Version 1
: Received: 1 September 2024 / Approved: 2 September 2024 / Online: 3 September 2024 (11:35:27 CEST)
How to cite:
Hsu, S.-M.; Hsiung, C.-C.; Chiu, Y.-J.; Liao, Y.-F.; Lin, J.-R. Development of Potential Slip Surface Identification Model for Active Deep-seated Landslide Sites: A Case Study in Taiwan. Preprints2024, 2024090143. https://doi.org/10.20944/preprints202409.0143.v1
Hsu, S.-M.; Hsiung, C.-C.; Chiu, Y.-J.; Liao, Y.-F.; Lin, J.-R. Development of Potential Slip Surface Identification Model for Active Deep-seated Landslide Sites: A Case Study in Taiwan. Preprints 2024, 2024090143. https://doi.org/10.20944/preprints202409.0143.v1
Hsu, S.-M.; Hsiung, C.-C.; Chiu, Y.-J.; Liao, Y.-F.; Lin, J.-R. Development of Potential Slip Surface Identification Model for Active Deep-seated Landslide Sites: A Case Study in Taiwan. Preprints2024, 2024090143. https://doi.org/10.20944/preprints202409.0143.v1
APA Style
Hsu, S. M., Hsiung, C. C., Chiu, Y. J., Liao, Y. F., & Lin, J. R. (2024). Development of Potential Slip Surface Identification Model for Active Deep-seated Landslide Sites: A Case Study in Taiwan. Preprints. https://doi.org/10.20944/preprints202409.0143.v1
Chicago/Turabian Style
Hsu, S., Yi-Fan Liao and Jie-Ru Lin. 2024 "Development of Potential Slip Surface Identification Model for Active Deep-seated Landslide Sites: A Case Study in Taiwan" Preprints. https://doi.org/10.20944/preprints202409.0143.v1
Abstract
Identifying locations of landslide slip surfaces provides critical information for understanding the volume of landslides and the scale of disasters, which is essential for formulating disaster preparedness and mitigation strategies. Based on hydrogeological survey data from 24 deep-seated landslide-prone sites in Taiwan's mountainous regions, this study first developed the hydraulic conductivity potential index (HCPI) using principal component analysis to quantify hydraulic properties of disturbed rock formation with six geological factors. Then, The regression analysis was performed to construct a permeability estimation model for the geological environment of landslides. Finally, the established model was utilized to develop an identification method for potential slip depths in landslide-prone sites. Results indicated a strong relation between HCPI and hydraulic conductivity with a determination coefficient of 0.895. The relation equation confirmed that its generated data concerning the depths of significant changes in hydraulic conductivity could be used to identify potential slip surfaces. Additionally, this study successfully establishes a rule for identifying potential slip zones by summarizing data concerning the generated hydraulic conductivity profiles, stratigraphic lithology, existing inclinometer slip depth records, and groundwater level of landslide sites. This identification method was then applied to predict potential slip depths for ten landslide sites where slip surfaces have not yet occurred. These findings offer a new alternative to having early information on potential sliding depths for timely disaster management and control implementation.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
