Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw Cycles Article Swipe
You Lv
,
Ru Zhang
,
Anlin Zhang
,
Yanjun Shen
,
Li Ren
,
Jing Xie
,
Zetian Zhang
,
Zhilong Zhang
,
Lu An
,
Junlong Sun
,
Zhiwei Yan
,
Ou Mi
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1755-1315/1337/1/012072
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1755-1315/1337/1/012072
The combined impact of freeze-thaw cycles and dynamic loads significantly influences the long-term durability of rock engineering in high-cold regions. Consequently, investigating the dynamic compressive strength (DCS) of rocks subjected to freeze-thaw cycles has emerged as a crucial area of scientific research to advance rock engineering construction in cold regions. Presently, the determination of the DCS of rocks under freeze-thaw cycles primarily relies on indoor experiments. However, this approach has faced criticism due to its drawbacks, including prolonged duration, high costs, and reliance on rock samples. To address these limitations, the exploration of using artificial intelligence technology to develop more accurate and convenient DCS prediction models for rocks under freeze-thaw cycles is a promising attempt. In this context, this paper introduces a DCS prediction model for rocks under freeze-thaw cycles, which integrates the Sparrow Search Algorithm (SSA) with Random Forest (RF). Firstly, employing a dataset of 216 samples, Principal Component Analysis (PCA) is utilized to reduce the dimensionality of ten influential factors. Subsequently, five optimization algorithms are employed to optimize the hyperparameters of both the BP and RF algorithms. Finally, a comprehensive evaluation and comparative analysis are carried out to assess the predictive performance of the optimized model, using evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2).The research findings demonstrate that the SSA-RF model exhibits the best predictive performance, surpassing the other nine models in terms of generalization. The prediction model proposed in this study has good applicability for predicting DCS of freeze-thaw rock in cold regions, and also provides new ideas for the combination of machine learning and rock mass engineering in cold regions.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1755-1315/1337/1/012072
- https://iopscience.iop.org/article/10.1088/1755-1315/1337/1/012072/pdf
- OA Status
- diamond
- Cited By
- 2
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4398203929
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4398203929Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1755-1315/1337/1/012072Digital Object Identifier
- Title
-
Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw CyclesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-01Full publication date if available
- Authors
-
You Lv, Ru Zhang, Anlin Zhang, Yanjun Shen, Li Ren, Jing Xie, Zetian Zhang, Zhilong Zhang, Lu An, Junlong Sun, Zhiwei Yan, Ou MiList of authors in order
- Landing page
-
https://doi.org/10.1088/1755-1315/1337/1/012072Publisher landing page
- PDF URL
-
https://iopscience.iop.org/article/10.1088/1755-1315/1337/1/012072/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://iopscience.iop.org/article/10.1088/1755-1315/1337/1/012072/pdfDirect OA link when available
- Concepts
-
Compressive strength, Algorithm, Computer science, Machine learning, Geology, Geotechnical engineering, Materials science, Composite materialTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
48Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4398203929 |
|---|---|
| doi | https://doi.org/10.1088/1755-1315/1337/1/012072 |
| ids.doi | https://doi.org/10.1088/1755-1315/1337/1/012072 |
| ids.openalex | https://openalex.org/W4398203929 |
| fwci | 4.49735452 |
| type | article |
| title | Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw Cycles |
| biblio.issue | 1 |
| biblio.volume | 1337 |
| biblio.last_page | 012072 |
| biblio.first_page | 012072 |
| topics[0].id | https://openalex.org/T10161 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9997000098228455 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2211 |
| topics[0].subfield.display_name | Mechanics of Materials |
| topics[0].display_name | Rock Mechanics and Modeling |
| topics[1].id | https://openalex.org/T10535 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9984999895095825 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2308 |
| topics[1].subfield.display_name | Management, Monitoring, Policy and Law |
| topics[1].display_name | Landslides and related hazards |
| topics[2].id | https://openalex.org/T13619 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9955000281333923 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2211 |
| topics[2].subfield.display_name | Mechanics of Materials |
| topics[2].display_name | Geotechnical and Geomechanical Engineering |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C30407753 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7184493541717529 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q186191 |
| concepts[0].display_name | Compressive strength |
| concepts[1].id | https://openalex.org/C11413529 |
| concepts[1].level | 1 |
| concepts[1].score | 0.5179418921470642 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[1].display_name | Algorithm |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.38423457741737366 |
| 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 | 0.3832100033760071 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[3].display_name | Machine learning |
| concepts[4].id | https://openalex.org/C127313418 |
| concepts[4].level | 0 |
| concepts[4].score | 0.38144680857658386 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[4].display_name | Geology |
| concepts[5].id | https://openalex.org/C187320778 |
| concepts[5].level | 1 |
| concepts[5].score | 0.3650735318660736 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1349130 |
| concepts[5].display_name | Geotechnical engineering |
| concepts[6].id | https://openalex.org/C192562407 |
| concepts[6].level | 0 |
| concepts[6].score | 0.19350191950798035 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q228736 |
| concepts[6].display_name | Materials science |
| concepts[7].id | https://openalex.org/C159985019 |
| concepts[7].level | 1 |
| concepts[7].score | 0.15532031655311584 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q181790 |
| concepts[7].display_name | Composite material |
| keywords[0].id | https://openalex.org/keywords/compressive-strength |
| keywords[0].score | 0.7184493541717529 |
| keywords[0].display_name | Compressive strength |
| keywords[1].id | https://openalex.org/keywords/algorithm |
| keywords[1].score | 0.5179418921470642 |
| keywords[1].display_name | Algorithm |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.38423457741737366 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/machine-learning |
| keywords[3].score | 0.3832100033760071 |
| keywords[3].display_name | Machine learning |
| keywords[4].id | https://openalex.org/keywords/geology |
| keywords[4].score | 0.38144680857658386 |
| keywords[4].display_name | Geology |
| keywords[5].id | https://openalex.org/keywords/geotechnical-engineering |
| keywords[5].score | 0.3650735318660736 |
| keywords[5].display_name | Geotechnical engineering |
| keywords[6].id | https://openalex.org/keywords/materials-science |
| keywords[6].score | 0.19350191950798035 |
| keywords[6].display_name | Materials science |
| keywords[7].id | https://openalex.org/keywords/composite-material |
| keywords[7].score | 0.15532031655311584 |
| keywords[7].display_name | Composite material |
| language | en |
| locations[0].id | doi:10.1088/1755-1315/1337/1/012072 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210195883 |
| locations[0].source.issn | 1755-1307, 1755-1315 |
| locations[0].source.type | conference |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1755-1307 |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | IOP Conference Series Earth and Environmental Science |
| locations[0].source.host_organization | https://openalex.org/P4310320083 |
| locations[0].source.host_organization_name | IOP Publishing |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| locations[0].source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://iopscience.iop.org/article/10.1088/1755-1315/1337/1/012072/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 | IOP Conference Series: Earth and Environmental Science |
| locations[0].landing_page_url | https://doi.org/10.1088/1755-1315/1337/1/012072 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5005590423 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | You Lv |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I24185976 |
| authorships[0].affiliations[0].raw_affiliation_string | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[0].institutions[0].id | https://openalex.org/I24185976 |
| authorships[0].institutions[0].ror | https://ror.org/011ashp19 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I24185976 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Sichuan University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | You Lv |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[1].author.id | https://openalex.org/A5100750675 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-7899-7312 |
| authorships[1].author.display_name | Ru Zhang |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I24185976 |
| authorships[1].affiliations[0].raw_affiliation_string | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[1].institutions[0].id | https://openalex.org/I24185976 |
| authorships[1].institutions[0].ror | https://ror.org/011ashp19 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I24185976 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Sichuan University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Ru Zhang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[2].author.id | https://openalex.org/A5042965041 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Anlin Zhang |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I24185976 |
| authorships[2].affiliations[0].raw_affiliation_string | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[2].institutions[0].id | https://openalex.org/I24185976 |
| authorships[2].institutions[0].ror | https://ror.org/011ashp19 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I24185976 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Sichuan University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Anlin Zhang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[3].author.id | https://openalex.org/A5043264044 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-8109-4819 |
| authorships[3].author.display_name | Yanjun Shen |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I25355098 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, Shaanxi, China |
| authorships[3].institutions[0].id | https://openalex.org/I25355098 |
| authorships[3].institutions[0].ror | https://ror.org/05mxya461 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I25355098 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Chang'an University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Yanjun Shen |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, Shaanxi, China |
| authorships[4].author.id | https://openalex.org/A5102702892 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Li Ren |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I24185976 |
| authorships[4].affiliations[0].raw_affiliation_string | MOE Key Laboratory of Deep Earth Science & Engineering, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[4].institutions[0].id | https://openalex.org/I24185976 |
| authorships[4].institutions[0].ror | https://ror.org/011ashp19 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I24185976 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Sichuan University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Li Ren |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | MOE Key Laboratory of Deep Earth Science & Engineering, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[5].author.id | https://openalex.org/A5100961012 |
| authorships[5].author.orcid | https://orcid.org/0009-0002-1096-376X |
| authorships[5].author.display_name | Jing Xie |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I24185976 |
| authorships[5].affiliations[0].raw_affiliation_string | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[5].institutions[0].id | https://openalex.org/I24185976 |
| authorships[5].institutions[0].ror | https://ror.org/011ashp19 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I24185976 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | Sichuan University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Jing Xie |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[6].author.id | https://openalex.org/A5051031104 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-7182-2787 |
| authorships[6].author.display_name | Zetian Zhang |
| authorships[6].countries | CN |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I24185976 |
| authorships[6].affiliations[0].raw_affiliation_string | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[6].institutions[0].id | https://openalex.org/I24185976 |
| authorships[6].institutions[0].ror | https://ror.org/011ashp19 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I24185976 |
| authorships[6].institutions[0].country_code | CN |
| authorships[6].institutions[0].display_name | Sichuan University |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Zetian Zhang |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[7].author.id | https://openalex.org/A5029895990 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-7156-5602 |
| authorships[7].author.display_name | Zhilong Zhang |
| authorships[7].countries | CN |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I24185976 |
| authorships[7].affiliations[0].raw_affiliation_string | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[7].institutions[0].id | https://openalex.org/I24185976 |
| authorships[7].institutions[0].ror | https://ror.org/011ashp19 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I24185976 |
| authorships[7].institutions[0].country_code | CN |
| authorships[7].institutions[0].display_name | Sichuan University |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Zhilong Zhang |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[8].author.id | https://openalex.org/A5017515601 |
| authorships[8].author.orcid | https://orcid.org/0000-0003-4552-7466 |
| authorships[8].author.display_name | Lu An |
| authorships[8].countries | CN |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I24185976 |
| authorships[8].affiliations[0].raw_affiliation_string | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[8].institutions[0].id | https://openalex.org/I24185976 |
| authorships[8].institutions[0].ror | https://ror.org/011ashp19 |
| authorships[8].institutions[0].type | education |
| authorships[8].institutions[0].lineage | https://openalex.org/I24185976 |
| authorships[8].institutions[0].country_code | CN |
| authorships[8].institutions[0].display_name | Sichuan University |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Lu An |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[9].author.id | https://openalex.org/A5087175464 |
| authorships[9].author.orcid | https://orcid.org/0000-0002-4482-2925 |
| authorships[9].author.display_name | Junlong Sun |
| authorships[9].countries | CN |
| authorships[9].affiliations[0].institution_ids | https://openalex.org/I24185976 |
| authorships[9].affiliations[0].raw_affiliation_string | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[9].institutions[0].id | https://openalex.org/I24185976 |
| authorships[9].institutions[0].ror | https://ror.org/011ashp19 |
| authorships[9].institutions[0].type | education |
| authorships[9].institutions[0].lineage | https://openalex.org/I24185976 |
| authorships[9].institutions[0].country_code | CN |
| authorships[9].institutions[0].display_name | Sichuan University |
| authorships[9].author_position | middle |
| authorships[9].raw_author_name | Junlong Sun |
| authorships[9].is_corresponding | False |
| authorships[9].raw_affiliation_strings | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[10].author.id | https://openalex.org/A5100735879 |
| authorships[10].author.orcid | https://orcid.org/0000-0001-6635-482X |
| authorships[10].author.display_name | Zhiwei Yan |
| authorships[10].countries | CN |
| authorships[10].affiliations[0].institution_ids | https://openalex.org/I24185976 |
| authorships[10].affiliations[0].raw_affiliation_string | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[10].institutions[0].id | https://openalex.org/I24185976 |
| authorships[10].institutions[0].ror | https://ror.org/011ashp19 |
| authorships[10].institutions[0].type | education |
| authorships[10].institutions[0].lineage | https://openalex.org/I24185976 |
| authorships[10].institutions[0].country_code | CN |
| authorships[10].institutions[0].display_name | Sichuan University |
| authorships[10].author_position | middle |
| authorships[10].raw_author_name | Zhiwei Yan |
| authorships[10].is_corresponding | False |
| authorships[10].raw_affiliation_strings | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[11].author.id | https://openalex.org/A5098753647 |
| authorships[11].author.orcid | |
| authorships[11].author.display_name | Ou Mi |
| authorships[11].countries | CN |
| authorships[11].affiliations[0].institution_ids | https://openalex.org/I24185976 |
