Reactivation of Ancient Landslide Deposits: Geological Characteristics and Deformation Mechanism

Abstract

The ancient Zhenggang landslide (47.5 million m3) represents a potential threat to the construction and safe operation of the proposed Gushui Hydropower Project and to the people living downstream. The landslide was caused by continuous rainfall from October 20 to November 5, 2008, indicating that groundwater aggravated sliding and deformation, and it can be divided into two distinct zones: zone I and zone II. Investigations of the Zhenggang landslide deposits have been conducted for 10 years, but the evolution of the landslide deformation is yet to be revealed. Geological surveys and stability analysis have revealed that the Zhenggang landslide is currently relatively stable and is not sliding. The deformational data for the deep soil layers show that subslide zone I is a translational slide, while subslide zone II is an uplift slide, and zone II is slightly more stable than zone I. Obvious interior deformational failure occurred and was observed in the exploratory adits. The numerical results show that rainfall infiltration is the main factor to induce the revival of ancient landslide, and it is necessary to strengthen the landslide risk assessment and reliability of Zhenggang landslide under the condition of rainfall infiltration. Finally, in order to reduce landslide risks, the local residents living near the landslide mass should be relocated, and measures should be taken to increase slope stability.

Pencirian Geohazad Tanah Runtuh Pendam Lama di Kawasan Bukit Fraser, Pahang

“…Zon tanah runtuh tidak aktif juga memberikan manfaat sosioekonomi kepada komuniti dalam hal kesuburan pertanian (Tajul Anuar, Norasiah & Nor Shahidah 2020). Batu-batuan besar sering mengalami luluhawa menjadi batu-batu kecil, bahan serpihan dan tanah di kawasan berbukit (Wang et al 2021).…”

A quantitative enhanced assessment for ancient landslide reactivation risk considering cross-time scale joint response mechanism

“…In particular, XGBoost is a type of decision tree model that optimizes classification performance by combining multiple weakly predictive models into a high-accuracy ensemble, following the steepest gradient along a differentiable loss function (Friedman, 2001;Friedman, 2002). The XGBoost algorithm has been preferred by many researchers to fit models with well-documented speed and high predictability for the training dataset, which has already achieved superior results in classification and regression prediction in several fields (Li and Liu, 2019;Pham et al, 2021;Wang R. et al, 2021). Furthermore, some scholars have also adopted it for landslide susceptibility mapping and surface deformation monitoring (Zhao et al, 2018;Stanley et al, 2020).…”