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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 - 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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slide1

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
  • Introduction
    • Objective
    • Literature Review
  • Methodology
    • Data base
    • Back analysis
  • Result and Discussion
  • Conclusions and Recommendation
slide3

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
slide4

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

slide5

Observed

Predicted

slide6

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]
slide7

Efficiency: (+)/(+++)

Sensitivity: /(+)

Specificity: /(+)

  • Exist many back analysis criterion.

Observed

Predicted

slide8

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]

slide9

Objective

Compare theexisting back analysis criterion.

Compare the result of deterministic method and probabilistic method.

slide10

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

slide13

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

input data
Input Data

Soil depth

slide17

Storm event

2001/7/29 ~ 2001/7/30

slide18

Input parameters

Consider Salciarini(2008) , Godt(2008)

slide19

Result and Discussion

Develop sensitivity

Efficiency

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

AUC

slide20

Criterion B :Efficiency

Low failure ratio

Overestimate parameters

Underestimate landslide

Select parameters hardly

slide21

Criterion A,C

Good constrain

Low friction angle

High cohesion

Assumption problem

(depth, variable)

slide22

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

slide24

Bayesian theorem:

Updates a probability given new information

slide26

雙變量常態分布

山崩

凝聚力

摩擦角

不山崩

Chen et al.(2005)

slide27

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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