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

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

Sheng liang Lu, Ning Zhang, Shui long Shen, Annan Zhou, Hu zhong Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)

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.

Translated title of the contributionA deep-learning method for evaluating shaft resistance of the cast-in-site pile on reclaimed ground using field data
Original languageChinese (Simplified)
Pages (from-to)496-508
Number of pages13
JournalJournal of Zhejiang University: Science A
Volume21
Issue number6
DOIs
Publication statusPublished - 1 Jun 2020
Externally publishedYes

Keywords

  • Cast-in-site pile
  • Deep-learning method
  • Field test
  • Reclaimed ground
  • Shaft resistance
  • TU473.1

ASJC Scopus subject areas

  • General Engineering

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