There are severe challenges for slurry pressure balance tunnel boring machine (TBM) tunnelling in sandy cobble soil of Beijing, Chengdu, and Lanzhou in China. And the problems caused by tunnelling from silty clay to sandy cobble stratum are more serious. With the change of stratum, the key parameters and surface settlement will change correspondingly. Controlling the key parameters and predicting the surface settlement accurately and efficiently is important for hazard mitigation and risk management. In this study, based on the Tsinghuayuan Tunnel project in Beijing, the key parameters and surface settlement while tunnelling from silty clay to sandy cobble stratum are studied. Firstly, the difference of key parameters while tunnelling in two different strata is analyzed. The analysis shows that immediate responses to changes in the stratum are recommended in order to ensure construction efficiency. Then, a refined 3D finite difference model is developed to simulate the slurry TBM tunnelling in different strata. For refined simulation, three key parameters obtained from measurement data were applied to the 3D models, and the simulation results were compared with the field data. Results show that the refined model has good performance in terms of the accuracy and efficiency. This study provides a good engineering practice reference for slurry TBM tunnelling in mixed strata.
The rock load acting on the lining of an underground excavation is influenced by multiple factors, including rock type, rock mass condition, depth, and construction method. This study focuses on quantifying the magnitude and distribution of the radial loads on the lining of a deep shaft constructed in hard rock by the so-called short-step method. The blasting-induced damage zone (BDZ) around the shaft was characterized using ultrasonic testing and incorporated into the convergence-confinement method (CCM) and 3D numerical analyses to assess the impact of BDZ on rock loading against the liner. The results show that excavation blasting of shafts is an important controlling factor for the degradation of the rock mass, while the orientation and magnitude of the principal stress had a minimal influence on the distribution of blast-induced damage. The analysis shows that increasing the depth of blast damage in the walls can increase the loads acting on the lining, and the shear loads acting on the liner could be significant for shafts sunk by the short-step method in an area with anisotropic in situ stresses.
In the past few years, the sparse representation (SR) graph-based semi-supervised learning (SSL) has drawn a lot of attention for its impressive performance in hyperspectral image classification with small numbers of training samples. Among these methods, the probabilistic class structure regularized sparse representation (PCSSR) approach, which introduces the probabilistic relationship between samples into the SR process, has shown its superiority over state-of-the-art approaches. However, this category of classification methods only apply another SR process to generate the probabilistic relationship, which focuses only on the spectral information but fails to utilize the spatial information. In this paper, we propose using the class adjusted spatial distance (CASD) to measure the distance between each two samples. We incorporate the proposed a CASD-based distance information into PCSSR mode to further increase the discriminability of original PCSSR approach. The proposed method considers not only the spectral information but also the spatial information of the hyperspectral data, consequently leading to significant performance improvement. Experimental results on different datasets demonstrate that compared with state-of-the-start classification models, the proposed method achieves the highest overall accuracies of 99.71%, 97.13%, and 97.07% on Botswana (BOT), Kennedy Space Center (KSC) and the truncated Indian Pines (PINE) datasets, respectively, with a small number of training samples selected from each class.
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