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 PowerPoint PPT Presentation


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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

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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

Outline

  • Introduction

    • Objective

    • Literature Review

  • Methodology

    • Data base

    • Back analysis

  • Result and Discussion

  • Conclusions and Recommendation


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Classification of landslide assessment:

  • 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


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Back analysis

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


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Observed

Predicted


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  • Literature Review

  • 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]


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Efficiency: (+)/(+++)

Sensitivity: /(+)

Specificity: /(+)

  • Exist many back analysis criterion.

Observed

Predicted


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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]


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Objective

Compare theexisting back analysis criterion.

Compare the result of deterministic method and probabilistic method.


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Methodology

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

  • 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

林衍丞,2009


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ROC

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


Input data

Input Data

Soil depth


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Input Data

Slope


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Storm event

2001/7/29 ~ 2001/7/30


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Input parameters

Consider Salciarini(2008) , Godt(2008)


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Result and Discussion

Develop sensitivity

Efficiency

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

AUC


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Criterion B :Efficiency

Low failure ratio

Overestimate parameters

Underestimate landslide

Select parameters hardly


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Criterion A,C

Good constrain

Low friction angle

High cohesion

Assumption problem

(depth, variable)


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Tiwari (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


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Bayesian theorem:

Updates a probability given new information


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雙變量常態分布

山崩

凝聚力

摩擦角

不山崩

Chen et al.(2005)


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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


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thanks for your attention


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