Machine learning approaches for predicting the construction time of drill-and-blast tunnels Article Swipe
Arsalan Mahmoodzadeh
,
Hamid Reza Nejati
,
Nejib Ghazouani
,
Abdulaziz S. Alghamdi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-17455-7
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-17455-7
This study examines the intricate task of predicting construction duration for drill-and-blast tunnels utilizing machine learning (ML) techniques. First, a comprehensive dataset (500 data points) encompassing 20 diverse parameters was compiled by constructing eight tunnels. After meticulous analysis, 17 of the 20 parameters were identified as crucial for training the algorithms. The overbreak and tunnel cross-section parameters were found to exert a significant influence on the tunnel construction duration. To enhance the predictive accuracy of the ML models, an intensive hyperparameter tuning process was conducted. The findings underscored the effectiveness of the Gaussian process regression model in capturing complex and nonlinear relationships, achieving an average R-squared of 0.89. Additionally, an ML-based graphical user interface (GUI) was developed to facilitate real‒time estimation of tunnel construction duration. This GUI not only enables initial predictions but also allows for dynamic updates throughout the construction phase, enhancing its practical utility.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-17455-7
- https://www.nature.com/articles/s41598-025-17455-7.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 21
- Related Works
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- OpenAlex ID
- https://openalex.org/W4413819219
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413819219Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-025-17455-7Digital Object Identifier
- Title
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Machine learning approaches for predicting the construction time of drill-and-blast tunnelsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-29Full publication date if available
- Authors
-
Arsalan Mahmoodzadeh, Hamid Reza Nejati, Nejib Ghazouani, Abdulaziz S. AlghamdiList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-025-17455-7Publisher landing page
- PDF URL
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https://www.nature.com/articles/s41598-025-17455-7.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41598-025-17455-7.pdfDirect OA link when available
- Concepts
-
Drill, Computer science, Engineering, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- References (count)
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21Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.machine | 14 |
| abstract_inverted_index.models, | 77 |
| abstract_inverted_index.points) | 24 |
| abstract_inverted_index.process | 82, 93 |
| abstract_inverted_index.tunnels | 12 |
| abstract_inverted_index.updates | 137 |
| abstract_inverted_index.Gaussian | 92 |
| abstract_inverted_index.ML-based | 110 |
| abstract_inverted_index.accuracy | 73 |
| abstract_inverted_index.compiled | 30 |
| abstract_inverted_index.duration | 9 |
| abstract_inverted_index.examines | 2 |
| abstract_inverted_index.findings | 86 |
| abstract_inverted_index.learning | 15 |
| abstract_inverted_index.training | 48 |
| abstract_inverted_index.tunnels. | 34 |
| abstract_inverted_index.utility. | 145 |
| abstract_inverted_index.R-squared | 105 |
| abstract_inverted_index.achieving | 102 |
| abstract_inverted_index.analysis, | 37 |
| abstract_inverted_index.capturing | 97 |
| abstract_inverted_index.developed | 116 |
| abstract_inverted_index.duration. | 68, 124 |
| abstract_inverted_index.enhancing | 142 |
| abstract_inverted_index.graphical | 111 |
| abstract_inverted_index.influence | 63 |
| abstract_inverted_index.intensive | 79 |
| abstract_inverted_index.interface | 113 |
| abstract_inverted_index.intricate | 4 |
| abstract_inverted_index.nonlinear | 100 |
| abstract_inverted_index.overbreak | 52 |
| abstract_inverted_index.practical | 144 |
| abstract_inverted_index.utilizing | 13 |
| abstract_inverted_index.conducted. | 84 |
| abstract_inverted_index.estimation | 120 |
| abstract_inverted_index.facilitate | 118 |
| abstract_inverted_index.identified | 44 |
| abstract_inverted_index.meticulous | 36 |
| abstract_inverted_index.parameters | 28, 42, 56 |
| abstract_inverted_index.predicting | 7 |
| abstract_inverted_index.predictive | 72 |
| abstract_inverted_index.regression | 94 |
| abstract_inverted_index.throughout | 138 |
| abstract_inverted_index.algorithms. | 50 |
| abstract_inverted_index.predictions | 131 |
| abstract_inverted_index.real‒time | 119 |
| abstract_inverted_index.significant | 62 |
| abstract_inverted_index.techniques. | 17 |
| abstract_inverted_index.underscored | 87 |
| abstract_inverted_index.constructing | 32 |
| abstract_inverted_index.construction | 8, 67, 123, 140 |
| abstract_inverted_index.encompassing | 25 |
| abstract_inverted_index.Additionally, | 108 |
| abstract_inverted_index.comprehensive | 20 |
| abstract_inverted_index.cross-section | 55 |
| abstract_inverted_index.effectiveness | 89 |
| abstract_inverted_index.hyperparameter | 80 |
| abstract_inverted_index.relationships, | 101 |
| abstract_inverted_index.drill-and-blast | 11 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| corresponding_author_ids | https://openalex.org/A5080141958 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I1516879 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.4000000059604645 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.91528861 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |