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ROLE OF UNCERTAINTY IN SOIL HYDRAULIC PROPERTIES IN RAINFALL-INDUCED LANDSLIDES

ROLE OF UNCERTAINTY IN SOIL HYDRAULIC PROPERTIES IN RAINFALL-INDUCED LANDSLIDES. KOK-KWANG PHOON ( 方国光） NATIONAL UNIVERSITY OF SINGAPORE. 20 KM. 40 KM. 1 ha = 100 m square  1.3 soccer fields. Punggol: 155 ha. Changi Airport: 2500 ha. Tekong/ Ubin: 1500 ha. Jurong Island: 3600 ha.

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ROLE OF UNCERTAINTY IN SOIL HYDRAULIC PROPERTIES IN RAINFALL-INDUCED LANDSLIDES

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  1. ROLE OF UNCERTAINTY IN SOIL HYDRAULIC PROPERTIES IN RAINFALL-INDUCED LANDSLIDES KOK-KWANG PHOON (方国光） NATIONAL UNIVERSITY OF SINGAPORE

  2. 20 KM 40 KM

  3. 1 ha = 100 m square  1.3 soccer fields Punggol: 155 ha Changi Airport: 2500 ha Tekong/ Ubin: 1500 ha Jurong Island: 3600 ha Marina Bay: 40 ha Tuas: 640 ha Pasir Panjang Port Semakau: 350 ha Sentosa: 10 ha Southern Islands: 35 ha

  4. ESPLANADE AT MARINA BAY, SINGAPORE

  5. ACKNOWLEDGMENTS • MS ANASTASIA SANTOSO • DR MUTHUSAMY KARTHIKEYAN • PROF DAVID TOLL • PROF SER-TONG QUEK

  6. SCOPE OF PRESENTATION • RAINFALL-INDUCED LANDSLIDES • UNSATURATED FLOW & STABILITY • PROBABILITY MODEL FOR SWCC • PROBABILITY MODEL FOR kS • SOME APPLICATIONS • CONCLUSIONS

  7. LANDSLIDE, SANTA TECLA, EL SALVADOR Photo by La Prensa Grafica, AP,  2001

  8. LANDSLIDE INDUCED BY TYPHOON MORAKOT, 2009

  9. Phoon, KK, Toll, DG & Karthikeyan, M, “Study on the Effects and Impacts Of Climate Change on Singapore – Slope Stability”, Final Report for National Environment Agency (NEA), Singapore, April 2009 RAINFALL-INDUCED LANDSLIDES

  10. - Pore water pressure + Depth UNSATURATED SLOPE • NEGATIVE PORE-WATER PRESSURES = MATRIC SUCTION • INCREASE STABILITY

  11. Rainfall - + RAINFALL • PORE-WATER PRESSURES BECOME LESS NEGATIVE • SATURATION NEAR SURFACE  SHALLOW FAILURE Pore water pressure Depth

  12. FIELD MEASUREMENTS SEEP/W IS HIGHLY SENSITIVE TO SOIL HYDRAULIC PROPERTIES – ABLE TO CAPTURE QUALITATIVE TREND ONLLY

  13. FRAMEWORK FOR LANDSLIDE HAZARD/RISK DATA DRIVEN PHYSICS DRIVEN

  14. Bt Batok West Ave 3 TPE (PIE) Expressway Slope failure at NUS after 166mm of rain 11 January 2006 Slope failure at NTU after 95mm of rain 26 February 1995 SINGAPORE LANDSLIDE DATABASE • NATIONAL DATABASE HAS BEEN ESTABLISHED • MINOR, SHALLOW LANDSLIDES ARE COMMON Mount Faber Park NO. OF LANDSLIDE EVENTS IN DATABASE: 489 SLE(BKE) slip road

  15. LANDSLIDE & RAINFALL BASED ON OBSERVED LANDSLIDE DATA ALL LANDSLIDES (GROUP 1) TOOK PLACE DURING NORTH-EAST MONSOON SEASON (NOVEMBER TO MARCH) BASED ON OBSERVED RAINFALL DATA

