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• W2945707192 abstract "Abstract Tunnel Boring Machines (TBMs) are large multi-million pound machines used to excavate underground tunnels. In order to make best use of the high-speed performance of a TBM and guarantee the safety of excavation, it is important to know the local geology, structures and ground properties ahead of the TBM cutter head, especially in complex geological conditions (e.g. karst caves). By working with experienced geophysical experts, tunnelling engineers/consultants and TBM manufacturers, we propose a novel web-based visualisation platform to help TBM operators efficiently manage, process and visualise the TBM parameters, the geology map created by geo-experts based on boreholes, and especially the imaging data captured by an on-board ground imaging system for “seeing through” the ground beyond the excavation surface. Informative visualisation interfaces were designed to facilitate interpretation of the imaging data and adding annotation by users; algorithms were developed for automatic detection of features and probable events by fusion of radar and seismic imaging data; and a back-end database was designed to store all such relevant information for supporting more advanced interpretation in the future. The web-based architecture not only allows the visualisation platform to be directly linked to on-board sensors (e.g. ground penetrating radars, seismic sensors), but also allows users away from the job site to access the captured data using a standard web browser, enabling a collaborative interpretation process. The data processing, management and visualisation platform presented in this paper is flexible with respect to different imaging sensors and modalities, so it is highly adaptable for any other ground imaging systems for tunnel geology inspection, underground utility surveys, etc." @default.
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• W2945707192 date "2019-09-01" @default.
• W2945707192 modified "2023-09-24" @default.
• W2945707192 title "Web-based visualisation for look-ahead ground imaging in tunnel boring machines" @default.
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• W2945707192 doi "https://doi.org/10.1016/j.autcon.2019.04.025" @default.
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