Machine learning classifier-based dynamic surrogate model for structural reliability analysis Article Swipe
Guoshao Su
,
Weizhe Sun
,
Ying Zhao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1080/15732479.2023.2218847
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1080/15732479.2023.2218847
The performance functions of large-scale complex structures are often implicit and nonlinear, which leads to problems such as high computational costs and low computational accuracy in reliability calculation. In this regard, a dynamic machine learning classifier surrogate model based on Monte Carlo simulation (DMLC-MCS) is proposed. The training sample points are generated by the Markov chain Monte Carlo (MCMC) method and numerical analysis to create the training sample dataset, and the surrogate model based on machine learning classifiers (MLCs) are used to reconstruct the limit state function (LSF). Then, samples are extracted by MCS technique, and the LSF values are predicted by the trained surrogate model. An iterative process is proposed around the most probable point (MPP), and the failure probability obtained by the MCS technique is taken as the convergence condition. If the convergence condition is not satisfied, the MPP information is added to the original sample set to refine the surrogate model. Compared with the traditional reliability method, the proposed method significantly reduces the computational cost on the premise of ensuring high accuracy. In addition, the method is easy to combine with numerical analysis and is proven to be applicable for reliability analysis of real-world complex engineering problems.
Related Topics
Concepts
Surrogate model
Markov chain Monte Carlo
Computer science
Monte Carlo method
Classifier (UML)
Reliability (semiconductor)
Machine learning
Artificial intelligence
Convergence (economics)
Algorithm
Uncertainty quantification
Mathematical optimization
Mathematics
Statistics
Bayesian probability
Quantum mechanics
Economic growth
Economics
Power (physics)
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/15732479.2023.2218847
- OA Status
- green
- Cited By
- 5
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4379515934
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4379515934Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1080/15732479.2023.2218847Digital Object Identifier
- Title
-
Machine learning classifier-based dynamic surrogate model for structural reliability analysisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-05Full publication date if available
- Authors
-
Guoshao Su, Weizhe Sun, Ying ZhaoList of authors in order
- Landing page
-
https://doi.org/10.1080/15732479.2023.2218847Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://figshare.com/articles/journal_contribution/Machine_learning_classifier-based_dynamic_surrogate_model_for_structural_reliability_analysis/23300427Direct OA link when available
- Concepts
-
Surrogate model, Markov chain Monte Carlo, Computer science, Monte Carlo method, Classifier (UML), Reliability (semiconductor), Machine learning, Artificial intelligence, Convergence (economics), Algorithm, Uncertainty quantification, Mathematical optimization, Mathematics, Statistics, Bayesian probability, Quantum mechanics, Economic growth, Economics, Power (physics), PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
54Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4379515934 |
|---|---|
| doi | https://doi.org/10.1080/15732479.2023.2218847 |
| ids.doi | https://doi.org/10.6084/m9.figshare.23300427.v1 |
| ids.openalex | https://openalex.org/W4379515934 |
| fwci | 1.77917391 |
| type | article |
| title | Machine learning classifier-based dynamic surrogate model for structural reliability analysis |
| biblio.issue | 3 |
| biblio.volume | 21 |
| biblio.last_page | 537 |
| biblio.first_page | 525 |
| topics[0].id | https://openalex.org/T10928 |
| topics[0].field.id | https://openalex.org/fields/18 |
| topics[0].field.display_name | Decision Sciences |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/2 |
| topics[0].domain.display_name | Social Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1804 |
| topics[0].subfield.display_name | Statistics, Probability and Uncertainty |
| topics[0].display_name | Probabilistic and Robust Engineering Design |
| 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 | 0.9904000163078308 |
