Synthetic system for prediction of slope failures due to rainfall

被引:0
作者
Kitamura, R [1 ]
Sako, K [1 ]
机构
[1] Kagoshima Univ, Dept Ocean Civil Engn, Kagoshima 8900065, Japan
来源
COMPUTATIONAL MECHANICS, VOLS 1 AND 2, PROCEEDINGS: NEW FRONTIERS FOR THE NEW MILLENNIUM | 2001年
关键词
Numerical model for voids; Measurement of suction and rainfall; Slope stability analysis; Shirasu;
D O I
暂无
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
In Kagoshima Prefecture, which is located in the southern part of Kyushu Island, Japan, there are a lot of volcanoes such as Mt. Sakurajima, Mt. Kirishima, Mt. Kaimon etc.. Consequently most of the surface ground is covered with various volcanic products. The non-welded part of pyroclastic flow deposits is locally called Shirasu in Japanese that is classified into sandy soil and forms steep slopes. In the rainy season (June similar to September) the slope failures often occur due to heavy rainfall on such steep slopes. In this paper a synthetic system to predict such slope failures and to prevent natural disasters due to rainfall system is proposed based on the combination of field measured data in real time with those obtained by some in-situ tests, the laboratory soil tests and the numerical models.
引用
收藏
页码:381 / 386
页数:6
相关论文
共 33 条
  • [21] Rainfall thresholds for landslide early warning system in Nakhon Si Thammarat
    Kanjanakul, Chollada
    Chub-uppakarn, Tanan
    Chalermyanont, Tanit
    ARABIAN JOURNAL OF GEOSCIENCES, 2016, 9 (11)
  • [22] Coupled effects of earthquake and rainfall on landslide susceptibility in non-tropical coastal areas: assessing governing mechanisms and innovative slope protection strategy
    Prabhakar Khadka
    Oladoyin Kolawole
    Andrew C. Amenuvor
    Mawuko L. Y. Ankah
    Discover Civil Engineering, 2 (1):
  • [23] Unload-load displacement response ratio parameter and its application in prediction of debris landslide induced by rainfall
    He Keqiang
    Zhao Min
    Zhang Yongjun
    Zhang Jiaxin
    ENVIRONMENTAL EARTH SCIENCES, 2017, 76 (01)
  • [24] Developing Correlations for Advance Prediction of Slope Factor of Safety Using Linear Regression Analysis - Karachi Landslide as a Case Study
    Wang, Shuhong
    Khan, Muhammad Israr
    POLISH JOURNAL OF ENVIRONMENTAL STUDIES, 2021, 30 (06): : 5849 - 5862
  • [25] Prediction of rainfall-induced landslide using machine learning models along highway Bandipora to Gurez road, India
    Nanda, Aadil Manzoor
    Lone, Fayaz A.
    Ahmed, Pervez
    NATURAL HAZARDS, 2024, 120 (07) : 6169 - 6197
  • [26] Improving Spatial Landslide Prediction with 3D Slope Stability Analysis and Genetic Algorithm Optimization: Application to the Oltrepo Pavese
    Palazzolo, Nunziarita
    Peres, David J.
    Bordoni, Massimiliano
    Meisina, Claudia
    Creaco, Enrico
    Cancelliere, Antonino
    WATER, 2021, 13 (06)
  • [27] Performance of artificial neural network and convolutional neural network on slope failure prediction using data from the random finite element method
    Cheng-Hsi Hsiao
    Albert Y. Chen
    Louis Ge
    Fu-Hsuan Yeh
    Acta Geotechnica, 2022, 17 : 5801 - 5811
  • [28] Performance of artificial neural network and convolutional neural network on slope failure prediction using data from the random finite element method
    Hsiao, Cheng-Hsi
    Chen, Albert Y.
    Ge, Louis
    Yeh, Fu-Hsuan
    ACTA GEOTECHNICA, 2022, 17 (12) : 5801 - 5811
  • [29] Development of slope mass rating system using K-means and fuzzy c-means clustering algorithms
    Jalali Zakaria
    International Journal of Mining Science and Technology, 2016, 26 (06) : 959 - 966
  • [30] Development of slope mass rating system using K-means and fuzzy c-means clustering algorithms
    Zakaria, Jalali
    INTERNATIONAL JOURNAL OF MINING SCIENCE AND TECHNOLOGY, 2016, 26 (06) : 959 - 966