基于Transformer的曼谷地基钻孔桩荷载-变形预测深度学习模型
Transformer-Based Deep Learning Model for Bored Pile Load-Deformation Prediction in Bangkok Subsoil
AI总结：
本文提出基于Transformer的深度学习模型，将曼谷地基土层剖面和桩体特征编码为输入，结合历史荷载-变形序列预测钻孔桩荷载-变形曲线，测试平均绝对误差5.72%，并可用于桩基参数化分析与设计优化。
摘要：
本文提出了一种基于Transformer架构的新型深度学习模型，用于预测曼谷地基中大直径钻孔桩的荷载-变形行为。该模型将土层剖面和桩体特征编码为分词输入，并生成荷载-变形曲线作为输出。该模型还将荷载-变形曲线的先前序列数据纳入解码器，以提高预测精度。模型在荷载-变形曲线预测中表现出令人满意的精度和泛化能力，测试数据的平均绝对误差为5.72%。该模型还可用于不同土层和桩体条件、桩截面、桩长和桩型下的参数化分析与设计优化。
英文摘要：
This paper presents a novel deep learning model based on the transformer architecture to predict the load-deformation behavior of large bored piles in Bangkok subsoil. The model encodes the soil profile and pile features as tokenization input, and generates the load-deformation curve as output. The model also incorporates the previous sequential data of load-deformation curve into the decoder to improve the prediction accuracy. The model also incorporates the previous sequential data of load-deformation curve into the decoder. The model shows a satisfactory accuracy and generalization ability for the load-deformation curve prediction, with a mean absolute error of 5.72% for the test data. The model could also be used for parametric analysis and design optimization of piles under different soil and pile conditions, pile cross section, pile length and type of pile.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
