Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques Article Swipe
Mohammed Amin Benbouras
,
Alexandru-Ionuţ Petrişor
,
Hamma Zedira
,
Laala Ghelani
,
Lina Lefilef
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/app112210908
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/app112210908
Estimating the bearing capacity of piles is an essential point when seeking for safe and economic geotechnical structures. However, the traditional methods employed in this estimation are time-consuming and costly. The current study aims at elaborating a new alternative model for predicting the pile-bearing capacity based on eleven new advanced machine-learning methods in order to overcome these limitations. The modeling phase used a database of 100 samples collected from different countries. Additionally, eight relevant factors were selected in the input layer based on the literature recommendations. The optimal inputs were modeled using the machine-learning methods and their performance was assessed through six performance measures using a K-fold cross-validation approach. The comparative study proved the effectiveness of the DNN model, which displayed a higher performance in predicting the pile-bearing capacity. This elaborated model provided the optimal prediction, i.e., the closest to the experimental values, compared to the other models and formulae proposed by previous studies. Finally, a reliable and easy-to-use graphical interface was generated, namely “BeaCa2021”. This will be very helpful for researchers and civil engineers when estimating the pile-bearing capacity, with the advantage of saving time and money.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app112210908
- https://www.mdpi.com/2076-3417/11/22/10908/pdf?version=1637234478
- OA Status
- gold
- Cited By
- 44
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3211512232
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3211512232Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app112210908Digital Object Identifier
- Title
-
Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning TechniquesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-18Full publication date if available
- Authors
-
Mohammed Amin Benbouras, Alexandru-Ionuţ Petrişor, Hamma Zedira, Laala Ghelani, Lina LefilefList of authors in order
- Landing page
-
https://doi.org/10.3390/app112210908Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/11/22/10908/pdf?version=1637234478Direct 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/2076-3417/11/22/10908/pdf?version=1637234478Direct OA link when available
- Concepts
-
Bearing capacity, Pile, Computer science, Machine learning, Point (geometry), Artificial intelligence, Engineering, Structural engineering, Mathematics, Algorithm, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
44Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 10, 2024: 19, 2023: 8, 2022: 6, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
56Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3211512232 |
|---|---|
| doi | https://doi.org/10.3390/app112210908 |
| ids.doi | https://doi.org/10.3390/app112210908 |
| ids.mag | 3211512232 |
| ids.openalex | https://openalex.org/W3211512232 |
| fwci | 5.72761986 |
| type | article |
| title | Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques |
| biblio.issue | 22 |
| biblio.volume | 11 |
| biblio.last_page | 10908 |
| biblio.first_page | 10908 |
| topics[0].id | https://openalex.org/T11046 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9993000030517578 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2213 |
| topics[0].subfield.display_name | Safety, Risk, Reliability and Quality |
| topics[0].display_name | Geotechnical Engineering and Analysis |
| topics[1].id | https://openalex.org/T10233 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9987999796867371 |
| 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 | Geotechnical Engineering and Soil Mechanics |
| topics[2].id | https://openalex.org/T12233 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9980999827384949 |
| 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 | Geotechnical Engineering and Underground Structures |
| is_xpac | False |
| apc_list.value | 2300 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2490 |
| apc_paid.value | 2300 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2490 |
