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.

翻译：本文提出了一种基于Transformer架构的新型深度学习模型，用于预测曼谷地下土中大直径钻孔桩的荷载-变形行为。该模型将土层剖面与桩体特征编码为标记化输入，并生成荷载-变形曲线作为输出。模型还将荷载-变形曲线的前序序列数据引入解码器，以提升预测精度。实验结果表明，该模型在荷载-变形曲线预测中表现出令人满意的精度与泛化能力，测试数据的平均绝对误差为5.72%。该模型还可用于不同土体与桩体条件、桩体截面、桩长及桩型下的参数分析与设计优化。