  16. RAINFALL TRIGGER FOR LANDSLIDES OBSERVED LANDSLIDES OBSERVED RAINFALL 100 MM OVER A 6-DAY PERIOD MEDIAN RAINFALL TRIGGER FOR SINGAPORE MEDIAN TRIGGER OF 100 MM EXCEEDED 46% OF THE TIME WITHIN NE MONSOON (DAILY RAINFALL RECORD FOR PAST 47 YEARS 1960 to 2006

  17. UNSATURATED FLOW & STABILITY

  18. STEADY STATE SEEPAGE SATURATED PERMEABILITY ks HYDRAULIC CONDUCTIVITY GARDNER MODEL ANALYTICAL SOLUTION OF h (z)

  19. STEADY STATE SUCTION PROFILE SAME INFILTRATION FLUX q a=0.1 a=0.7 a=0.7 a=0.1

  20. TRANSIENT SEEPAGE CONDUCTIVITY SWCC SOLVED WITH FINITE ELEMENT METHOD – FIND h (z,t)

  21. SOIL SHEAR STRENGTH SATURATED SOIL UNSATURATED SOIL σn σn c + (σn-ua) tan c + (σn-uw) tan (ua - uw) χtan  uw -uw SATURATED SHEAR STRENGTH: UNSATURATED SHEAR STRENGTH: c = EFFECTIVECOHESİON f = FRİCTİON ANGLE VOLUMETRIC WATER CONTENT

  22. z SLOPE SURFACE FAILURE SURFACE L MATRIC SUCTION CONTRIBUTION SOIL ROCK INTERFACE  INFINITE SLOPE

  23. SATURATED INFINITE SLOPE INITIAL CONDITION: MINIMUM FS AT BASE DURING RAINFALL: TOP LAYERS -- SATURATED MINIMUM FS AT SATURATED ZONE SHALLOW FAILURE FAILURE: FSmin < 1 FS=1

  24. Phoon, KK, Santoso, AM & Quek, ST, “Probabilistic analysis of soil water characteristic curves”, Journal of Geotechnical and Geoenvironmental Engineering, ASCE, 136(3), March 2010, 445-455.    PROBABILITY MODEL FOR SOIL-WATER CHARACTERISTIC CURVE (SWCC)

  25. VARIABILITY OF SWCC MEASUREMENT DATA SOURCE: UNSODA DATA FROM THE SAME SOIL TYPE SHOWS VARIABILITY

  26. PROBABILITY MODEL OF SWCC • REDUCE MEASURED DATA INTO FEW PARAMETERS VIA CURVE-FITTING • NORMALIZE FITTED EQUATIONS WITH THE SATURATED WATER CONTENT TO REDUCE DATA SCATTER • MODEL CURVE-FITTING PARAMETERS AS A RANDOM VECTOR (POSSIBLY CORRELATED) TO HANDLE REMAINING SCATTER

  27. CURVE-FIT PARAMETERS VAN-GENUCHTEN MODEL a > 0, n > 1 s

  28. s Soil 2372 a=0.653 n=1.501 Soil 1104 a=1.960, n=1.085 Soil 1104 Soil 2372 CURVE FITTING s

  29. STATISTICS OF SWCC PARAMETERS an = - 0.268 SOIL TYPE: SANDY CLAY LOAM SAMPLE SIZE N=38 DISTRIBUTION FIT: SHIFTED LOGNORMAL

  30. SIMULATION LOGNORMAL RANDOM VECTOR a = exp(l1 + x1X1) n = exp(l2 + x2X2) + 1 CORRELATED LOGNORMALS STANDARD NORMALS, CORRELATED STANDARD NORMALS, UNCORRELATED ρx1x2 ≠ρ(a,n) -- CLOSED FORM RELATION

