8
[2] CiVelekler, E., &Pekkan, E. (2022). The application
of GIS in visualization of geotechnical data (SPT-
Soil Properties): A case study in Eskisehir-Tepebaşı,
Turkey. International Journal of Engineering and
Geosciences, 7(3), 302–313. https://doi.org/10.26833/
ijeg.980611.
[3] Dos Santos, S., &Brodlie, K. (2004). Gaining
understanding of multivariate and multidimen-
sional data through visualization. Computers &
Graphics, 28(3), 311–325. https://doi.org/10.1016/j.
cag.2004.03.013.
[4] Kehrer, J., &Hauser, H. (2013). Visualization and
Visual Analysis of Multifaceted Scientific Data:
A Survey. IEEE Transactions on Visualization and
Computer Graphics, 19(3), 495–513. https://doi.
org/10.1109/TVCG.2012.110.
[5] Kim, M., Kim, H.-S., &Chung, C.-K. (2020). A
Three-Dimensional Geotechnical Spatial Modeling
Method for Borehole Dataset Using Optimization
of Geostatistical Approaches. KSCE Journal of
Civil Engineering, 24(3), 778–793. https://doi.
org/10.1007/s12205-020-1379-1.
[6] Liang, R., Zhang, C., Huang, C., Li, B., Saydam, S.,
Canbulat, I., &Munsamy, L. (2024). Multimodal
data fusion for geo-hazard prediction in under-
ground mining operation. Computers &Industrial
Engineering, 193, 110268. https://doi.org/10.1016/j.
cie.2024.110268.
[7] Lyu, B., &Wang, Y. (2024). Immersive visualization
of 3D subsurface ground model developed from sparse
boreholes using virtual reality (VR). Underground
Space, 17, 188–206. https://doi.org/10.1016/j.
undsp.2023.11.004.
[8] Ninić, J., Gamra, A., &Ghiassi, B. (2024). Real-
time assessment of tunnelling-induced damage to
structures within the building information modelling
framework. Underground Space, 14, 99–117. https://
doi.org/10.1016/j.undsp.2023.05.010.
[9] Patwardhan, A., &Karim, R. (2024). Ground sup-
port condition monitoring through point cloud
analytics. Deep Mining 2024: Proceedings of the
10th International Conference on Deep and High
Stress Mining, 631–642. https://doi.org/10.36487/
ACG_repo/2465_38.
[10] Providakis, S., Rogers, C. D. F., &Chapman,
D. N. (2019). Predictions of settlement risk
induced by tunnelling using BIM and 3D visual-
ization tools. Tunnelling and Underground Space
Technology, 92, 103049. https://doi.org/10.1016/j.
tust.2019.103049.
[11] Puppala, A. J., Congress, S. S. C., Bheemasetti,
T. V., &Caballero, S. (2018). Geotechnical Data
Visualization and Modeling of Civil Infrastructure
Projects. In X. Shi, Z. Liu, &J. Liu (Eds.), Proceedings
of GeoShanghai 2018 International Conference:
Transportation Geotechnics and Pavement Engineering
(pp. 1–12). Springer Singapore. https://doi.
org/10.1007/978-981-13-0011-0_1.
[12] Sekiya, H., Masuda, K., Nagakura, S., &Inuzuka,
S. (2022). Determination of shield tunnel defor-
mation under train load using MEMS accel-
erometers. Tunnelling and Underground Space
Technology, 126, 104535. https://doi.org/10.1016/j.
tust.2022.104535.
[13] Sternberg, B., Ryan, T., McGill, J. &Breitrick, M.
(1988). The San Xavier Geophysics and Tunnel
Detection Test Site. Laboratory for Subsurface
Imaging (LASI). University of Arizona, 156 p.
[14] Tukey, J. W. (1990). Data-Based Graphics: Visual
Display in the Decades to Come. Statistical Science,
5(3). https://doi.org/10.1214/ss/1177012101.
[15] Wang, R.-X., Wang, R., Fu, P., &Zhang, J.-M. (2020).
Portable interactive visualization of large-scale simu-
lations in geotechnical engineering using Unity3D.
Advances in Engineering Software, 148, 102838.
https://doi.org/10.1016/j.advengsoft.2020.102838.
[16] Wilson, C.A. (1960). Ore Controls of the San Xavier
Mine, MS Thesis, University of Arizona.
[2] CiVelekler, E., &Pekkan, E. (2022). The application
of GIS in visualization of geotechnical data (SPT-
Soil Properties): A case study in Eskisehir-Tepebaşı,
Turkey. International Journal of Engineering and
Geosciences, 7(3), 302–313. https://doi.org/10.26833/
ijeg.980611.
[3] Dos Santos, S., &Brodlie, K. (2004). Gaining
understanding of multivariate and multidimen-
sional data through visualization. Computers &
Graphics, 28(3), 311–325. https://doi.org/10.1016/j.
cag.2004.03.013.
[4] Kehrer, J., &Hauser, H. (2013). Visualization and
Visual Analysis of Multifaceted Scientific Data:
A Survey. IEEE Transactions on Visualization and
Computer Graphics, 19(3), 495–513. https://doi.
org/10.1109/TVCG.2012.110.
[5] Kim, M., Kim, H.-S., &Chung, C.-K. (2020). A
Three-Dimensional Geotechnical Spatial Modeling
Method for Borehole Dataset Using Optimization
of Geostatistical Approaches. KSCE Journal of
Civil Engineering, 24(3), 778–793. https://doi.
org/10.1007/s12205-020-1379-1.
[6] Liang, R., Zhang, C., Huang, C., Li, B., Saydam, S.,
Canbulat, I., &Munsamy, L. (2024). Multimodal
data fusion for geo-hazard prediction in under-
ground mining operation. Computers &Industrial
Engineering, 193, 110268. https://doi.org/10.1016/j.
cie.2024.110268.
[7] Lyu, B., &Wang, Y. (2024). Immersive visualization
of 3D subsurface ground model developed from sparse
boreholes using virtual reality (VR). Underground
Space, 17, 188–206. https://doi.org/10.1016/j.
undsp.2023.11.004.
[8] Ninić, J., Gamra, A., &Ghiassi, B. (2024). Real-
time assessment of tunnelling-induced damage to
structures within the building information modelling
framework. Underground Space, 14, 99–117. https://
doi.org/10.1016/j.undsp.2023.05.010.
[9] Patwardhan, A., &Karim, R. (2024). Ground sup-
port condition monitoring through point cloud
analytics. Deep Mining 2024: Proceedings of the
10th International Conference on Deep and High
Stress Mining, 631–642. https://doi.org/10.36487/
ACG_repo/2465_38.
[10] Providakis, S., Rogers, C. D. F., &Chapman,
D. N. (2019). Predictions of settlement risk
induced by tunnelling using BIM and 3D visual-
ization tools. Tunnelling and Underground Space
Technology, 92, 103049. https://doi.org/10.1016/j.
tust.2019.103049.
[11] Puppala, A. J., Congress, S. S. C., Bheemasetti,
T. V., &Caballero, S. (2018). Geotechnical Data
Visualization and Modeling of Civil Infrastructure
Projects. In X. Shi, Z. Liu, &J. Liu (Eds.), Proceedings
of GeoShanghai 2018 International Conference:
Transportation Geotechnics and Pavement Engineering
(pp. 1–12). Springer Singapore. https://doi.
org/10.1007/978-981-13-0011-0_1.
[12] Sekiya, H., Masuda, K., Nagakura, S., &Inuzuka,
S. (2022). Determination of shield tunnel defor-
mation under train load using MEMS accel-
erometers. Tunnelling and Underground Space
Technology, 126, 104535. https://doi.org/10.1016/j.
tust.2022.104535.
[13] Sternberg, B., Ryan, T., McGill, J. &Breitrick, M.
(1988). The San Xavier Geophysics and Tunnel
Detection Test Site. Laboratory for Subsurface
Imaging (LASI). University of Arizona, 156 p.
[14] Tukey, J. W. (1990). Data-Based Graphics: Visual
Display in the Decades to Come. Statistical Science,
5(3). https://doi.org/10.1214/ss/1177012101.
[15] Wang, R.-X., Wang, R., Fu, P., &Zhang, J.-M. (2020).
Portable interactive visualization of large-scale simu-
lations in geotechnical engineering using Unity3D.
Advances in Engineering Software, 148, 102838.
https://doi.org/10.1016/j.advengsoft.2020.102838.
[16] Wilson, C.A. (1960). Ore Controls of the San Xavier
Mine, MS Thesis, University of Arizona.