
| abstract_inverted_index.method, | 168 |
| abstract_inverted_index.methods | 10 |
| abstract_inverted_index.models. | 160 |
| abstract_inverted_index.natural | 195 |
| abstract_inverted_index.optimal | 111 |
| abstract_inverted_index.overall | 85 |
| abstract_inverted_index.results | 96, 172, 177, 184 |
| abstract_inverted_index.solving | 243 |
| abstract_inverted_index.surface | 29, 62 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Finally, | 76 |
| abstract_inverted_index.accuracy | 89 |
| abstract_inverted_index.achieved | 133 |
| abstract_inverted_index.analysis | 77 |
| abstract_inverted_index.approach | 237 |
| abstract_inverted_index.combined | 99 |
| abstract_inverted_index.compared | 174, 232 |
| abstract_inverted_index.develops | 21 |
| abstract_inverted_index.finally, | 144 |
| abstract_inverted_index.function | 119 |
| abstract_inverted_index.identify | 216 |
| abstract_inverted_index.increase | 2 |
| abstract_inverted_index.indicate | 185 |
| abstract_inverted_index.location | 136, 218 |
| abstract_inverted_index.obtained | 97, 178 |
| abstract_inverted_index.proposed | 149, 167, 202 |
| abstract_inverted_index.provides | 238 |
| abstract_inverted_index.research | 20 |
| abstract_inverted_index.response | 28, 61, 69, 103 |
| abstract_inverted_index.solution | 112 |
| abstract_inverted_index.variance | 79 |
| abstract_inverted_index.according | 134 |
| abstract_inverted_index.algorithm | 36 |
| abstract_inverted_index.construct | 106 |
| abstract_inverted_index.frequency | 68, 196 |
| abstract_inverted_index.hypercube | 42 |
| abstract_inverted_index.increased | 226 |
| abstract_inverted_index.objective | 107, 118 |
| abstract_inverted_index.parameter | 114 |
| abstract_inverted_index.performed | 81 |
| abstract_inverted_index.precision | 231 |
| abstract_inverted_index.problems. | 245 |
| abstract_inverted_index.reduction | 72 |
| abstract_inverted_index.selection | 51 |
| abstract_inverted_index.stiffness | 71, 192 |
| abstract_inverted_index.surrogate | 63 |
| abstract_inverted_index.timeously | 12 |
| abstract_inverted_index.accurately | 14, 215 |
| abstract_inverted_index.efficiency | 4, 228 |
| abstract_inverted_index.functions; | 108 |
| abstract_inverted_index.high-order | 60 |
| abstract_inverted_index.identified | 199 |
| abstract_inverted_index.parameter; | 143 |
| abstract_inverted_index.parameters | 193 |
| abstract_inverted_index.prediction | 88 |
| abstract_inverted_index.structural | 6, 17, 101, 191 |
| abstract_inverted_index.algorithms. | 182, 235 |
| abstract_inverted_index.competitive | 35 |
| abstract_inverted_index.established | 92 |
| abstract_inverted_index.imperialist | 34 |
| abstract_inverted_index.positioning | 128 |
| abstract_inverted_index.traditional | 180, 234 |
| abstract_inverted_index.established. | 75 |
| abstract_inverted_index.experimental | 48 |
| abstract_inverted_index.furthermore, | 109 |
| abstract_inverted_index.optimization | 181 |
| abstract_inverted_index.RSM-ICA-based | 146, 210 |
| abstract_inverted_index.effectiveness | 164 |
| abstract_inverted_index.respectively. | 208 |
| abstract_inverted_index.significantly | 225 |
| abstract_inverted_index.identification | 8, 154, 171, 227 |
| abstract_inverted_index.quantification | 130 |
| abstract_inverted_index.goodness-of-fit | 86 |
| abstract_inverted_index.high-dimensional | 156 |
| abstract_inverted_index.simply-supported | 158 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5100698326 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I4210116144 |
| citation_normalized_percentile.value | 0.69407946 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |