Machine learning-based intelligent modeling of hydraulic conductivity of sandy soils considering a wide range of grain sizes

Engineering Geology, Journal Year: 2022, Volume and Issue: 311, P. 106899 - 106899

Published: Oct. 26, 2022

Language: Английский

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A new index for cutter life evaluation and ensemble model for prediction of cutter wear

Tunnelling and Underground Space Technology, Journal Year: 2022, Volume and Issue: 131, P. 104830 - 104830

Published: Nov. 14, 2022

Language: Английский

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COSMA-RF: New intelligent model based on chaos optimized slime mould algorithm and random forest for estimating the peak cutting force of conical picks

Transportation Geotechnics, Journal Year: 2022, Volume and Issue: 36, P. 100806 - 100806

Published: July 8, 2022

Language: Английский

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Dynamic prediction for attitude and position of shield machine in tunneling: A hybrid deep learning method considering dual attention

Advanced Engineering Informatics, Journal Year: 2023, Volume and Issue: 57, P. 102032 - 102032

Published: June 8, 2023

Language: Английский

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Integrative modeling of heterogeneous soil salinity using sparse ground samples and remote sensing images

Geoderma, Journal Year: 2023, Volume and Issue: 430, P. 116321 - 116321

Published: Jan. 4, 2023

Soil salinization is a major environmental risk caused by natural or human activities especially in arid and semi-arid regions. Machine learning for rapidly monitoring large-scale spatial soil becomes possible. However, machine often needs large training samples obtaining extensive information field investigation laborious difficult. In practice, the sampling datasets are sparse non-normally distributed. The intricacy of features extracted from remote sensing images increases model complexity leads to degradation prediction performance. To solve this problem, an integrative framework proposed predict salt content (SSC) based on light gradient boosting (LGBM). model, we first introduce data augmentation method (Mixup) improve sample diversity alleviate overfitting sparsity samples. generalization robustness different heterogeneity salinization, Mixup-LGBM adaptively jointly optimized combining hyperparameters feature selection Bayesian optimization framework. Furthermore, interpretability improved using shapley additive explanations (SHAP) value combination confidence synthetic through visualization importance assessment. addition, cases simulated test Case I, raw sample-sparsity algorithm has higher accuracy than other unused models. Ⅱ, extreme still achieves satisfactory results while models can’t learn any effective after multiple iterations. experimental reveal that can automatically find representative heterogeneous environments strong adaptability study areas. This finding indicates digital elevation (DEM) high influence SSC both Besides DEM, Manasi River Basin more sensitive activities, Werigan–Kuqa Delta Oasis factors. suitable predicting scenarios ensuring accuracy. considerable potential dealing with complex regression tasks.

Language: Английский

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State-of-the-art review on the use of AI-enhanced computational mechanics in geotechnical engineering

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(8)

Published: July 5, 2024

Abstract Significant uncertainties can be found in the modelling of geotechnical materials. This attributed to complex behaviour soils and rocks amidst construction processes. Over past decades, field has increasingly embraced application artificial intelligence methodologies, thus recognising their suitability forecasting non-linear relationships intrinsic review offers a critical evaluation AI methodologies incorporated computational mechanics for engineering. The analysis categorises four pivotal areas: physical properties, mechanical constitutive models, other characteristics relevant Among various analysed, ANNs stand out as most commonly used strategy, while methods such SVMs, LSTMs, CNNs also see significant level application. widely algorithms are Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), representing 35%, 19%, 17% respectively. extensive is domain accounting 59%, followed by applications at 16%. efficacy intrinsically linked type datasets employed, selected model input. study outlines future research directions emphasising need integrate physically guided adaptive learning mechanisms enhance reliability adaptability addressing multi-scale multi-physics coupled problems geotechnics.

Language: Английский

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Effects of binder proportion and curing condition on the mechanical characteristics of volcanic ash- and slag-based geopolymer mortars; machine learning integrated experimental study

Construction and Building Materials, Journal Year: 2023, Volume and Issue: 395, P. 132330 - 132330

Published: July 3, 2023

Language: Английский

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A deep transfer learning model for the deformation of braced excavations with limited monitoring data

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: July 1, 2024

The current deep learning models for braced excavation cannot predict deformation from the beginning of due to need a substantial corpus sufficient historical data training purposes. To address this issue, study proposes transfer model based on sequence-to-sequence two-dimensional (2D) convolutional long short-term memory neural network (S2SCL2D). can use existing other adjacent similar excavations achieve wall deflection prediction once limited amount monitoring target has been recorded. In absence data, numerical simulation project be employed instead. A weight update strategy is proposed improve accuracy by integrating stochastic gradient masking with an early stopping mechanism. illustrate methodology, in Hangzhou, China adopted. model, which uses either or as source domain, shows significant improvement performance when compared non-transfer model. Using even leads better than using actual excavations. results demonstrate that reasonably project.

Language: Английский

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A comparative analysis of hybrid RF models for efficient lithology prediction in hard rock tunneling using TBM working parameters

Acta Geophysica, Journal Year: 2024, Volume and Issue: 72(3), P. 1847 - 1866

Published: April 15, 2024

Language: Английский

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Evaluation of empirical and machine learning models for predicting shear wave velocity of granular soils based on laboratory element tests

Soil Dynamics and Earthquake Engineering, Journal Year: 2024, Volume and Issue: 183, P. 108805 - 108805

Published: June 28, 2024

Language: Английский

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