JZUS - Journal of Zhejiang University SCIENCE

Journal of Zhejiang University SCIENCE A

Accepted manuscript available online (unedited version)

Machine learning for soil parameter inversion enhanced with Bayesian optimization

Author(s): Anfeng HU¹, Chi WANG^1, ², Senlin XIE¹, Zhirong XIAO³, Tang LI¹, Ang XU¹
Affiliation(s): ¹Research Center of Coastal and Urban Geotechnical Engineering, Zhejiang University, Hangzhou 310058, China; more
Corresponding email(s): 100106@zust.edu.cn
Key Words: ABAQUS; Bayesian optimization; Machine learning algorithms; Parameter inversion; Settlement prediction

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Anfeng HU¹, Chi WANG^1,², Senlin XIE¹, Zhirong XIAO³, Tang LI¹, Ang XU¹. Machine learning for soil parameter inversion enhanced with Bayesian optimization[J]. Journal of Zhejiang University Science A,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.A2400568

@article{title="Machine learning for soil parameter inversion enhanced with Bayesian optimization",
author="Anfeng HU¹, Chi WANG^1,², Senlin XIE¹, Zhirong XIAO³, Tang LI¹, Ang XU¹",
journal="Journal of Zhejiang University Science A",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/jzus.A2400568"
}

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%A Chi WANG^1
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%A Senlin XIE¹
%A Zhirong XIAO³
%A Tang LI¹
%A Ang XU¹
%J Journal of Zhejiang University SCIENCE A
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%@ 1673-565X
%D in press
%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/jzus.A2400568"

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A1 - Anfeng HU¹
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A1 - Tang LI¹
A1 - Ang XU¹
J0 - Journal of Zhejiang University Science A
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doi="https://doi.org/10.1631/jzus.A2400568"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: Machine learning has strong potential for soil settlement prediction, but determining hyperparameters for machine learning models is often intricate and laborious. Therefore, we apply Bayesian optimization to determine optimal hyperparameter combinations, enhancing the effectiveness of machine learning models for soil parameter inversion. The machine learning models are trained using numerical simulation data generated with the Modified Cam-Clay model in ABAQUS, and their performance is evaluated using ground set-tlement monitoring data from an airport runway. Five optimized machine learning models�decision tree, random forest, support vector regression, deep neural network, and one-dimensional convolutional neural network�are compared in terms of their accuracy for soil parameter inversion and settlement prediction. The results indicate that Bayesian optimization efficiently utilizes prior knowledge to identify optimal hyperparameters, significantly improving model performance. Among the evaluated models, the one-dimensional convolutional neural network achieves the highest accuracy in soil parameter inversion, generating settlement predictions that closely match real monitoring data. These findings demonstrate the effectiveness of the proposed approach for soil parameter inversion and settlement prediction, and how Bayesian optimization can refine the model selection process.

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