| authorships[11].affiliations[0].raw_affiliation_string | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| authorships[11].institutions[0].id | https://openalex.org/I24185976 |
| authorships[11].institutions[0].ror | https://ror.org/011ashp19 |
| authorships[11].institutions[0].type | education |
| authorships[11].institutions[0].lineage | https://openalex.org/I24185976 |
| authorships[11].institutions[0].country_code | CN |
| authorships[11].institutions[0].display_name | Sichuan University |
| authorships[11].author_position | last |
| authorships[11].raw_author_name | Ou Mi |
| authorships[11].is_corresponding | False |
| authorships[11].raw_affiliation_strings | State Key Laboratory of Intelligent Construction & Healthy Operation & Maintenance of Deep Underground Engineering, College of Water Resources & Hydropower, Sichuan University, Chengdu 610065, Sichuan, China |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://iopscience.iop.org/article/10.1088/1755-1315/1337/1/012072/pdf |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw Cycles |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10161 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9997000098228455 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2211 |
| primary_topic.subfield.display_name | Mechanics of Materials |
| primary_topic.display_name | Rock Mechanics and Modeling |
| related_works | https://openalex.org/W2051487156, https://openalex.org/W2073681303, https://openalex.org/W2324615561, https://openalex.org/W2086120259, https://openalex.org/W2245170124, https://openalex.org/W2076393078, https://openalex.org/W3186982001, https://openalex.org/W1984402782, https://openalex.org/W2137941439, https://openalex.org/W2004200960 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1088/1755-1315/1337/1/012072 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210195883 |
| best_oa_location.source.issn | 1755-1307, 1755-1315 |
| best_oa_location.source.type | conference |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1755-1307 |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | IOP Conference Series Earth and Environmental Science |
| best_oa_location.source.host_organization | https://openalex.org/P4310320083 |
| best_oa_location.source.host_organization_name | IOP Publishing |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| best_oa_location.source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://iopscience.iop.org/article/10.1088/1755-1315/1337/1/012072/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 | IOP Conference Series: Earth and Environmental Science |
| best_oa_location.landing_page_url | https://doi.org/10.1088/1755-1315/1337/1/012072 |
| primary_location.id | doi:10.1088/1755-1315/1337/1/012072 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210195883 |
| primary_location.source.issn | 1755-1307, 1755-1315 |
| primary_location.source.type | conference |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1755-1307 |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | IOP Conference Series Earth and Environmental Science |
| primary_location.source.host_organization | https://openalex.org/P4310320083 |
| primary_location.source.host_organization_name | IOP Publishing |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| primary_location.source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://iopscience.iop.org/article/10.1088/1755-1315/1337/1/012072/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 | IOP Conference Series: Earth and Environmental Science |
| primary_location.landing_page_url | https://doi.org/10.1088/1755-1315/1337/1/012072 |
| publication_date | 2024-05-01 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2388587499, https://openalex.org/W4380792760, https://openalex.org/W3016903593, https://openalex.org/W6885396662, https://openalex.org/W3163226061, https://openalex.org/W4295182907, https://openalex.org/W4206535851, https://openalex.org/W4205430130, https://openalex.org/W4367837446, https://openalex.org/W4310355366, https://openalex.org/W3214697318, https://openalex.org/W4281677477, https://openalex.org/W4320729194, https://openalex.org/W4308348111, https://openalex.org/W4307918322, https://openalex.org/W3043056975, https://openalex.org/W2826767757, https://openalex.org/W2804402200, https://openalex.org/W2152565940, https://openalex.org/W4312126045, https://openalex.org/W2050796978, https://openalex.org/W3043697723, https://openalex.org/W4283220983, https://openalex.org/W2799437296