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.