UNet-like transformer for 1D soil stratification using cone penetration test and borehole data

Subsurface stratification is crucial for the construction safety of underground projects. The one-dimensional (1D) soil stratification aims at identifying segmentation points that separate soil strata. Current engineering practice mainly requires human judgement, which is time-consuming, labour-intensive, and heavily relies on domain expertise. Other probabilistic methods, such as Bayesian approaches, usually involve complex expressions. With the advent of artificial intelligence, deep learning has emerged as a powerful tool in various domains. The UNet, as a typical convolutional neural network, has been extensively utilized for its superior performance in segmentation tasks, but struggles to capture global and long-range semantic information due to the locality of convolution operations. To realize intelligent and automatic 1D soil stratification, this paper introduces a UNet-like Transformer (ULTra) that integrates multiple data sources, including cone penetration test and borehole data, to incorporate prior knowledge. The architecture features a multi-level Transformer with shifted windows in both the encoder and decoder to extract context features and restore spatial resolution, respectively. Experimental results demonstrate that the ULTra outperforms other UNet variants, particularly in detecting minor textures and local details, underscoring the benefits of integrating Transformers into a standard UNet. Case studies indicate that compared with probabilistic methods, the ULTra enables automatic 1D soil stratification using original exploration data with less human intervention, which is fast, effective, and could be continuously improved through interaction with human knowledge, thus streamlining the intelligent data analysis.