Transformer-Based Deep Learning Model for Bored Pile Load-Deformation Prediction in Bangkok Subsoil

TL;DR

This paper introduces a transformer-based deep learning model that accurately predicts load-deformation curves of bored piles in Bangkok's subsoil, aiding in design and analysis.

Contribution

The paper presents a novel transformer architecture tailored for pile load-deformation prediction, integrating soil and pile features for improved accuracy.

Findings

Mean absolute error of 5.72% on test data

Effective for parametric analysis and design optimization

Demonstrates good generalization ability

Abstract

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.