| concepts[0].id | https://openalex.org/C135677250 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6869292259216309 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q3319673 |
| concepts[0].display_name | Bearing capacity |
| concepts[1].id | https://openalex.org/C119560385 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6395962238311768 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q7193850 |
| concepts[1].display_name | Pile |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.588183581829071 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C119857082 |
| concepts[3].level | 1 |
| concepts[3].score | 0.4801004230976105 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[3].display_name | Machine learning |
| concepts[4].id | https://openalex.org/C28719098 |
| concepts[4].level | 2 |
| concepts[4].score | 0.45280197262763977 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q44946 |
| concepts[4].display_name | Point (geometry) |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.3868982791900635 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C127413603 |
| concepts[6].level | 0 |
| concepts[6].score | 0.31512725353240967 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[6].display_name | Engineering |
| concepts[7].id | https://openalex.org/C66938386 |
| concepts[7].level | 1 |
| concepts[7].score | 0.1435548961162567 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q633538 |
| concepts[7].display_name | Structural engineering |
| concepts[8].id | https://openalex.org/C33923547 |
| concepts[8].level | 0 |
| concepts[8].score | 0.12032482028007507 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[8].display_name | Mathematics |
| concepts[9].id | https://openalex.org/C11413529 |
| concepts[9].level | 1 |
| concepts[9].score | 0.10659351944923401 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[9].display_name | Algorithm |
| concepts[10].id | https://openalex.org/C2524010 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[10].display_name | Geometry |
| keywords[0].id | https://openalex.org/keywords/bearing-capacity |
| keywords[0].score | 0.6869292259216309 |
| keywords[0].display_name | Bearing capacity |
| keywords[1].id | https://openalex.org/keywords/pile |
| keywords[1].score | 0.6395962238311768 |
| keywords[1].display_name | Pile |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.588183581829071 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/machine-learning |
| keywords[3].score | 0.4801004230976105 |
| keywords[3].display_name | Machine learning |
| keywords[4].id | https://openalex.org/keywords/point |
| keywords[4].score | 0.45280197262763977 |
| keywords[4].display_name | Point (geometry) |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.3868982791900635 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/engineering |
| keywords[6].score | 0.31512725353240967 |
| keywords[6].display_name | Engineering |
| keywords[7].id | https://openalex.org/keywords/structural-engineering |
| keywords[7].score | 0.1435548961162567 |
| keywords[7].display_name | Structural engineering |
| keywords[8].id | https://openalex.org/keywords/mathematics |
| keywords[8].score | 0.12032482028007507 |
| keywords[8].display_name | Mathematics |
| keywords[9].id | https://openalex.org/keywords/algorithm |
| keywords[9].score | 0.10659351944923401 |
| keywords[9].display_name | Algorithm |
| language | en |
| locations[0].id | doi:10.3390/app112210908 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210205812 |
| locations[0].source.issn | 2076-3417 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2076-3417 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Applied Sciences |
| 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].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/2076-3417/11/22/10908/pdf?version=1637234478 |
| 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 | Applied Sciences |
| locations[0].landing_page_url | https://doi.org/10.3390/app112210908 |
| locations[1].id | pmh:oai:doaj.org/article:87b2eb427c144d3aaf2d2b429c5d7c07 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].source.host_organization_lineage | |
| locations[1].license | cc-by-sa |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Applied Sciences, Vol 11, Iss 22, p 10908 (2021) |
| locations[1].landing_page_url | https://doaj.org/article/87b2eb427c144d3aaf2d2b429c5d7c07 |
