Improved Prediction of Peak Resistance for Spudcan Penetration in Sand Layer Overlying Clay

Zheng, Jingbin; Chen, Yangbin; Chen, Xiao; Wang, Dong; Jing, Shaodong

doi:10.1061/(asce)gt.1943-5606.0002697

Cited by 6 publications

(1 citation statement)

References 25 publications

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“…The modifications include four aspects, which are detailed as follows. Zheng et al [20] reported that the peak resistances were significantly overestimated for sand overlying clay with high undrained shear strength using the distribution factors estimated by Equation (A3). They have hence collected centrifuge test data from existing publications, carried out additional centrifuge tests and LDFE analyses, and proposed a new design formula for D F , which is expressed as…”

Section: Modified Predictive Modelmentioning

confidence: 99%

Optimization for the Assessment of Spudcan Peak Resistance in Clay–Sand–Clay Deposits

Zheng

Zhang

Wang

et al. 2021

JMSE

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Clay–sand–clay deposits are commonly encountered in the offshore field. For spudcan installation in this soil stratigraphy, the potential for punch-through exists, with the peak penetration resistance formed within the interbedded sand layer. Therefore, a careful assessment of the penetration resistance profile has to be performed. Based on the recently proposed failure-stress-dependent model, this paper presents a modified predictive model for estimating the peak resistance. The modified model incorporates the bearing capacity depth factor and the protruded soil plug in the bottom clay layer into the formulation. It is proven that the modified predictive model provides improved deterministic estimations for the peak resistances measured in centrifuge tests. Based on the modified predictive model, a parameter optimization technique is utilized to optimize the prediction of peak resistance using penetration resistances observed beforehand. A detailed application procedure is proposed and applied to the centrifuge tests accumulated from existing publications, with further improvement on the predictions demonstrated. The proposed parameter optimization procedure combined with the modified predictive model provides an approach to perform real-time optimization for assessing spudcan peak resistance in clay–sand–clay deposits.

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Section: Modified Predictive Modelmentioning

confidence: 99%