, https://openalex.org/W3008518231, https://openalex.org/W3131319691, https://openalex.org/W2515286690, https://openalex.org/W4353094365, https://openalex.org/W4353094384, https://openalex.org/W3102212426, https://openalex.org/W6922830664, https://openalex.org/W4380208092, https://openalex.org/W4214806397, https://openalex.org/W3000029390, https://openalex.org/W3153805502, https://openalex.org/W2295124130, https://openalex.org/W3011249019, https://openalex.org/W3144214148, https://openalex.org/W1999140879, https://openalex.org/W4297236492, https://openalex.org/W3047112094, https://openalex.org/W2913929164, https://openalex.org/W3214408964, https://openalex.org/W2999527155, https://openalex.org/W3107652718, https://openalex.org/W3212085559, https://openalex.org/W4283740082, https://openalex.org/W2144908581 |
| referenced_works_count | 48 |
| abstract_inverted_index.a | 37, 113, 122, 144, 181 |
| abstract_inverted_index.BP | 176 |
| abstract_inverted_index.In | 116 |
| abstract_inverted_index.RF | 178 |
| abstract_inverted_index.To | 87 |
| abstract_inverted_index.as | 36, 203 |
| abstract_inverted_index.in | 18, 48, 240, 248, 260, 278 |
| abstract_inverted_index.is | 112, 153 |
| abstract_inverted_index.of | 4, 15, 28, 40, 54, 57, 93, 146, 159, 173, 195, 220, 242, 257, 271 |
| abstract_inverted_index.on | 64, 84 |
| abstract_inverted_index.to | 31, 43, 74, 98, 155, 169, 190 |
| abstract_inverted_index.216 | 147 |
| abstract_inverted_index.DCS | 56, 104, 123, 256 |
| abstract_inverted_index.The | 1, 244 |
| abstract_inverted_index.and | 7, 82, 102, 177, 184, 218, 263, 274 |
| abstract_inverted_index.are | 167, 187 |
| abstract_inverted_index.due | 73 |
| abstract_inverted_index.for | 107, 126, 254, 268 |
| abstract_inverted_index.has | 34, 70, 251 |
| abstract_inverted_index.its | 75 |
| abstract_inverted_index.new | 266 |
| abstract_inverted_index.out | 189 |
| abstract_inverted_index.ten | 160 |
| abstract_inverted_index.the | 12, 23, 52, 55, 91, 133, 157, 171, 175, 192, 196, 227, 231, 236, 269 |
| abstract_inverted_index.Mean | 205, 209, 213 |
| abstract_inverted_index.Root | 204 |
| abstract_inverted_index.also | 264 |
| abstract_inverted_index.area | 39 |
| abstract_inverted_index.best | 232 |
| abstract_inverted_index.both | 174 |
| abstract_inverted_index.cold | 49, 261, 279 |
| abstract_inverted_index.five | 164 |
| abstract_inverted_index.good | 252 |
| abstract_inverted_index.high | 80 |
| abstract_inverted_index.mass | 276 |
| abstract_inverted_index.more | 100 |
| abstract_inverted_index.nine | 238 |
| abstract_inverted_index.rock | 16, 45, 85, 259, 275 |
| abstract_inverted_index.such | 202 |
| abstract_inverted_index.that | 226 |
| abstract_inverted_index.this | 68, 117, 119, 249 |
| abstract_inverted_index.with | 138 |
| abstract_inverted_index.(DCS) | 27 |
| abstract_inverted_index.(PCA) | 152 |
| abstract_inverted_index.(RF). | 141 |
| abstract_inverted_index.(SSA) | 137 |
| abstract_inverted_index.Error | 207, 211, 216 |
| abstract_inverted_index.faced | 71 |
| abstract_inverted_index.ideas | 267 |
| abstract_inverted_index.loads | 9 |
| abstract_inverted_index.model | 125, 229, 246 |
| abstract_inverted_index.other | 237 |
| abstract_inverted_index.paper | 120 |
| abstract_inverted_index.rocks | 29, 58, 108, 127 |
| abstract_inverted_index.study | 250 |
| abstract_inverted_index.terms | 241 |
| abstract_inverted_index.these | 89 |
| abstract_inverted_index.under | 59, 109, 128 |
| abstract_inverted_index.using | 94, 199 |
| abstract_inverted_index.which | 131 |
| abstract_inverted_index.(MAE), | 212 |
| abstract_inverted_index.Forest | 140 |
| abstract_inverted_index.Random | 139 |
| abstract_inverted_index.SSA-RF | 228 |
| abstract_inverted_index.Search | 135 |
| abstract_inverted_index.Square | 206 |
| abstract_inverted_index.assess | 191 |
| abstract_inverted_index.costs, | 81 |
| abstract_inverted_index.cycles | 6, 33, 61, 111 |
| abstract_inverted_index.impact | 3 |
| abstract_inverted_index.indoor | 65 |
| abstract_inverted_index.model, | 198 |
| abstract_inverted_index.models | 106, 239 |
| abstract_inverted_index.reduce | 156 |
| abstract_inverted_index.relies | 63 |
| abstract_inverted_index.(MAPE), | 217 |
| abstract_inverted_index.(RMSE), | 208 |
| abstract_inverted_index.Sparrow | 134 |
| abstract_inverted_index.address | 88 |
| abstract_inverted_index.advance | 44 |
| abstract_inverted_index.carried | 188 |
| abstract_inverted_index.crucial | 38 |