| locations[2].id | pmh:oai:mdpi.com:/2076-3417/11/22/10908/ |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400947 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | MDPI (MDPI AG) |
| locations[2].source.host_organization | https://openalex.org/I4210097602 |
| locations[2].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[2].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Applied Sciences; Volume 11; Issue 22; Pages: 10908 |
| locations[2].landing_page_url | https://dx.doi.org/10.3390/app112210908 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5022089324 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-4986-9885 |
| authorships[0].author.display_name | Mohammed Amin Benbouras |
| authorships[0].countries | DZ |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210110082, https://openalex.org/I98962006 |
| authorships[0].affiliations[0].raw_affiliation_string | Technology Department, École Normale Supérieure d’Enseignement Technologique de Skikda (ENSET), Skikda 21001, Algeria |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I4210116852 |
| authorships[0].affiliations[1].raw_affiliation_string | Central Public Works Laboratory (LCTP), Algiers 16006, Algeria |
| authorships[0].institutions[0].id | https://openalex.org/I4210116852 |
| authorships[0].institutions[0].ror | https://ror.org/025tvnr06 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210116852 |
| authorships[0].institutions[0].country_code | DZ |
| authorships[0].institutions[0].display_name | National School of Built and Ground Works Engineering |
| authorships[0].institutions[1].id | https://openalex.org/I98962006 |
| authorships[0].institutions[1].ror | https://ror.org/02571vj15 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I98962006 |
| authorships[0].institutions[1].country_code | DZ |
| authorships[0].institutions[1].display_name | University of Skikda |
| authorships[0].institutions[2].id | https://openalex.org/I4210110082 |
| authorships[0].institutions[2].ror | https://ror.org/02p97w646 |
| authorships[0].institutions[2].type | education |
| authorships[0].institutions[2].lineage | https://openalex.org/I4210110082 |
| authorships[0].institutions[2].country_code | DZ |
| authorships[0].institutions[2].display_name | École Nationale Polytechnique d'Oran |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Mohammed Amin Benbouras |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Central Public Works Laboratory (LCTP), Algiers 16006, Algeria, Technology Department, École Normale Supérieure d’Enseignement Technologique de Skikda (ENSET), Skikda 21001, Algeria |
| authorships[1].author.id | https://openalex.org/A5054289356 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-2159-4034 |
| authorships[1].author.display_name | Alexandru-Ionuţ Petrişor |
| authorships[1].countries | RO |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210114681 |
| authorships[1].affiliations[0].raw_affiliation_string | National Institute for Research and Development in Tourism, 50741 Bucharest, Romania |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I4210150670 |
| authorships[1].affiliations[1].raw_affiliation_string | Doctoral School of Urban Planning, "Ion Mincu" University of Architecture and Urbanism, 010014 Bucharest, Romania |
| authorships[1].institutions[0].id | https://openalex.org/I4210150670 |
| authorships[1].institutions[0].ror | https://ror.org/04z5xnd65 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210150670 |
| authorships[1].institutions[0].country_code | RO |
| authorships[1].institutions[0].display_name | Ion Mincu University of Architecture and Urbanism |
| authorships[1].institutions[1].id | https://openalex.org/I4210114681 |
| authorships[1].institutions[1].ror | https://ror.org/028rq5v79 |
| authorships[1].institutions[1].type | facility |
| authorships[1].institutions[1].lineage | https://openalex.org/I4210114681 |
| authorships[1].institutions[1].country_code | RO |
| authorships[1].institutions[1].display_name | National Institute for Research and Development in Informatics - ICI Bucharest |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Alexandru-Ionuţ Petrişor |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Doctoral School of Urban Planning, "Ion Mincu" University of Architecture and Urbanism, 010014 Bucharest, Romania, National Institute for Research and Development in Tourism, 50741 Bucharest, Romania |
