ข้อมูลผลงานวิจัย

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.