Machine Learning-Enhanced Analysis of Small-Strain Hardening Soil Model Parameters for Shallow Tunnels in Weak Soil
Accurate prediction of tunneling-induced settlements in shallow tunnels in weak soil is challenging, as advanced constitutive models, such as the small-strain hardening soil model (SS-HSM) require several input parameters. In this study, a case study was used as a benchmark to investigate the sensit...
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MDPI AG
2025-04-01
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| Series: | Geotechnics |
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| Online Access: | https://www.mdpi.com/2673-7094/5/2/26 |
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| author | Tzuri Eilat Alison McQuillan Amichai Mitelman |
| author_facet | Tzuri Eilat Alison McQuillan Amichai Mitelman |
| author_sort | Tzuri Eilat |
| collection | DOAJ |
| description | Accurate prediction of tunneling-induced settlements in shallow tunnels in weak soil is challenging, as advanced constitutive models, such as the small-strain hardening soil model (SS-HSM) require several input parameters. In this study, a case study was used as a benchmark to investigate the sensitivity of the SS-HSM parameters. An automated framework was developed, and 100 finite-element (FE) models were generated, representing realistic input ranges and inter-parameter relationships. The resulting distribution of predicted surface settlements resembled observed outcomes, exhibiting a tightly clustered majority of small displacements (less than 20 mm) alongside a minority of widely scattered large displacements. Subsequently, machine-learning (ML) techniques were applied to enhance data interpretation and assess predictive capability. Regression models were used to predict final surface settlements based on partial excavation stages, highlighting the potential for improved decision-making during staged excavation projects. The regression models achieved only moderate accuracy, reflecting the challenges of precise displacement prediction. In contrast, binary classification models effectively distinguished between small displacements and large displacements. Arguably, classification models offer a more attainable approach that better aligns with geotechnical engineering practice, where identifying favorable and adverse geotechnical conditions is more critical than precise predictions. |
| format | Article |
| id | doaj-art-c420ce60f44944f78a3c184836bd19b1 |
| institution | Kabale University |
| issn | 2673-7094 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Geotechnics |
| spelling | doaj-art-c420ce60f44944f78a3c184836bd19b12025-08-20T03:24:33ZengMDPI AGGeotechnics2673-70942025-04-01522610.3390/geotechnics5020026Machine Learning-Enhanced Analysis of Small-Strain Hardening Soil Model Parameters for Shallow Tunnels in Weak SoilTzuri Eilat0Alison McQuillan1Amichai Mitelman2Department of Civil Engineering, Ariel University, Ariel 4077625, IsraelRocscience Inc., Toronto, ON M5T 1V1, CanadaDepartment of Civil Engineering, Ariel University, Ariel 4077625, IsraelAccurate prediction of tunneling-induced settlements in shallow tunnels in weak soil is challenging, as advanced constitutive models, such as the small-strain hardening soil model (SS-HSM) require several input parameters. In this study, a case study was used as a benchmark to investigate the sensitivity of the SS-HSM parameters. An automated framework was developed, and 100 finite-element (FE) models were generated, representing realistic input ranges and inter-parameter relationships. The resulting distribution of predicted surface settlements resembled observed outcomes, exhibiting a tightly clustered majority of small displacements (less than 20 mm) alongside a minority of widely scattered large displacements. Subsequently, machine-learning (ML) techniques were applied to enhance data interpretation and assess predictive capability. Regression models were used to predict final surface settlements based on partial excavation stages, highlighting the potential for improved decision-making during staged excavation projects. The regression models achieved only moderate accuracy, reflecting the challenges of precise displacement prediction. In contrast, binary classification models effectively distinguished between small displacements and large displacements. Arguably, classification models offer a more attainable approach that better aligns with geotechnical engineering practice, where identifying favorable and adverse geotechnical conditions is more critical than precise predictions.https://www.mdpi.com/2673-7094/5/2/26hardening soil modelshallow tunnelsmachine learningtunneling-induced settlementsfinite-element modellinggeotechnical engineering |
| spellingShingle | Tzuri Eilat Alison McQuillan Amichai Mitelman Machine Learning-Enhanced Analysis of Small-Strain Hardening Soil Model Parameters for Shallow Tunnels in Weak Soil Geotechnics hardening soil model shallow tunnels machine learning tunneling-induced settlements finite-element modelling geotechnical engineering |
| title | Machine Learning-Enhanced Analysis of Small-Strain Hardening Soil Model Parameters for Shallow Tunnels in Weak Soil |
| title_full | Machine Learning-Enhanced Analysis of Small-Strain Hardening Soil Model Parameters for Shallow Tunnels in Weak Soil |
| title_fullStr | Machine Learning-Enhanced Analysis of Small-Strain Hardening Soil Model Parameters for Shallow Tunnels in Weak Soil |
| title_full_unstemmed | Machine Learning-Enhanced Analysis of Small-Strain Hardening Soil Model Parameters for Shallow Tunnels in Weak Soil |
| title_short | Machine Learning-Enhanced Analysis of Small-Strain Hardening Soil Model Parameters for Shallow Tunnels in Weak Soil |
| title_sort | machine learning enhanced analysis of small strain hardening soil model parameters for shallow tunnels in weak soil |
| topic | hardening soil model shallow tunnels machine learning tunneling-induced settlements finite-element modelling geotechnical engineering |
| url | https://www.mdpi.com/2673-7094/5/2/26 |
| work_keys_str_mv | AT tzurieilat machinelearningenhancedanalysisofsmallstrainhardeningsoilmodelparametersforshallowtunnelsinweaksoil AT alisonmcquillan machinelearningenhancedanalysisofsmallstrainhardeningsoilmodelparametersforshallowtunnelsinweaksoil AT amichaimitelman machinelearningenhancedanalysisofsmallstrainhardeningsoilmodelparametersforshallowtunnelsinweaksoil |