  This paper introduces a novel 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.