| 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/T11850 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.982200026512146 |
| 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 | Concrete Corrosion and Durability |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C131675550 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8953008055686951 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q7646884 |
| concepts[0].display_name | Surrogate model |
| concepts[1].id | https://openalex.org/C111350023 |
| concepts[1].level | 3 |
| concepts[1].score | 0.6573416590690613 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1191869 |
| concepts[1].display_name | Markov chain Monte Carlo |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.6350542306900024 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C19499675 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5730832815170288 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q232207 |
| concepts[3].display_name | Monte Carlo method |
| concepts[4].id | https://openalex.org/C95623464 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5296825170516968 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1096149 |
| concepts[4].display_name | Classifier (UML) |
| concepts[5].id | https://openalex.org/C43214815 |
| concepts[5].level | 3 |
| concepts[5].score | 0.49871277809143066 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7310987 |
| concepts[5].display_name | Reliability (semiconductor) |
| concepts[6].id | https://openalex.org/C119857082 |
| concepts[6].level | 1 |
| concepts[6].score | 0.49297189712524414 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[6].display_name | Machine learning |
| concepts[7].id | https://openalex.org/C154945302 |
| concepts[7].level | 1 |
| concepts[7].score | 0.4916391968727112 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[7].display_name | Artificial intelligence |
| concepts[8].id | https://openalex.org/C2777303404 |
| concepts[8].level | 2 |
| concepts[8].score | 0.46728071570396423 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q759757 |
| concepts[8].display_name | Convergence (economics) |
| concepts[9].id | https://openalex.org/C11413529 |
| concepts[9].level | 1 |
| concepts[9].score | 0.4547116756439209 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[9].display_name | Algorithm |
| concepts[10].id | https://openalex.org/C32230216 |
| concepts[10].level | 2 |
| concepts[10].score | 0.41180139780044556 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7882499 |
| concepts[10].display_name | Uncertainty quantification |
| concepts[11].id | https://openalex.org/C126255220 |
| concepts[11].level | 1 |
| concepts[11].score | 0.3505837917327881 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q141495 |
| concepts[11].display_name | Mathematical optimization |
| concepts[12].id | https://openalex.org/C33923547 |
| concepts[12].level | 0 |
| concepts[12].score | 0.19580423831939697 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[12].display_name | Mathematics |
| concepts[13].id | https://openalex.org/C105795698 |
| concepts[13].level | 1 |
| concepts[13].score | 0.10933679342269897 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[13].display_name | Statistics |
| concepts[14].id | https://openalex.org/C107673813 |
| concepts[14].level | 2 |
| concepts[14].score | 0.08784234523773193 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q812534 |
| concepts[14].display_name | Bayesian probability |
| concepts[15].id | https://openalex.org/C62520636 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[15].display_name | Quantum mechanics |
| concepts[16].id | https://openalex.org/C50522688 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q189833 |
| concepts[16].display_name | Economic growth |
| concepts[17].id | https://openalex.org/C162324750 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[17].display_name | Economics |
| concepts[18].id | https://openalex.org/C163258240 |
| concepts[18].level | 2 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q25342 |
| concepts[18].display_name | Power (physics) |
| concepts[19].id | https://openalex.org/C121332964 |
| concepts[19].level | 0 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[19].display_name | Physics |
| keywords[0].id | https://openalex.org/keywords/surrogate-model |
| keywords[0].score | 0.8953008055686951 |
| keywords[0].display_name | Surrogate model |
| keywords[1].id | https://openalex.org/keywords/markov-chain-monte-carlo |
