• Geomechanics and Engineering
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• v.31 no.1
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• pp.53-69
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• 2022

In densely built areas, the development of underground transportation systems often involves twin excavations, which are sometimes unavoidably constructed adjacent to existing piled foundations. Because soil stiffness degrades with induced stress release and shear strain during excavation, it is vital to investigate the piled raft responses to subsequent excavation after the first tunnel in a twin-excavation system. The effects of deep excavations on existing piled foundations have been extensively investigated, but the influence of twin excavations on a piled raft is seldom reported in the literature. In this study, three-dimensional numerical analyses were carried out to investigate the influence of sand density on an existing piled raft (with a working load on top of the raft) due to twin excavations. A wide range of relative density (D_r) from loosest (30%), loose to medium (50% and 70%), and densest (90%) were selected to investigate the effects on settlement and load transfer mechanism of the piled raft during twin excavations. An advanced hypoplastic sand model (which can capture small-strain stiffness and stress-state dependent dilatancy of sand) was adopted. The model parameters are calibrated against centrifuge test results in sand reported in the literature. From the computed results, it is found that twin excavations in loose sand (D_r=30%) caused the most significant settlement. This is because of the higher stiffness of denser sand (D_r=90%) than that of loose sand. In contrast, a much larger tilting (maximum magnitude=0.18%) was computed in dense sand than in loose sand after the completion of the first excavation. As far as the load transfer mechanism along the piles is concerned, an upward load transfer to mobilize shaft resistance is observed in loose sand. On the contrary, a downward load transfer is observed in dense sand.

• Tunnel and Underground Space
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• v.33 no.4
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• pp.281-298
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• 2023

Continuous research efforts are being devoted to unmanned mobile platforms for lunar exploration. There is an ongoing demand for real-time information processing to accurately determine the positioning and mapping of areas of interest on the lunar surface. To apply deep learning processing and analysis techniques to practical rovers, research on software integration and optimization is imperative. In this study, a foundational investigation has been conducted on real-time analysis of virtual lunar base construction site images, aimed at automatically quantifying spatial information of key objects. This study involved transitioning from an existing region-based object recognition algorithm to a boundary box-based algorithm, thus enhancing object recognition accuracy and inference speed. To facilitate extensive data-based object matching training, the Batch Hard Triplet Mining technique was introduced, and research was conducted to optimize both training and inference processes. Furthermore, an improved software system for object recognition and identical object matching was integrated, accompanied by the development of visualization software for the automatic matching of identical objects within input images. Leveraging satellite simulative captured video data for training objects and moving object-captured video data for inference, training and inference for identical object matching were successfully executed. The outcomes of this research suggest the feasibility of implementing 3D spatial information based on continuous-capture video data of mobile platforms and utilizing it for positioning objects within regions of interest. As a result, these findings are expected to contribute to the integration of an automated on-site system for video-based construction monitoring and control of significant target objects within future lunar base construction sites.