CTrPile: A Computer Vision and Transformer Approach for Pile Capacity Estimation from Dynamic Pile Load Test

Dynamic pile load tests are essential for verifying the ultimate limit state for pile design in geotechnical engineering. However, conventional methods for monitoring these tests, such as strain gauges and accelerometers, are expensive and labor-intensive. This paper proposes a novel method that uses computer vision and artificial markers to measure pile head movement during dynamic pile load tests, and a transformer-based deep learning model to predict pile capacity from the movement data. The proposed method is low-cost, easy-to-use, and accurate, with a mean absolute error of 2.4% for pile capacity prediction using K-fold cross-validation. The paper also presents a sensitivity analysis of the transformer model with respect to the number of heads and layers, which indicated the optimal settings to avoid overfitting of the training data. The paper discusses the limitations of the proposed method, such as the dependency on the camera position and suggests future directions of the research, such as incorporating other features and improving the data quality. The proposed method can be applied in real cases of dynamic pile load tests to increase the number of tests on site and to ensure the safety and reliability of pile design.