| authorships[2].author.id | https://openalex.org/A5011712226 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-7836-3675 |
| authorships[2].author.display_name | Hamma Zedira |
| authorships[2].countries | DZ |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210130929 |
| authorships[2].affiliations[0].raw_affiliation_string | Civil Engineering Department, University of Abbes Laghrour, Khenchela 40051, Algeria |
| authorships[2].institutions[0].id | https://openalex.org/I4210130929 |
| authorships[2].institutions[0].ror | https://ror.org/02yyskm09 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210130929 |
| authorships[2].institutions[0].country_code | DZ |
| authorships[2].institutions[0].display_name | Abbès Laghrour University of Khenchela |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Hamma Zedira |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Civil Engineering Department, University of Abbes Laghrour, Khenchela 40051, Algeria |
| authorships[3].author.id | https://openalex.org/A5017204535 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Laala Ghelani |
| authorships[3].countries | DZ |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210130929 |
| authorships[3].affiliations[0].raw_affiliation_string | Civil Engineering Department, University of Abbes Laghrour, Khenchela 40051, Algeria |
| authorships[3].institutions[0].id | https://openalex.org/I4210130929 |
| authorships[3].institutions[0].ror | https://ror.org/02yyskm09 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210130929 |
| authorships[3].institutions[0].country_code | DZ |
| authorships[3].institutions[0].display_name | Abbès Laghrour University of Khenchela |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Laala Ghelani |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Civil Engineering Department, University of Abbes Laghrour, Khenchela 40051, Algeria |
| authorships[4].author.id | https://openalex.org/A5046246491 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Lina Lefilef |
| authorships[4].countries | DZ |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I116970179 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of English Language and Literature, Mohamed Seddik Ben Yahia University, Jijel 18000, Algeria |
| authorships[4].institutions[0].id | https://openalex.org/I116970179 |
| authorships[4].institutions[0].ror | https://ror.org/03kkfk814 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I116970179 |
| authorships[4].institutions[0].country_code | DZ |
| authorships[4].institutions[0].display_name | University of Jijel |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Lina Lefilef |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of English Language and Literature, Mohamed Seddik Ben Yahia University, Jijel 18000, Algeria |
| 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/2076-3417/11/22/10908/pdf?version=1637234478 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2021-11-22T00:00:00 |
| display_name | Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11046 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9993000030517578 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2213 |
| primary_topic.subfield.display_name | Safety, Risk, Reliability and Quality |
| primary_topic.display_name | Geotechnical Engineering and Analysis |
| related_works | https://openalex.org/W2485372704, https://openalex.org/W2380261036, https://openalex.org/W2384996535, https://openalex.org/W4381806208, https://openalex.org/W1977068136, https://openalex.org/W2370027897, https://openalex.org/W2355301224, https://openalex.org/W2961085424, https://openalex.org/W4306674287, https://openalex.org/W3046775127 |
| cited_by_count | 44 |
| 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 | 19 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 8 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 6 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 1 |
| locations_count | 3 |
| best_oa_location.id | doi:10.3390/app112210908 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210205812 |
| best_oa_location.source.issn | 2076-3417 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2076-3417 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Applied Sciences |
| 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.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/2076-3417/11/22/10908/pdf?version=1637234478 |
