降雨誘發淺層山崩模型土壤強度參數逆分析之比較與驗證
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降雨誘發淺層山崩模型土壤強度參數逆分析之比較與驗證. Adviser: 董家鈞、劉家男 Student: 陳麒任. Outline. Introduction Objective Literature Review Methodology Data base Back analysis Result and Discussion Conclusions and Recommendation. Classification of landslide assessment:. Qualitative analysis Empirical method

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降雨誘發淺層山崩模型土壤強度參數逆分析之比較與驗證降雨誘發淺層山崩模型土壤強度參數逆分析之比較與驗證

Adviser:董家鈞、劉家男

Student:陳麒任


Outline
Outline降雨誘發淺層山崩模型土壤強度參數逆分析之比較與驗證

  • Introduction

    • Objective

    • Literature Review

  • Methodology

    • Data base

    • Back analysis

  • Result and Discussion

  • Conclusions and Recommendation


Classification of landslide assessment:降雨誘發淺層山崩模型土壤強度參數逆分析之比較與驗證

  • Qualitative analysis

    • Empirical method

  • Quantitative analysis

    • Statistic method

      • Discriminant analysis

      • Logistic regression

      • Conditional Probability Approach

    • Artificial intelligence

      • Fuzzy Theory

      • neural network

    • Deterministic analysis

      • Rainfall trigger landslide

      • Earthquake trigger landslide


Back analysis降雨誘發淺層山崩模型土壤強度參數逆分析之比較與驗證

Cohesion

Lab test

Lab test

Friction angle

hydraulic conductivity

Deterministic analysis

hydraulic conductivity

Soil depth

Soil depth

DEM

DEM

unit weight of soil

unit weight of soil

Slope

Slope

Deterministic analysis

Predicted landslide inventory

Parameters

In situ test or Empirical methods

In situ test or Empirical methods

Rainfall intensity

Rainfall intensity

Godt et al. (2008)

Remote sensing

Remote sensing

Observed landslide inventory


Observed降雨誘發淺層山崩模型土壤強度參數逆分析之比較與驗證

Predicted


  • Literature Review降雨誘發淺層山崩模型土壤強度參數逆分析之比較與驗證

  • Extensive work to get reliable data. [林衍丞,2009]

  • Strength parameter and hydraulic parameter are difficult to obtain. [李錫堤,2009]

  • There are scale issues involved in the translation of laboratory values to the field problem. [Guimaraes, 2003]

  • Back analysis of strength has advantages over lab testing in that the scale is much larger. [Gilbert ,1998]

  • Back analysis is reliable only when the model and all assumptions are reasonable and accurate representations of the real system[Deschamps, 2006]


Efficiency: (降雨誘發淺層山崩模型土壤強度參數逆分析之比較與驗證+)/(+++)

Sensitivity: /(+)

Specificity: /(+)

  • Exist many back analysis criterion.

Observed

Predicted


However, the output of back analysis is usually uncertain because of the random factors existing in the problem. [Zheng, 2008]

Methodologies used for back analysis can be classified into two groups, i.e., deterministic method and probabilistic method.[Zhang, 2010]


Objective because of the random factors existing in the problem. [

Compare theexisting back analysis criterion.

Compare the result of deterministic method and probabilistic method.


Methodology because of the random factors existing in the problem. [

Rainfall-induced landslide model

  • This research use TRIGRS, a Fortran program developed by USGS.

  • The Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability.


Theoretical basis
Theoretical Basis because of the random factors existing in the problem. [

  • Infinite-slope stability

    • Landslide with planar failure surfaces.

    • Slide depth is much smaller than length and width.

      where c’ is soil cohesion for effective stress, Φ’ is the soil friction angle for effective stress, γw is unit weight of groundwater, and γs is soil unit weight, β is slope angle, ψ is pressure head.


Back analysis parameters
Back analysis parameters because of the random factors existing in the problem. [

林衍丞,2009


ROC because of the random factors existing in the problem. [

Collect the back analysis criterion

Maximum Efficiency(林衍丞,2009) 。

Maximum AUC (林衍丞,2009) 。

Efficiency greater than 80%, Sensitivity greater than 60% and Specificity greater than 90%(中興工程顧問社,2004)。

FS=1

FS=1.5

FS=0.5

Sensitivity

林衍承(2009)

Maximum Develop Sensitivity

Specificity


Study area
Study Area because of the random factors existing in the problem. [


Input data
Input Data because of the random factors existing in the problem. [

Soil depth


Input Data because of the random factors existing in the problem. [

Slope


Storm event because of the random factors existing in the problem. [

2001/7/29 ~ 2001/7/30


Input parameters because of the random factors existing in the problem. [

Consider Salciarini(2008) , Godt(2008)


Result and Discussion because of the random factors existing in the problem. [

Develop sensitivity

Efficiency

Efficiency greater than 80%, Sensitivity greater than 60% and Specificity greater than 90%

AUC


Criterion B :Efficiency because of the random factors existing in the problem. [

Low failure ratio

Overestimate parameters

Underestimate landslide

Select parameters hardly


Criterion A,C because of the random factors existing in the problem. [

Good constrain

Low friction angle

High cohesion

Assumption problem

(depth, variable)


Tiwari because of the random factors existing in the problem. [ (2000,2005) assumed factor of safety is equal to 0.98 for

back analysis cohesion and friction angle.

Sensitivity= 0.4~0.44

Specificity=0.80~0.88

Efficiency=0.75~0.85


Bayesian theorem: because of the random factors existing in the problem. [

Updates a probability given new information


雙變量常態分布 because of the random factors existing in the problem. [

山崩

凝聚力

摩擦角

不山崩

Chen et al.(2005)


Zhang et al.(2010):Back analysis of slope failure with Markov chain Monte Carlo simulation

Gilbert et al.(1998):Uncertainty in back analysis of slopes: Kettlemen Hills case history

P

P

多變量常態分布

0.42

0.84

0.8

Fs

Sensitivity

Specificity

Efficiency

1


thanks for your attention Markov chain Monte Carlo simulation


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