| abstract_inverted_index.cycles, | 130 |
| abstract_inverted_index.dataset | 145 |
| abstract_inverted_index.develop | 99 |
| abstract_inverted_index.dynamic | 8, 24 |
| abstract_inverted_index.emerged | 35 |
| abstract_inverted_index.machine | 272 |
| abstract_inverted_index.metrics | 201 |
| abstract_inverted_index.(R2).The | 222 |
| abstract_inverted_index.Absolute | 210, 214 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Analysis | 151 |
| abstract_inverted_index.Finally, | 180 |
| abstract_inverted_index.Firstly, | 142 |
| abstract_inverted_index.However, | 67 |
| abstract_inverted_index.accurate | 101 |
| abstract_inverted_index.analysis | 186 |
| abstract_inverted_index.approach | 69 |
| abstract_inverted_index.attempt. | 115 |
| abstract_inverted_index.combined | 2 |
| abstract_inverted_index.context, | 118 |
| abstract_inverted_index.employed | 168 |
| abstract_inverted_index.exhibits | 230 |
| abstract_inverted_index.factors. | 162 |
| abstract_inverted_index.findings | 224 |
| abstract_inverted_index.learning | 273 |
| abstract_inverted_index.optimize | 170 |
| abstract_inverted_index.proposed | 247 |
| abstract_inverted_index.provides | 265 |
| abstract_inverted_index.regions, | 262 |
| abstract_inverted_index.regions. | 20, 50, 280 |
| abstract_inverted_index.reliance | 83 |
| abstract_inverted_index.research | 42, 223 |
| abstract_inverted_index.samples, | 148 |
| abstract_inverted_index.samples. | 86 |
| abstract_inverted_index.strength | 26 |
| abstract_inverted_index.utilized | 154 |
| abstract_inverted_index.Algorithm | 136 |
| abstract_inverted_index.Component | 150 |
| abstract_inverted_index.Principal | 149 |
| abstract_inverted_index.criticism | 72 |
| abstract_inverted_index.duration, | 79 |
| abstract_inverted_index.employing | 143 |
| abstract_inverted_index.high-cold | 19 |
| abstract_inverted_index.including | 77 |
| abstract_inverted_index.long-term | 13 |
| abstract_inverted_index.optimized | 197 |
| abstract_inverted_index.primarily | 62 |
| abstract_inverted_index.prolonged | 78 |
| abstract_inverted_index.promising | 114 |
| abstract_inverted_index.subjected | 30 |
| abstract_inverted_index.Percentage | 215 |
| abstract_inverted_index.Presently, | 51 |
| abstract_inverted_index.algorithms | 166 |
| abstract_inverted_index.artificial | 95 |
| abstract_inverted_index.convenient | 103 |
| abstract_inverted_index.drawbacks, | 76 |
| abstract_inverted_index.durability | 14 |
| abstract_inverted_index.evaluation | 183, 200 |
| abstract_inverted_index.influences | 11 |
| abstract_inverted_index.integrates | 132 |
| abstract_inverted_index.introduces | 121 |
| abstract_inverted_index.predicting | 255 |
| abstract_inverted_index.prediction | 105, 124, 245 |
| abstract_inverted_index.predictive | 193, 233 |
| abstract_inverted_index.scientific | 41 |
| abstract_inverted_index.surpassing | 235 |
| abstract_inverted_index.technology | 97 |
| abstract_inverted_index.Coefficient | 219 |
| abstract_inverted_index.algorithms. | 179 |
| abstract_inverted_index.combination | 270 |
| abstract_inverted_index.comparative | 185 |
| abstract_inverted_index.compressive | 25 |
| abstract_inverted_index.demonstrate | 225 |
| abstract_inverted_index.engineering | 17, 46, 277 |
| abstract_inverted_index.exploration | 92 |
| abstract_inverted_index.freeze-thaw | 5, 32, 60, 110, 129, 258 |
| abstract_inverted_index.influential | 161 |
| abstract_inverted_index.performance | 194 |
| abstract_inverted_index.construction | 47 |
| abstract_inverted_index.experiments. | 66 |
| abstract_inverted_index.intelligence | 96 |
| abstract_inverted_index.limitations, | 90 |
| abstract_inverted_index.optimization | 165 |
| abstract_inverted_index.performance, | 234 |
| abstract_inverted_index.Consequently, | 21 |
| abstract_inverted_index.Determination | 221 |
| abstract_inverted_index.Subsequently, | 163 |
| abstract_inverted_index.applicability | 253 |
| abstract_inverted_index.comprehensive | 182 |
| abstract_inverted_index.determination | 53 |
| abstract_inverted_index.investigating | 22 |
| abstract_inverted_index.significantly | 10 |
| abstract_inverted_index.dimensionality | 158 |
| abstract_inverted_index.generalization. | 243 |
| abstract_inverted_index.hyperparameters | 172 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 12 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.91666667 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |