Algorithm development for automated key block analysis in tunnels from LiDAR point cloud data: Fid-Bau Portal

Fachinformationsdienst BAUdigital

Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik

Algorithm development for automated key block analysis in tunnels from LiDAR point cloud data

Carter-Greaves, L.E. / Eyre, M. / Vogt, D. / Coggan, J.

Highlight • The algorithm operates in a fully autonomous fashion. • Capable of processing point clouds in near real-time, i.e. 9 million points in under one second. • Automatically extracts planar surfaces to generate a stereo-net. • Uses ISODATA to extract the joint sets. • Extends current systems by applying key block theory to identify high risk blocks.

Abstract The process of scaling and support installation in recently blasted tunnels is one of the most hazardous aspects of the underground construction process. An algorithm is developed that can process point clouds captured via LiDAR which can identify the rock mass discontinuities and perform a kinematic key-block analysis. Points clouds containing nine million points can be processed in under one second and the algorithm operates autonomously requiring no human input. The resultant inputs can provide insight into the underlying rock mass and its geo-mechanical behaviour to an entering scaling crew to aid in risk mitigation.

Zugriff

Zugriff prüfen

Verfügbarkeit in meiner Bibliothek prüfen

Bestellung bei Subito €

Seitennavigation

Dokumentinformationen

Ähnliche Titel

Exportieren, teilen und zitieren

Algorithm development for automated key block analysis in tunnels from LiDAR point cloud data

Carter-Greaves, L.E. / Eyre, M. / Vogt, D. / Coggan, J.

Highlight • The algorithm operates in a fully autonomous fashion. • Capable of processing point clouds in near real-time, i.e. 9 million points in under one second. • Automatically extracts planar surfaces to generate a stereo-net. • Uses ISODATA to extract the joint sets. • Extends current systems by applying key block theory to identify high risk blocks.

Abstract The process of scaling and support installation in recently blasted tunnels is one of the most hazardous aspects of the underground construction process. An algorithm is developed that can process point clouds captured via LiDAR which can identify the rock mass discontinuities and perform a kinematic key-block analysis. Points clouds containing nine million points can be processed in under one second and the algorithm operates autonomously requiring no human input. The resultant inputs can provide insight into the underlying rock mass and its geo-mechanical behaviour to an entering scaling crew to aid in risk mitigation.

Algorithm development for automated key block analysis in tunnels from LiDAR point cloud data

Carter-Greaves, L.E. / Eyre, M. / Vogt, D. / Coggan, J.

Highlight • The algorithm operates in a fully autonomous fashion. • Capable of processing point clouds in near real-time, i.e. 9 million points in under one second. • Automatically extracts planar surfaces to generate a stereo-net. • Uses ISODATA to extract the joint sets. • Extends current systems by applying key block theory to identify high risk blocks.

Abstract The process of scaling and support installation in recently blasted tunnels is one of the most hazardous aspects of the underground construction process. An algorithm is developed that can process point clouds captured via LiDAR which can identify the rock mass discontinuities and perform a kinematic key-block analysis. Points clouds containing nine million points can be processed in under one second and the algorithm operates autonomously requiring no human input. The resultant inputs can provide insight into the underlying rock mass and its geo-mechanical behaviour to an entering scaling crew to aid in risk mitigation.

Zugriff

Zugriff prüfen

Verfügbarkeit in meiner Bibliothek prüfen

Bestellung bei Subito €

Seitennavigation

Dokumentinformationen

Ähnliche Titel

Exportieren, teilen und zitieren

Dokumentinformationen

Titel:

Algorithm development for automated key block analysis in tunnels from LiDAR point cloud data

Beteiligte:

Carter-Greaves, L.E. (Autor:in) / Eyre, M. (Autor:in) / Vogt, D. (Autor:in) / Coggan, J. (Autor:in)

Erschienen in:

Tunnelling and Underground Space Technology ; 132

Erscheinungsdatum:

28.09.2022

ISSN:

DOI:

https://doi.org/10.1016/j.tust.2022.104787

Medientyp:

Aufsatz (Zeitschrift)

Format:

Elektronische Ressource

Sprache:

Englisch

Schlagwörter:

LIDAR , Kinematic analysis , Key block analysis , Scaling and support , Structural evaluation

Ähnliche Titel

Algorithm development for automated key block analysis in tunnels from LiDAR point cloud data

Carter-Greaves, L.E. / Eyre, M. / Vogt, D. et al. | Elsevier | 2023

Freier Zugriff

Automated Road Extraction and Analysis from LiDAR Point Cloud Data Using Local Optimization

Liu, Xuanzhen / Li, Hongjun | Springer Verlag | 2024

Automated Segmentation of Individual Tree Structures Using Deep Learning over LiDAR Point Cloud Data

Dong-Hyeon Kim / Chi-Ung Ko / Dong-Geun Kim et al. | DOAJ | 2023

Freier Zugriff

LIDAR rock mass characterisation and automated point cloud processing - Technical note

Online Contents | 2005

LIDAR rock mass characterisation and automated point cloud processing Technical note

British Library Online Contents | 2005