Ruiyu Liang is a researcher in data visualisation, visual analytics, and multimodal data fusion within the context of mining engineering and geohazard management. His work focuses on integrating advanced visual computing, data‑driven modelling, and intelligent analytics to support the digital transformation of underground mining operations. He has contributed to topics including 3D visual analytics frameworks, geo‑hazard prediction, drill‑core recognition, mining method evaluation, and immersive virtual environments for training and education. His recent work includes developing a data‑driven visualisation system and multimodal fusion platform for underground mines, as well as collaborative studies on wildfire modelling and agentic AI.
Sepasgozar SME; Ahmad Khan A; Shirowzhan S; Garzon Romero JS; Pettit C; Zhang C; Oh J; Liang R, 2024, 'Immersive virtual environments and digital twin applications for education and training: Trends in construction, mining, and urban planning studies', in Digital Twin Adoption and BIM-GIS Implementation, Routledge, London, pp. 66 - 109, http://dx.doi.org/10.1201/9781003507000-5
Journal articles | 2024
Liang R; Huang C; Zhang C; Li B; Saydam S; Canbulat I, 2024, 'Data Diagram Design and Data Management for Visualisation and Analytics Fusion in The Mining Industry', Engineered Science, 32, http://dx.doi.org/10.30919/es1036
Journal articles | 2024
Liang R; Zhang C; Huang C; Li B; Saydam S; Canbulat I; Munsamy L, 2024, 'Multimodal data fusion for geo-hazard prediction in underground mining operation', Computers and Industrial Engineering, 193, http://dx.doi.org/10.1016/j.cie.2024.110268
Journal articles | 2024
Liang R; Zhang C; Li B; Saydam S; Canbulat I; Munsamy L, 2024, 'Data-driven visual model development and 3D visual analytics framework for underground mining', Tunnelling and Underground Space Technology, 153, http://dx.doi.org/10.1016/j.tust.2024.106054
Journal articles | 2023
Liang R; Huang C; Zhang C; Li B; Saydam S; Canbulat I, 2023, 'Exploring the Fusion Potentials of Data Visualization and Data Analytics in the Process of Mining Digitalization', IEEE Access, 11, pp. 40608 - 40628, http://dx.doi.org/10.1109/ACCESS.2023.3267813
Journal articles | 2023
Xu S; Ma J; Liang R; Zhang C; Li B; Saydam S; Canbulat I, 2023, 'Intelligent recognition of drill cores and automatic RQD analytics based on deep learning', Acta Geotechnica, 18, pp. 6027 - 6050, http://dx.doi.org/10.1007/s11440-023-02011-2
Preprints | 2025
Xu H; Zlatanova S; Liang R; Canbulat I, 2025, Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learning, http://dx.doi.org/10.48550/arxiv.2506.02485
