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This paper introduces a novel deep learning approach to predict theengineering properties of the ground improved by Rapid Impact Compaction (RIC),which is a ground improvement technique that uses a drop hammer to compact thesoil and fill layers. The proposed approach uses transformer-based neuralnetworks to capture the complex nonlinear relationships between the inputfeatures, such as the hammer energy, drop height, and number of blows, and theoutput variables, such as the cone resistance. The approach is applied to areal-world dataset from a trial test section for the new apron construction ofthe Utapao International Airport in Thailand. The results show that theproposed approach outperforms the existing methods in terms of predictionaccuracy and efficiency and provides interpretable attention maps that revealthe importance of different features for RIC prediction. The paper alsodiscusses the limitations and future directions of applying deep learningmethods to RIC prediction.

Transformer Based Model for Predicting Rapid Impact Compaction Outcomes: A Case Study of Utapao International Airport