  31. CORRELATION COEFFICIENT LOGNORMALS an = - 0.268 NORMALS X1X2 = - 0.415

  32. SIMULATED SWCC PARAMETERS CORRELATION IS IMPORTANT an = - 0.268 an = 0

  33. SIMULATED SWCC

  34. CLAYEY SOIL METHODOLOGY IS APPLICABLE TO OTHER SOIL TYPES

  35. Phoon, KK, Santoso, AM & Quek, ST, “Probability Models for SWCC and Hydraulic Conductivity”, ISSMGE 14th Asian Regional Conference, 23-27 May 2011, Hong Kong   PROBABILITY MODEL FOR HYDRAULIC CONDUCTIVITY

  36. HYDRAULIC CONDUCTIVITY FUNCTION ks GARDNER MODEL PARAMETERS: a, kS VARIABILITY OF a – USE PROB. MODEL OF SWCC

  37. VARIABILITY OF kS • INSUFFICIENT DATA OF ks • USE DATA OF SAT. WATER CONTENT qs SANDY CLAY LOAM LOGNORMAL DISTRIBUTION

  38. VARIABILITY OF kS • SIMULATE REALIZATIONS OF qs FROM THE LOGNORMAL MODEL • FOR EACH REALIZATION, CALCULATE ks USING KOZENY-CARMAN EQUATION A CONSTANT, FUNCTION OF GRAIN SIZE

  39. VARIABILITY OF ks VARIABILITY OF a VARIABILITY OF kS SANDY CLAY LOAM qs MEAN = 0.395, C.O.V. = 0.13 LOGNORMAL VARIABLE ks MEAN = 1.1 x 10-6 m/s, C.O.V. = 0.67 ALSO LOGNORMAL

  40. SPATIAL VARIABILITY • ks VARIES FROM ONE POINT TO ANOTHER • 1D VARIATION(ALONG Z) • AT EACH POINT, KS (z) HAS ITS OWN DISTRIBUTION / HISTOGRAM • ONE SOIL TYPE – SAME DISTRIBUTION AT ANY POINTS

  41. 1D RANDOM FIELD • 1D LOGNORMAL STATIONARY RANDOM FIELD (ks, sks, D) • EXPONENTIAL CORRELATION FUNCTION • CORRELATION LENGTH d • NORMALIZED CORR. LENGTH, D = d / L

  42. d d = 0.6 m d = 6 m CORRELATION LENGTH exp(-2)

  43. SUMMARY OF PROBABILITY MODELS

  44. Santoso, A.M., Phoon K.K. and Quek, S.T., Effects of spatial variability on rainfall-induced landslides, Sixth MIT Conference on Computational Fluid and Solid Mechanics, June 15-17, 2011, Massachusetts, USA. SOME APPLICATIONS

  45. EXAMPLE OF INFINITE SLOPE INITIAL CONDITION: HYDROSTATIC BOUNDARY CONDITION: h = 0 m (GWT AT BASE) CONSTANT FLUX AT SURFACE q = - 0.5 mks Elevation RAINFALL CLAYEY SOIL L=6 m Pressure head β= 30

  46. DETERMINISTIC RESULTS q = - 0.5 ks HOMOGENEOUS PROFILE: NO SHALLOW FAILURE EXCEPT FOR q / ks≈ 1

  47. PROBABILISTIC ANALYSIS CHAR. UNCERTAINTY – PROBABILITY MODEL SIMULATION OF RANDOM SAMPLES DO SEEPAGE & STABILITY ANALYSIS FOR EACH SAMPLE OUTPUT: STATISTICS OF RESPONSE (PRESSURE HEAD, FS), PROBABILITY OF FAILURE

  48. PROBABILISTIC ANALYSIS DO SEEPAGE & STABILITY ANALYSIS FOR EACH SAMPLE AT A GIVEN ELAPSED TIME

  49. (+) PORE PRESSURE d PRESSURE HEAD 8 DAYS 12 DAYS 20 DAYS RANDOM FIELD WITH SHORT CORR. LENGTH  (+) PORE PRESSURE

  50. d FACTOR OF SAFETY 8 DAYS 12 DAYS 20 DAYS RANDOM FIELD WITH SHORT CORR. LENGTH OF KS CAN CAPTURE SHALLOW FAILURES

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