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Journal of Zhejiang University SCIENCE A

ISSN 1673-565X(Print), 1862-1775(Online), Monthly

A deep-learning method for evaluating shaft resistance of the cast-in-site pile on reclaimed ground using field data

Abstract: This study proposes a deep learning-based approach for shaft resistance evaluation of cast-in-site piles on reclaimed ground, independent of theoretical hypotheses and engineering experience. A series of field tests was first performed to investigate the characteristics of the shaft resistance of cast-in-site piles on reclaimed ground. Then, an intelligent approach based on the long short term memory deep-learning technique was proposed to calculate the shaft resistance of the cast-in-site pile. The proposed method allows accurate estimation of the shaft resistance of cast-in-site piles, not only under the ultimate load but also under the working load. Comparisons with empirical methods confirmed the effectiveness of the proposed method for the shaft resistance estimation of cast-in-site piles on reclaimed ground in offshore areas.

Key words: Deep-learning method; Cast-in-site pile; Shaft resistance; Field test; Reclaimed ground

Chinese Summary  <39> 基于现场试验的复垦地层灌注桩侧摩阻力的深度学习评价方法

关键词组:深度学习方法; 灌注桩; 侧摩阻力; 现场试验; 复垦地层


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DOI:

10.1631/jzus.A1900544

CLC number:

TU473.1

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On-line Access:

2020-06-10

Received:

2019-10-24

Revision Accepted:

2020-03-20

Crosschecked:

2020-05-27

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