Detection of subway tunnel distress is a crucial task for ensuring public safety. It is typically performed manually by technical workers, which has become increasingly expensive due to the decline in working-age population and increase in aging subway tunnels. We have previously proposed deep learning-based approaches for segmenting distress regions in subway tunnel images to reduce the maintenance cost; however, these approaches still have some difficulties in practical application and cannot address some worksite requirements. Thus, in this study, we developed technologies for enabling deep learning-based distress segmentation approaches to adapt better to practical scenarios. We propose a soft label approach for model training to specifically address the issue of slight deviation in the manual annotations of cracks. Additionally, for model evaluation, we propose using region-based metrics as a supplement to the commonly employed pixel-based metrics for a better evaluation of the crack segmentation performance. In the experiments, our distress segmentation approach, when enhanced by the above technologies, demonstrates the potential for practical use in subway tunnel inspection.

Geological surveys conducted at the initial and design stages of a project are imprecise due to the limitation in both the time and the budgets provided to conduct them, and often require further verification. With the advent of artificial intelligence, several attempts have been made to train machine learning models to classify, assess, and predict in situ rock types and properties. The objective of this study was to use readily obtainable data to train machine learning algorithms in classifying rock types from the local geology of a construction site. The output would then be automatically displayed in a 3D digital twin model and made available to all stakeholders of a project, thereby maximizing information utility. The machine learning models were trained to identify several rock types including slate, greenstone, chert, and limestone from the trial site. The models used were a gradient boosted decision tree, a normalization-free net, and a custom neural net, trained using drilling parameter, photographic, and hyperspectral imaging data of drill cuttings, respectively. The best performance was obtained by the model trained using hyperspectral imaging data, likely due to the unique spectral signatures produced by minerals in the near-infrared frequency range.