<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Punch-through is a major threat to the jack-up unit, especially at well sites with layered stiff-over-soft clays. A model is proposed to predict the spudcan penetration resistance in stiff-over-soft clays, based on the random forest (RF) method. The RF model was trained and tested with numerical simulation results obtained through the Finite Element model, implemented with the Coupled Eulerian Lagrangian (CEL) approach. With the proposed CEL model, the effects of the stiff layer thickness, undrained shear strength ratio, and the undrained shear strength of the soft layer on the bearing characteristics, as well as the soil failure mechanism, were numerically studied. A simplified resistance profile model of penetration in stiff-over-soft clays is proposed, divided into three sections by the peak point and the transition point. The importance of soil parameters to the penetration resistance was analysed. Then, the trained RF model was tested against the test set, showing a good prediction of the numerical cases. Finally, the trained RF was validated against centrifuge tests. The RF model successfully captured the punch-through potential, and was verified using data recorded in the field, showing advantages over the SNAME guideline. It is supposed that the trained RF model should give a good prediction of the spudcan penetration resistance profile, especially if trained with more field data.</p></abstract>

Punch-through is a major threat to the jack-up unit, especially at well sites with layered stiff-over-soft clays. A model is proposed to predict the spudcan penetration resistance in stiff-over-soft clays, based on the random forest (RF) method. The RF model was trained and tested with numerical simulation results obtained through the Finite Element model, implemented with the Coupled Eulerian Lagrangian (CEL) approach. With the proposed CEL model, the effects of the stiff layer thickness, undrained shear strength ratio, and the undrained shear strength of the soft layer on the bearing characteristics, as well as the soil failure mechanism, were numerically studied. A simplified resistance profile model of penetration in stiff-over-soft clays is proposed, divided into three sections by the peak point and the transition point. The importance of soil parameters to the penetration resistance was analysed. Then, the trained RF model was tested against the test set, showing a good prediction of the numerical cases. Finally, the trained RF was validated against centrifuge tests. The RF model successfully captured the punch-through potential, and was verified using data recorded in the field, showing advantages over the SNAME guideline. It is supposed that the trained RF model should give a good prediction of the spudcan penetration resistance profile, especially if trained with more field data.

A Random Forest Model for the Prediction of Spudcan Penetration Resistance in Stiff-Over-Soft Clays

Polish Maritime Research

This work is licensed under the Creative Commons Attribution 4.0 International License.

Punch-through is a major threat to the jack-up unit, especially at well sites with layered stiff-over-soft clays. A model is proposed to predict the spudcan...

A Random Forest Model for the Prediction of Spudcan Penetration Resistance in Stiff-Over-Soft Clays

Pubblicato online: 24 dic 2020

Pagine: 130 - 138

DOI: https://doi.org/10.2478/pomr-2020-0073

Parole chiave
Machine learning, Random forest, Jack-up, Penetration resistance, Stiff-over-soft clays

© 2020 Pan Gao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Random Forest Model for the Prediction of Spudcan Penetration Resistance in Stiff-Over-Soft Clays

Pubblicato online: 24 dic 2020

Pagine: 130 - 138

DOI: https://doi.org/10.2478/pomr-2020-0073

Parole chiaveMachine learning, Random forest, Jack-up, Penetration resistance, Stiff-over-soft clays

© 2020 Pan Gao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Machine learning, Random forest, Jack-up, Penetration resistance, Stiff-over-soft clays