| keywords[1].score | 0.6573416590690613 |
| keywords[1].display_name | Markov chain Monte Carlo |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.6350542306900024 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/monte-carlo-method |
| keywords[3].score | 0.5730832815170288 |
| keywords[3].display_name | Monte Carlo method |
| keywords[4].id | https://openalex.org/keywords/classifier |
| keywords[4].score | 0.5296825170516968 |
| keywords[4].display_name | Classifier (UML) |
| keywords[5].id | https://openalex.org/keywords/reliability |
| keywords[5].score | 0.49871277809143066 |
| keywords[5].display_name | Reliability (semiconductor) |
| keywords[6].id | https://openalex.org/keywords/machine-learning |
| keywords[6].score | 0.49297189712524414 |
| keywords[6].display_name | Machine learning |
| keywords[7].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[7].score | 0.4916391968727112 |
| keywords[7].display_name | Artificial intelligence |
| keywords[8].id | https://openalex.org/keywords/convergence |
| keywords[8].score | 0.46728071570396423 |
| keywords[8].display_name | Convergence (economics) |
| keywords[9].id | https://openalex.org/keywords/algorithm |
| keywords[9].score | 0.4547116756439209 |
| keywords[9].display_name | Algorithm |
| keywords[10].id | https://openalex.org/keywords/uncertainty-quantification |
| keywords[10].score | 0.41180139780044556 |
| keywords[10].display_name | Uncertainty quantification |
| keywords[11].id | https://openalex.org/keywords/mathematical-optimization |
| keywords[11].score | 0.3505837917327881 |
| keywords[11].display_name | Mathematical optimization |
| keywords[12].id | https://openalex.org/keywords/mathematics |
| keywords[12].score | 0.19580423831939697 |
| keywords[12].display_name | Mathematics |
| keywords[13].id | https://openalex.org/keywords/statistics |
| keywords[13].score | 0.10933679342269897 |
| keywords[13].display_name | Statistics |
| keywords[14].id | https://openalex.org/keywords/bayesian-probability |
| keywords[14].score | 0.08784234523773193 |
| keywords[14].display_name | Bayesian probability |
| language | en |
| locations[0].id | doi:10.1080/15732479.2023.2218847 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S28058569 |
| locations[0].source.issn | 1573-2479, 1744-8980 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1573-2479 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Structure and Infrastructure Engineering |
| locations[0].source.host_organization | https://openalex.org/P4310320547 |
| locations[0].source.host_organization_name | Taylor & Francis |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320547 |
| locations[0].source.host_organization_lineage_names | Taylor & Francis |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Structure and Infrastructure Engineering |
| locations[0].landing_page_url | https://doi.org/10.1080/15732479.2023.2218847 |
| locations[1].id | pmh:oai:figshare.com:article/23300427 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400572 |
| 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 | OPAL (Open@LaTrobe) (La Trobe University) |
| locations[1].source.host_organization | https://openalex.org/I196829312 |
| locations[1].source.host_organization_name | La Trobe University |
| locations[1].source.host_organization_lineage | https://openalex.org/I196829312 |
| 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 | |
| locations[1].landing_page_url | https://figshare.com/articles/journal_contribution/Machine_learning_classifier-based_dynamic_surrogate_model_for_structural_reliability_analysis/23300427 |
| locations[2].id | doi:10.6084/m9.figshare.23300427.v1 |
| locations[2].is_oa | True |
| locations[2].source | |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | |
| locations[2].raw_type | article-journal |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | https://doi.org/10.6084/m9.figshare.23300427.v1 |
| indexed_in | crossref, datacite |
| authorships[0].author.id | https://openalex.org/A5039750959 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-9506-6827 |
| authorships[0].author.display_name | Guoshao Su |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I150807315 |
| authorships[0].affiliations[0].raw_affiliation_string | Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, Guangxi University, Nanning, China |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I150807315 |
| authorships[0].affiliations[1].raw_affiliation_string | Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, School of Civil and Architecture Engineering, Guangxi University, Nanning, China |
| authorships[0].affiliations[2].institution_ids | https://openalex.org/I150807315 |
| authorships[0].affiliations[2].raw_affiliation_string | Guangxi Provincial Engineering Research Center of Water Security and Intelligent Control for Karst Region, Guangxi University, Nanning, China |
| authorships[0].institutions[0].id | https://openalex.org/I150807315 |
| authorships[0].institutions[0].ror | https://ror.org/02c9qn167 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I150807315 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Guangxi University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Guoshao Su |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, Guangxi University, Nanning, China, Guangxi Provincial Engineering Research Center of Water Security and Intelligent Control for Karst Region, Guangxi University, Nanning, China, Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, School of Civil and Architecture Engineering, Guangxi University, Nanning, China |
| authorships[1].author.id | https://openalex.org/A5020082003 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-4905-075X |
| authorships[1].author.display_name | Weizhe Sun |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I150807315 |
| authorships[1].affiliations[0].raw_affiliation_string | Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, School of Civil and Architecture Engineering, Guangxi University, Nanning, China |
| authorships[1].institutions[0].id | https://openalex.org/I150807315 |
| authorships[1].institutions[0].ror | https://ror.org/02c9qn167 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I150807315 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Guangxi University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Weizhe Sun |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, School of Civil and Architecture Engineering, Guangxi University, Nanning, China |
| authorships[2].author.id | https://openalex.org/A5088018488 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-8350-4033 |
| authorships[2].author.display_name | Ying Zhao |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I150807315 |
| authorships[2].affiliations[0].raw_affiliation_string | Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, School of Civil and Architecture Engineering, Guangxi University, Nanning, China |
| authorships[2].institutions[0].id | https://openalex.org/I150807315 |
| authorships[2].institutions[0].ror | https://ror.org/02c9qn167 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I150807315 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Guangxi University |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Ying Zhao |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, School of Civil and Architecture Engineering, Guangxi University, Nanning, China |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://figshare.com/articles/journal_contribution/Machine_learning_classifier-based_dynamic_surrogate_model_for_structural_reliability_analysis/23300427 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Machine learning classifier-based dynamic surrogate model for structural reliability analysis |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10928 |
| primary_topic.field.id | https://openalex.org/fields/18 |
| primary_topic.field.display_name | Decision Sciences |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/2 |
| primary_topic.domain.display_name | Social Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1804 |
| primary_topic.subfield.display_name | Statistics, Probability and Uncertainty |
| primary_topic.display_name | Probabilistic and Robust Engineering Design |
| related_works | https://openalex.org/W2151689585, https://openalex.org/W4388965878, https://openalex.org/W2911578761, https://openalex.org/W3133763041, https://openalex.org/W2791576819, https://openalex.org/W3201166396, https://openalex.org/W4312678460, https://openalex.org/W2790762380, https://openalex.org/W4388873753, https://openalex.org/W3082898132 |
| cited_by_count | 5 |
| 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 | 2 |
| locations_count | 3 |
| best_oa_location.id | pmh:oai:figshare.com:article/23300427 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400572 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | OPAL (Open@LaTrobe) (La Trobe University) |
| best_oa_location.source.host_organization | https://openalex.org/I196829312 |
| best_oa_location.source.host_organization_name | La Trobe University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I196829312 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | Text |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://figshare.com/articles/journal_contribution/Machine_learning_classifier-based_dynamic_surrogate_model_for_structural_reliability_analysis/23300427 |
| primary_location.id | doi:10.1080/15732479.2023.2218847 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S28058569 |
| primary_location.source.issn | 1573-2479, 1744-8980 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1573-2479 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Structure and Infrastructure Engineering |
| primary_location.source.host_organization | https://openalex.org/P4310320547 |
| primary_location.source.host_organization_name | Taylor & Francis |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320547 |
| primary_location.source.host_organization_lineage_names | Taylor & Francis |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Structure and Infrastructure Engineering |
| primary_location.landing_page_url | https://doi.org/10.1080/15732479.2023.2218847 |
| publication_date | 2023-06-05 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2066884548, https://openalex.org/W1902835416, https://openalex.org/W6991957103, https://openalex.org/W3111626061, https://openalex.org/W2068892839, https://openalex.org/W3122431067, https://openalex.org/W2067829701, https://openalex.org/W4242186041, https://openalex.org/W2008034786, https://openalex.org/W4281702620, https://openalex.org/W2085351890, https://openalex.org/W1990244371, https://openalex.org/W2075017033, https://openalex.org/W1991548802, https://openalex.org/W2791467530, https://openalex.org/W2007560771, https://openalex.org/W2038726880, https://openalex.org/W2791551166, https://openalex.org/W2215569344, https://openalex.org/W2077951942, https://openalex.org/W2136132422, https://openalex.org/W3171148293, https://openalex.org/W2003986068, https://openalex.org/W1981567151, https://openalex.org/W2085404581, https://openalex.org/W2002106843, https://openalex.org/W2022044805, https://openalex.org/W2066722671, https://openalex.org/W4286495459, https://openalex.org/W2963105841, https://openalex.org/W2321654184, https://openalex.org/W2016312558, https://openalex.org/W1994005439, https://openalex.org/W4243645092, https://openalex.org/W1495049647, https://openalex.org/W2188591201, https://openalex.org/W4236137412, https://openalex.org/W2080735494, https://openalex.org/W2118641598, https://openalex.org/W2728779792, https://openalex.org/W2732319205, https://openalex.org/W3108173890, https://openalex.org/W2404504846, https://openalex.org/W2962574691, https://openalex.org/W2943160824, https://openalex.org/W2156909104, https://openalex.org/W4283124025, https://openalex.org/W2975091471, https://openalex.org/W2963839761, https://openalex.org/W2220407229, https://openalex.org/W4247215908, https://openalex.org/W4239275769, https://openalex.org/W4206686222, https://openalex.org/W4292872700 |
| referenced_works_count | 54 |
| abstract_inverted_index.a | 31 |
| abstract_inverted_index.An | 106 |
| abstract_inverted_index.If | 132 |
| abstract_inverted_index.In | 28, 175 |
| abstract_inverted_index.as | 17, 128 |
| abstract_inverted_index.be | 190 |
| abstract_inverted_index.by | 52, 92, 101, 122 |
| abstract_inverted_index.in | 25 |
| abstract_inverted_index.is | 44, 109, 126, 136, 142, 179, 187 |
| abstract_inverted_index.of | 3, 171, 195 |
| abstract_inverted_index.on | 39, 74, 168 |
| abstract_inverted_index.to | 14, 63, 81, 144, 149, 181, 189 |
| abstract_inverted_index.LSF | 97 |
| abstract_inverted_index.MCS | 93, 124 |
| abstract_inverted_index.MPP | 140 |
| abstract_inverted_index.The | 0, 46 |
| abstract_inverted_index.and | 10, 21, 60, 69, 95, 117, 186 |
| abstract_inverted_index.are | 7, 50, 79, 90, 99 |
| abstract_inverted_index.for | 192 |
| abstract_inverted_index.low | 22 |
| abstract_inverted_index.not | 137 |
| abstract_inverted_index.set | 148 |
| abstract_inverted_index.the | 53, 65, 70, 83, 96, 102, 112, 118, 123, 129, 133, 139, 145, 151, 156, 160, 165, 169, 177 |
| abstract_inverted_index.cost | 167 |
| abstract_inverted_index.easy | 180 |
| abstract_inverted_index.high | 18, 173 |
| abstract_inverted_index.most | 113 |
| abstract_inverted_index.such | 16 |
| abstract_inverted_index.this | 29 |
| abstract_inverted_index.used | 80 |
| abstract_inverted_index.with | 155, 183 |
| abstract_inverted_index.Carlo | 41, 57 |
| abstract_inverted_index.Monte | 40, 56 |
| abstract_inverted_index.Then, | 88 |
| abstract_inverted_index.added | 143 |
| abstract_inverted_index.based | 38, 73 |
| abstract_inverted_index.chain | 55 |
| abstract_inverted_index.costs | 20 |
| abstract_inverted_index.leads | 13 |
| abstract_inverted_index.limit | 84 |
| abstract_inverted_index.model | 37, 72 |
| abstract_inverted_index.often | 8 |
| abstract_inverted_index.point | 115 |
| abstract_inverted_index.state | 85 |
| abstract_inverted_index.taken | 127 |
| abstract_inverted_index.which | 12 |
| abstract_inverted_index.(LSF). | 87 |
| abstract_inverted_index.(MCMC) | 58 |
| abstract_inverted_index.(MLCs) | 78 |
| abstract_inverted_index.(MPP), | 116 |
| abstract_inverted_index.Markov | 54 |
| abstract_inverted_index.around | 111 |
| abstract_inverted_index.create | 64 |
| abstract_inverted_index.method | 59, 162, 178 |
| abstract_inverted_index.model. | 105, 153 |
| abstract_inverted_index.points | 49 |
| abstract_inverted_index.proven | 188 |
| abstract_inverted_index.refine | 150 |
| abstract_inverted_index.sample | 48, 67, 147 |
| abstract_inverted_index.values | 98 |
| abstract_inverted_index.combine | 182 |
| abstract_inverted_index.complex | 5, 197 |
| abstract_inverted_index.dynamic | 32 |
| abstract_inverted_index.failure | 119 |
| abstract_inverted_index.machine | 33, 75 |
| abstract_inverted_index.method, | 159 |
| abstract_inverted_index.premise | 170 |
| abstract_inverted_index.process | 108 |
| abstract_inverted_index.reduces | 164 |
| abstract_inverted_index.regard, | 30 |
| abstract_inverted_index.samples | 89 |
| abstract_inverted_index.trained | 103 |
| abstract_inverted_index.Compared | 154 |
| abstract_inverted_index.accuracy | 24 |
| abstract_inverted_index.analysis | 62, 185, 194 |
| abstract_inverted_index.dataset, | 68 |
| abstract_inverted_index.ensuring | 172 |
| abstract_inverted_index.function | 86 |
| abstract_inverted_index.implicit | 9 |
| abstract_inverted_index.learning | 34, 76 |
| abstract_inverted_index.obtained | 121 |
| abstract_inverted_index.original | 146 |
| abstract_inverted_index.probable | 114 |
| abstract_inverted_index.problems | 15 |
| abstract_inverted_index.proposed | 110, 161 |
| abstract_inverted_index.training | 47, 66 |
| abstract_inverted_index.accuracy. | 174 |
| abstract_inverted_index.addition, | 176 |
| abstract_inverted_index.condition | 135 |
| abstract_inverted_index.extracted | 91 |
| abstract_inverted_index.functions | 2 |
| abstract_inverted_index.generated | 51 |
| abstract_inverted_index.iterative | 107 |
| abstract_inverted_index.numerical | 61, 184 |
| abstract_inverted_index.predicted | 100 |
| abstract_inverted_index.problems. | 199 |
| abstract_inverted_index.proposed. | 45 |
| abstract_inverted_index.surrogate | 36, 71, 104, 152 |
| abstract_inverted_index.technique | 125 |
| abstract_inverted_index.(DMLC-MCS) | 43 |
| abstract_inverted_index.applicable | 191 |
| abstract_inverted_index.classifier | 35 |
| abstract_inverted_index.condition. | 131 |
| abstract_inverted_index.nonlinear, | 11 |
| abstract_inverted_index.real-world | 196 |
| abstract_inverted_index.satisfied, | 138 |
| abstract_inverted_index.simulation | 42 |
| abstract_inverted_index.structures | 6 |
| abstract_inverted_index.technique, | 94 |
| abstract_inverted_index.classifiers | 77 |
| abstract_inverted_index.convergence | 130, 134 |
| abstract_inverted_index.engineering | 198 |
| abstract_inverted_index.information | 141 |
| abstract_inverted_index.large-scale | 4 |
| abstract_inverted_index.performance | 1 |
| abstract_inverted_index.probability | 120 |
| abstract_inverted_index.reconstruct | 82 |
| abstract_inverted_index.reliability | 26, 158, 193 |
| abstract_inverted_index.traditional | 157 |
| abstract_inverted_index.calculation. | 27 |
| abstract_inverted_index.computational | 19, 23, 166 |
| abstract_inverted_index.significantly | 163 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5039750959 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I150807315 |
| citation_normalized_percentile.value | 0.83723669 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |