Knowledge-based U-Net and transfer learning for automatic boundary segmentation

Xiaoqi Zhou

doi:10.1016/j.aei.2023.102243

Knowledge-based U-Net and transfer learning for automatic boundary segmentation

2024-01-01

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AI

Stephen K. Suryasentana

DOI：10.1016/j.aei.2023.102243

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Abstract

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An effective soil classification method is essential for soil layering interpretation. Traditional methods rely on empirical formulae or a general unified system, which may not be accurate for every site. Recent advancements in deep learning have shown promising results in a wide range of practical domains, which provides motivation to explore its potential application in soil classification. A U-shaped convolutional neural network (U-Net), coupled with multiple source data to incorporate prior knowledge, is adopted in this study to predict the depths of soil stratum boundaries based on cone penetration testing (CPT), standard penetration testing (SPT) and laboratory index testing data. The U-Net model is first pre-trained on open-access data from other sites around the world and then trained through transfer learning on datasets gathered specifically for the Suzhou No.6 metroline project. Comparison is made from different metrics to investigate how pre-training benefits model performance. The soil boundaries in nine selected output images are extracted and reversed to their original depth for visualization of a subsurface profile. Comparisons to the benchmark interpretations show that the predicted soil profiles have reasonably good agreement with the benchmark profiles developed by an engineering expert. The results indicate that the proposed method is effective and efficient for the prediction of soil stratification.

Keywords:

Subsurface profile

Multiple source data

U-Net

Transfer learning

CPT

SPT

Knowledge-based U-Net and transfer learning for automatic boundary segmentation

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