Predicting Rapid Impact Compaction Outcomes with Transformer-Based Deep Learning Models

Abstract

Abstract This paper introduces a novel generative deep learning approach to predict the engineering properties of the ground improved by Rapid Impact Compaction (RIC), which is a ground improvement technique that uses a drop hammer to compact the soil and fill layers. The proposed approach uses transformer-based neural networks to capture the complex nonlinear relationships between the input features, such as the hammer energy, drop height, and number of blows, and the output variables, such as the cone resistance. The approach is applied to a real-world dataset from a trial test section for the new apron construction of the Utapao International Airport in Thailand. The results show that the proposed approach outperforms the existing methods in terms of prediction accuracy and efficiency and provides interpretable attention maps that reveal the importance of different features for RIC prediction. The paper also discusses the limitations and future directions of applying deep learning methods to RIC prediction.