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

1. 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

2. Outline • Introduction • Objective • Literature Review • Methodology • Data base • Back analysis • Result and Discussion • Conclusions and Recommendation

3. 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

4. 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

5. Observed Predicted

6. 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]

7. Efficiency: (+)/(+++) Sensitivity: /(+) Specificity: /(+) • Exist many back analysis criterion. Observed Predicted

8. 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]

9. Objective Compare theexisting back analysis criterion. Compare the result of deterministic method and probabilistic method.

10. 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.

11. 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.

12. Back analysis parameters 林衍丞，2009

13. 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

14. Study Area

15. Input Data Soil depth

16. Input Data Slope

17. Storm event 2001/7/29 ~ 2001/7/30

18. Input parameters Consider Salciarini(2008) , Godt(2008)

19. Result and Discussion Develop sensitivity Efficiency Efficiency greater than 80%, Sensitivity greater than 60% and Specificity greater than 90% AUC

20. Criterion B :Efficiency Low failure ratio Overestimate parameters Underestimate landslide Select parameters hardly

21. Criterion A,C Good constrain Low friction angle High cohesion Assumption problem (depth, variable)

22. 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

24. Bayesian theorem: Updates a probability given new information

26. 雙變量常態分布 山崩 凝聚力 摩擦角 不山崩 Chen et al.(2005)

27. 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

29. thanks for your attention