| 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 | Applied Sciences |
| best_oa_location.landing_page_url | https://doi.org/10.3390/app112210908 |
| primary_location.id | doi:10.3390/app112210908 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210205812 |
| primary_location.source.issn | 2076-3417 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2076-3417 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Applied Sciences |
| 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.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2076-3417/11/22/10908/pdf?version=1637234478 |
| 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 | Applied Sciences |
| primary_location.landing_page_url | https://doi.org/10.3390/app112210908 |
| publication_date | 2021-11-18 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W1916166419, https://openalex.org/W2999209402, https://openalex.org/W2482246741, https://openalex.org/W2102771921, https://openalex.org/W154041102, https://openalex.org/W2164063121, https://openalex.org/W2003753444, https://openalex.org/W2018702855, https://openalex.org/W3107286102, https://openalex.org/W2086282769, https://openalex.org/W6605189880, https://openalex.org/W2139941099, https://openalex.org/W2046421825, https://openalex.org/W4244791366, https://openalex.org/W3112308150, https://openalex.org/W3200191244, https://openalex.org/W3185287488, https://openalex.org/W6696394776, https://openalex.org/W2024761716, https://openalex.org/W2913772749, https://openalex.org/W2903584931, https://openalex.org/W2969696253, https://openalex.org/W2768643127, https://openalex.org/W2990990692, https://openalex.org/W2944188217, https://openalex.org/W3099567700, https://openalex.org/W15044778, https://openalex.org/W1999338295, https://openalex.org/W2019371222, https://openalex.org/W2070628113, https://openalex.org/W2030384459, https://openalex.org/W6661165060, https://openalex.org/W2319304323, https://openalex.org/W2897768203, https://openalex.org/W7064675543, https://openalex.org/W2111072639, https://openalex.org/W2216946510, https://openalex.org/W4213113494, https://openalex.org/W2481769138, https://openalex.org/W1976734415, https://openalex.org/W2038751902, https://openalex.org/W6645120843, https://openalex.org/W2071377852, https://openalex.org/W2466974575, https://openalex.org/W3092192795, https://openalex.org/W3119419854, https://openalex.org/W2111814036, https://openalex.org/W2009897462, https://openalex.org/W2063987149, https://openalex.org/W6791580609, https://openalex.org/W2103728465, https://openalex.org/W3048411561, https://openalex.org/W2289156018, https://openalex.org/W2044385497, https://openalex.org/W1979382840, https://openalex.org/W3134866198 |
| referenced_works_count | 56 |
| abstract_inverted_index.a | 36, 62, 105, 121, 155 |
| abstract_inverted_index.an | 7 |
| abstract_inverted_index.at | 34 |
| abstract_inverted_index.be | 167 |
| abstract_inverted_index.by | 151 |
| abstract_inverted_index.in | 23, 52, 77, 124 |
| abstract_inverted_index.is | 6 |
| abstract_inverted_index.of | 4, 64, 115, 183 |
| abstract_inverted_index.on | 46, 82 |
| abstract_inverted_index.to | 54, 139, 144 |
| abstract_inverted_index.100 | 65 |
| abstract_inverted_index.DNN | 117 |
| abstract_inverted_index.The | 30, 58, 86, 109 |
| abstract_inverted_index.and | 14, 28, 95, 148, 157, 172, 186 |
| abstract_inverted_index.are | 26 |
| abstract_inverted_index.for | 12, 40, 170 |
| abstract_inverted_index.new | 37, 48 |
| abstract_inverted_index.six | 101 |
| abstract_inverted_index.the | 1, 19, 42, 78, 83, 92, 113, 116, 126, 133, 137, 140, 145, 177, 181 |
| abstract_inverted_index.was | 98, 161 |
| abstract_inverted_index.This | 129, 165 |
| abstract_inverted_index.aims | 33 |
| abstract_inverted_index.from | 68 |
| abstract_inverted_index.safe | 13 |
| abstract_inverted_index.this | 24 |
| abstract_inverted_index.time | 185 |
| abstract_inverted_index.used | 61 |
| abstract_inverted_index.very | 168 |
| abstract_inverted_index.were | 75, 89 |
| abstract_inverted_index.when | 10, 175 |
| abstract_inverted_index.will | 166 |
| abstract_inverted_index.with | 180 |
| abstract_inverted_index.based | 45, 81 |
| abstract_inverted_index.civil | 173 |
| abstract_inverted_index.eight | 72 |
| abstract_inverted_index.i.e., | 136 |
| abstract_inverted_index.input | 79 |
| abstract_inverted_index.layer | 80 |
