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...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.展开更多
基金the Research Funding of Shantou University for New Faculty Member(No.NTF19024-2019)the National Nature Science Foundation of China(No.41372283)。
文摘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.