| abstract_inverted_index.model | 39, 131 |
| abstract_inverted_index.order | 53 |
| abstract_inverted_index.other | 146 |
| abstract_inverted_index.phase | 60 |
| abstract_inverted_index.piles | 5 |
| abstract_inverted_index.point | 9 |
| abstract_inverted_index.study | 32, 111 |
| abstract_inverted_index.their | 96 |
| abstract_inverted_index.these | 56 |
| abstract_inverted_index.using | 91, 104 |
| abstract_inverted_index.which | 119 |
| abstract_inverted_index.K-fold | 106 |
| abstract_inverted_index.eleven | 47 |
| abstract_inverted_index.higher | 122 |
| abstract_inverted_index.inputs | 88 |
| abstract_inverted_index.model, | 118 |
| abstract_inverted_index.models | 147 |
| abstract_inverted_index.money. | 187 |
| abstract_inverted_index.namely | 163 |
| abstract_inverted_index.proved | 112 |
| abstract_inverted_index.saving | 184 |
| abstract_inverted_index.bearing | 2 |
| abstract_inverted_index.closest | 138 |
| abstract_inverted_index.costly. | 29 |
| abstract_inverted_index.current | 31 |
| abstract_inverted_index.factors | 74 |
| abstract_inverted_index.helpful | 169 |
| abstract_inverted_index.methods | 21, 51, 94 |
| abstract_inverted_index.modeled | 90 |
| abstract_inverted_index.optimal | 87, 134 |
| abstract_inverted_index.samples | 66 |
| abstract_inverted_index.seeking | 11 |
| abstract_inverted_index.through | 100 |
| abstract_inverted_index.values, | 142 |
| abstract_inverted_index.Finally, | 154 |
| abstract_inverted_index.However, | 18 |
| abstract_inverted_index.advanced | 49 |
| abstract_inverted_index.assessed | 99 |
| abstract_inverted_index.capacity | 3, 44 |
| abstract_inverted_index.compared | 143 |
| abstract_inverted_index.database | 63 |
| abstract_inverted_index.economic | 15 |
| abstract_inverted_index.employed | 22 |
| abstract_inverted_index.formulae | 149 |
| abstract_inverted_index.measures | 103 |
| abstract_inverted_index.modeling | 59 |
| abstract_inverted_index.overcome | 55 |
| abstract_inverted_index.previous | 152 |
| abstract_inverted_index.proposed | 150 |
| abstract_inverted_index.provided | 132 |
| abstract_inverted_index.relevant | 73 |
| abstract_inverted_index.reliable | 156 |
| abstract_inverted_index.selected | 76 |
| abstract_inverted_index.studies. | 153 |
| abstract_inverted_index.advantage | 182 |
| abstract_inverted_index.approach. | 108 |
| abstract_inverted_index.capacity, | 179 |
| abstract_inverted_index.capacity. | 128 |
| abstract_inverted_index.collected | 67 |
| abstract_inverted_index.different | 69 |
| abstract_inverted_index.displayed | 120 |
| abstract_inverted_index.engineers | 174 |
| abstract_inverted_index.essential | 8 |
| abstract_inverted_index.graphical | 159 |
| abstract_inverted_index.interface | 160 |
| abstract_inverted_index.Estimating | 0 |
| abstract_inverted_index.countries. | 70 |
| abstract_inverted_index.elaborated | 130 |
| abstract_inverted_index.estimating | 176 |
| abstract_inverted_index.estimation | 25 |
| abstract_inverted_index.generated, | 162 |
| abstract_inverted_index.literature | 84 |
| abstract_inverted_index.predicting | 41, 125 |
| abstract_inverted_index.alternative | 38 |
| abstract_inverted_index.comparative | 110 |
| abstract_inverted_index.easy-to-use | 158 |
| abstract_inverted_index.elaborating | 35 |
| abstract_inverted_index.performance | 97, 102, 123 |
| abstract_inverted_index.prediction, | 135 |
| abstract_inverted_index.researchers | 171 |
| abstract_inverted_index.structures. | 17 |
| abstract_inverted_index.traditional | 20 |
| abstract_inverted_index.experimental | 141 |
| abstract_inverted_index.geotechnical | 16 |
| abstract_inverted_index.limitations. | 57 |
| abstract_inverted_index.pile-bearing | 43, 127, 178 |
| abstract_inverted_index.Additionally, | 71 |
| abstract_inverted_index.effectiveness | 114 |
| abstract_inverted_index.time-consuming | 27 |
| abstract_inverted_index.cross-validation | 107 |
| abstract_inverted_index.machine-learning | 50, 93 |
| abstract_inverted_index.recommendations. | 85 |
| abstract_inverted_index.“BeaCa2021”. | 164 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5022089324 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I4210110082, https://openalex.org/I4210116852, https://openalex.org/I98962006 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5199999809265137 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.96779095 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |