9oct 1 esposito-landslide risk reduction

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9oct 1 esposito-landslide risk reduction

  1. 1. LANDSLIDE RISK REDUCTION BY COUPLING MONITORING AND NUMERICAL MODELING BOZZANO F.1, CIPRIANI I.1, ESPOSITO C.1, MARTINO S.1, MAZZANTI P.1, 2, ,PRESTININZI A.1, ROCCA A.1 & SCARASCIA MUGNOZZA G.1 Dipartimento di Scienze della Terra e Centro di Ricerca CERI– Sapienza Università di Roma, P.le A. Moro 5 00185, Rome, Italy 2 NHAZCA S.r.l., spin-off “Sapienza” Università di Roma, Via Cori snc, 00177, Rome, Italy 1 Landslide risk reduction by coupling monitoring and numerical modelling
  2. 2. Outline • The case history; short summary of slope-infrastructure interaction • Description of the methodological approach • Geological and geomorphological background • Field activities and preliminary geological model • Monitoring activities: criteria of the monitoring platform design; highlights on the main results • Construction of the geomechanical model and numerical back-analysis • Some considerations Landslide risk reduction by coupling monitoring and numerical modelling
  3. 3. The case history Frame: modernization of a major motorway in southern Italy Study slope: construction of a new tunnel Landslide risk reduction by coupling monitoring and numerical modelling
  4. 4. The first landslide March 2007 February 2007 Landslide risk reduction by coupling monitoring and numerical modelling
  5. 5. Following this event, the Research Centre for Geological Risks CERI of the University of Rome “Sapienza” carried out detailed engineering-geological investigation and surveys (field geomorphological, geological and geomechanical surveys, boreholes, seismic surveys and laboratory tests of samples) on the slope in order to define a reference model to explain the occurrence of the landslide and to plan the remediation works Landslide risk reduction by coupling monitoring and numerical modelling
  6. 6. Methodological approach Geology – Structure – Gemorphology (site surveys; field investigations) PRELIMINARY GEOLOGICAL MODEL OF THE SLOPE/LANDSLIDE ue ngr Co hec yc nc k Geomechanical data MONITORING DATA (in depth and surficial; geotechnical, topographic, meteorological) EMERGENCY PHASES MANAGEMENT Strain (and stress) history of the slope Calib rat rheol ion of param ogical eters analy (backsis) Back-analysis Testing suitability of semi-empirical models for time of failure forecasting Landslide risk reduction by coupling monitoring and numerical modelling REFERENCE GEOMECHANICAL MODEL NUMERICAL BACKANALYSIS OF SLOPEINFRASTRUCTURE INTERACTION INTEGRATED TOOL FOR DISPLACEMENT FORECASTING
  7. 7. Geological and geomorphological background High Quaternary uplift rates: marine terraces and steep slopes Metamorphic bedrock covered by marine/continental deposits Landslide risk reduction by coupling monitoring and numerical modelling
  8. 8. Geological model of the slope and kinematic model of the landslide • Site surveys (geological- Marine terrace deposit Sands Landslide-involved gneiss • Gneiss • March 2007 event: partial re-activation of an existing complex, deep-seated, roto-translational landslide Landslide risk reduction by coupling monitoring and numerical modelling structural and geomorphologic); Stratigraphic logs from boreholes; Geophysical site investigations
  9. 9. Monitoring activities Before and during the construction of stabilization countermeasures 1. Inclinometers 2. Piezometers 3. Terrestrial InSAR 4. Total station 5. Load cells on man-made reinforcements Landslide risk reduction by coupling monitoring and numerical modelling After the construction of stabilization countermeasures During the restart of tunnel excavation
  10. 10. Layout of monitoring instrumentation Landslide risk reduction by coupling monitoring and numerical modelling
  11. 11. In-depth monitoring: inclinometers and piezometers SAbis SA Sc Inclinometer Piezometer Landslide risk reduction by coupling monitoring and numerical modelling
  12. 12. Surface monitoring: TInSAR Landslide risk reduction by coupling monitoring and numerical modelling
  13. 13. Cognitive monitoring: slope movements before and during the construction of stabilization countermeasures (slope reprofiling and retaining structures) SAbis SA SB Inclinometer Piezometer Landslide risk reduction by coupling monitoring and numerical modelling
  14. 14. Cognitive monitoring: slope movements before and during the construction of stabilization countermeasures (slope reprofiling and retaining structures) Landslide risk reduction by coupling monitoring and numerical modelling t ne m c a psi D So L e l ) mm ( 10 November 2007 – 29 February 2008
  15. 15. Control monitoring: displacements of the first remedial works - gabions Landslide risk reduction by coupling monitoring and numerical modelling
  16. 16. Control monitoring: displacements of the first remedial works - gabions Landslide risk reduction by coupling monitoring and numerical modelling
  17. 17. Control monitoring: displacements of the first remedial works - bulkheads Landslide risk reduction by coupling monitoring and numerical modelling
  18. 18. Control monitoring: displacements of the first remedial works bulkheads Landslide risk reduction by coupling monitoring and numerical modelling
  19. 19. Control monitoring: displacements related the restarting of tunnel excavation Bulkheads Landslide risk reduction by coupling monitoring and numerical modelling
  20. 20. Excavation stopped Preparatory works Tunneling excavation progress Lack of TInSAR data Landslide risk reduction by coupling monitoring and numerical modelling
  21. 21. Control monitoring: displacements related to the restarting of tunnel excavation Before the beginning of the tunnel excavation the anchored bulkheads showed an almost constant velocity of displacement on the order of 0.05 mm/h. Immediately after the beginning of the excavation the velocity of bulkheads suddenly increased reaching maximum values of 0.75 mm/h with acceleration and deceleration peaks on the order of 0.02 mm/h2. During the three excavation phases a maximum displacement of about 100 mm was recorded on the first order of bulkhead. In the last two phases, the interferometric monitoring allowed us to clearly recognize a typical creep behaviour. Landslide risk reduction by coupling monitoring and numerical modelling
  22. 22. Control monitoring: displacements related to the restarting of tunnel excavation Velocity of displacement: about 1 mm/hour. Activation of a protocol to immediately stop tunneling Landslide risk reduction by coupling monitoring and numerical modelling
  23. 23. Some hints from the large collected dataset • • • During more than 40 months monitoring, several shallow landslides were detected by TInSAR images of the slope. In particular, ten events were identified by TInSAR data and then confirmed by optical photos The large dataset of events occurred on the same slope (which means similar conditions and features of the landslides) and the detailed displacement data available represented an occasion to test the efficacy of semi-empirical approaches based on time series of displacement or derived quantities (i.e velocity, acceleration etc). The displacement behaviour of the 10 shallow landslides, and especially in their pre-failure stage, were analysed in detail in order to infer information about the total amount of displacement, the duration of the entire process, the velocity, the acceleration, etc. Landslide risk reduction by coupling monitoring and numerical modelling
  24. 24. Some hints from the large collected dataset: testing the suitability of semi-empirical models for time of failure prediction Creep behavior, except for a slight deceleration immediateley before failure Landslide risk reduction by coupling monitoring and numerical modelling
  25. 25. Test # 1: Fukozono (1985) linear model and its modifications applied to shallow landslides • • For each landslide, the predicted time of failure was computed iteratively since the beginning of the displacement phase (looking at the tertiary creep phase) by increasing the number of displacement data step by step. Hence, the real prediction of the time of failure based on the newly collected data over time was simulated. A new approach named ADF (Average Data Fukuzono) was developed. ADF is based on the average and moving average velocity computed from temporal consecutive data. In the first case, data were averaged iteratively, starting from the first data collected. In the case of the moving average, the data were averaged by using the half of the dataset moved iteratively by one single step until the last half before the failure. Landslide risk reduction by coupling monitoring and numerical modelling
  26. 26. Test # 2: non-linear approach for anchored bulkheads. Landslide risk reduction by coupling monitoring and numerical modelling
  27. 27. From the geological model to the geomechanical model Landslide risk reduction by coupling monitoring and numerical modelling
  28. 28. Geomechanical surveys Correlation Jv - Ib 25 20 Ib (cm) = -6,09 ln(Jv) + 30,06 R2 = 0,92 15 ) m c ( b I Ib (cm) = -5,76 ln(Jv) + 27,48 R2 = 0,90 10 Ib (cm)= -5,43 ln(Jv) + 24,90 R2 = 0,87 5 Data from 144 survey sites 0 0 5 10 15 20 Jv (n° discontinuità/m3) Landslide risk reduction by coupling monitoring and numerical modelling 25 30 35
  29. 29. AN ALTERNATIVE APPROACH FOR ROCK MASS CLASSIFICATION Based on Jv and Ib values 1) z_score transformation 4 D30 D26 3 D4 D29 D14 D44 2 D42 Z40 Z3 GM6 S17 Z2 1 I _ z b D16 0 D19 ST2 Z6 ST1 D11GM8 D35 D33 ST6 Z28 ST25 D1 S3 Z1 ST3 GM1 S21 D3 D13 0,44 Z30 Z29 D8 D43 S19 S4 S14 S18 S9 D6 S27 D41 D9D21 S20 ST4 D2 ST9 STZ2 GM15 Z9 ST10 Z33 D37 Z8 GM7 ST8 D38 Z38 S8 ST5 D32 S5 Z5 Z4S2 D45 GM4 -1 Stimato 0,61 D27 D46 D36 S26 S15 Z36 S10 Z24 D15 Z39 S11 S29 Z27 GM5 D7 GM10 GM14 D34 0,06 -0,68 D40 GM9 Empirico 1,24 D28 D25 D23 1,88 1,77 GM11 0,03 D5 GM13 Z7 S22 D10 ST7 Z23 Z25 STZ1 S24Z20 S28 S7 GM3 GM12 D22 D12 D18 -0,50 GM16 -0,75 Z31 S25 Z22S23 S1 S13 S6 D39 D24 S12 Z21 Z35 Z37 S16 D20 -2 Z32 Z34 -1,66 GM2 D17 D31 -3 -3 -2 -1 0 1 z_Jv Landslide risk reduction by coupling monitoring and numerical modelling 2 3 4
  30. 30. AN ALTERNATIVE APPROACH FOR ROCK MASS CLASSIFICATION 2) hierarchical clustering (identification of rock mass classes) 4 GM3 GM7 3 D18 GM8 D4 1 D44 2 Z39 Optimal number of clusters (15) based on the index by Calinski – Harabasz (1974) I _ z b S15 0 -1 11 12 D31 S3 S9 D37 D2 1 10 ST10 ST2 D39 GM5 GM4 GM10 ST5 13 14 S6 Z16 D43 S16 Z40 S2 D5 ST1 D10 Z5 D3 S1 ST8 S4 D29 D41 S14 S20 Z37 S29 D9 ST4 D40 S28 S12 D12Z29 S5 D36 Z36 D46 GM16 Z38 D1 S18 D11 Z2 GM2 Z33 D22 Z20 D6 D23 D15 Z15 Z7 S11 Z8 Z4Z31 D30 D16 Z18 Z1D42 D38 STZ1 GM14 GM1 Z22 S17 S24 S19 Z12 D13 S13 S22 D35 S8 Z17Z27 S7 Z6 Z28 S10 Z26 D32 S26 Z30D20 D34 Z24 Z23 Z9 S23 S21D26 D28 D24 D7 Z35Z3 D25 STZ2 Z13 Z21 ST3 D21 D19 S25 ST7 D8 ST6 Z25 D17 GM15 D33 D45 15 2 3 4 5 ST9 6 7 GM9 Z10 D14 D27 8 Z14 Z34 Z11 Z32 GM11 Z19 GM12 -2 9 GM6 S27 GM13 ST25 -3 -3 -2 -1 0 1 z_Jv Landslide risk reduction by coupling monitoring and numerical modelling 2 3 4
  31. 31. AN ALTERNATIVE APPROACH FOR ROCK MASS CLASSIFICATION 3) Factorial analysis (quantification of the proposed rock mass index) ISD = (0,911 * z_Jv) – (0,911 * z_Ib) CLUSTER Jv Ib (cm) ISD N° osservazioni 7 35,7 6,3 3,75 5 0,35 9 Q 6 31,0 7,3 2,83 7 0,31 11 P 2 27,5 9,0 1,90 11 0,26 14 O 9 21,0 6,9 1,60 5 0,52 33 N 5 22,8 9,6 1,12 13 0,18 16 M 11 16,2 8,1 0,64 7 0,32 50 L 13 22,9 12,5 0,34 6 0,31 91 I 1 17,7 11,1 0,00 33 0,39 8337 H 12 12,2 9,9 -0,38 4 0,32 -84 G 14 23,0 17,5 -1,06 2 0,13 -12 F 3 13,0 12,9 -1,11 29 0,28 -25 E 10 17,3 15,4 -1,22 4 0,25 -20 D 4 8,5 15,0 -2,29 11 0,48 -21 C 8 13,4 19,1 -2,77 5 0,38 -14 B 15 9,5 23,4 -4,46 2 0,40 -9 A Landslide risk reduction by coupling monitoring and numerical modelling Deviazione standard ISD Coeff. Variaz. ISD CLASSE di AMMASSO
  32. 32. Parametrization of rock mass classes by equivalent continuum approach 1) Hoek & Brown criterion for strength Inviluppo a rottura di Hoek & Brown - CLASSE A Percorso tensionale a rottura (Kf line) - CLASSE A 30 14 25 8 ) a P M ( q 10 15 σ1( ) a P M 20 10 Kf line 6 4 5 2 0 -0,5 q = 0,86p + 0,27 R² = 0,99 12 0 0,0 0,5 1,0 1,5 2,0 0 2,5 5 10 σ 3 (MPa) Classe Q P O N M L I H G F E D C B A σci (MPa) 32,7 34,5 36,5 37,2 38,2 39,4 40,1 40,9 41,8 43,6 43,7 44,0 46,9 48,3 53,4 15 20 p (MPa) mi D RQD (% ) BRMR GSI mb s a Jv 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 0,95 0,85 0,75 0,70 0,65 0,60 0,55 0,50 0,45 0,40 0,40 0,40 0,25 0,20 0,05 0,0 12,7 24,3 45,7 39,8 61,5 39,4 56,6 74,7 39,1 72,1 57,9 87,0 70,8 83,7 35 37 40 43 44 49 44 49 52 46 52 51 56 55 58 30 32 35 38 39 44 39 44 47 41 47 46 51 50 53 0,2821 0,4833 0,8043 1,0941 1,3086 1,8953 1,6349 2,2930 2,8694 2,3693 3,0970 2,9618 4,4661 4,5375 5,8997 1,14E-05 2,64E-05 6,58E-05 1,25E-04 1,75E-04 4,19E-04 2,49E-04 5,72E-04 9,80E-04 5,19E-04 1,12E-03 9,85E-04 2,63E-03 2,60E-03 4,94E-03 0,52234 0,51953 0,51595 0,51302 0,51217 0,50866 0,51217 0,50866 0,50705 0,51062 0,50705 0,50755 0,50535 0,50573 0,50466 35,7 31,0 27,5 21,0 22,8 16,2 22,9 17,7 12,2 23,0 13,0 17,3 8,5 13,4 9,5 Landslide risk reduction by coupling monitoring and numerical modelling φ (°) 28 34 39 42 45 48 47 50 52 51 54 53 56 57 59 c (MPa) 0,16 0,19 0,20 0,22 0,24 0,24 0,27 0,31 0,31 0,32 0,34 0,35 0,41 0,42 0,53 σt (MPa) -0,001 -0,002 -0,003 -0,004 -0,005 -0,009 -0,006 -0,010 -0,014 -0,010 -0,016 -0,015 -0,028 -0,028 -0,045
  33. 33. Parametrization of rock mass classes by equivalent continuum approach 2) Sridevi & Sitharam method for deformability Ej(σ 3=0) = exp(-1,15*(10E-2)*Jf) * Ei(σ 3=0) CLASSE di AMMASSO Jv Ib (cm) ISD "r" "n" Jf Q 35,7 6,3 3,75 5 32,7 0,75 0,4 119 51 13 P 31,0 7,3 2,83 7 34,5 0,76 0,4 102 51 16 O 27,5 9,0 1,90 11 36,5 0,76 0,4 90 51 18 N 21,0 6,9 1,60 5 37,2 0,77 0,4 68 51 23 M 22,8 9,6 1,12 13 38,2 0,77 0,5 59 51 26 L 16,2 8,1 0,64 7 39,4 0,78 0,5 42 51 32 I 22,9 12,5 0,34 6 40,1 0,78 0,5 59 51 26 H 17,7 11,1 0,00 33 40,9 0,78 0,5 45 51 30 G 12,2 9,9 -0,38 4 41,8 0,79 0,6 26 51 38 F 23,0 17,5 -1,06 2 43,6 0,79 0,6 49 51 29 E 13,0 12,9 -1,11 29 43,7 0,79 0,6 27 51 37 D 17,3 15,4 -1,22 4 44,0 0,80 0,7 31 51 36 C 8,5 15,0 -2,29 11 46,9 0,80 0,7 15 51 43 B 13,4 19,1 -2,77 5 48,3 0,82 0,7 23 51 39 A 9,5 23,4 -4,46 2 53,4 0,84 0,7 16 51 42 Landslide risk reduction by coupling monitoring and numerical modelling N° osservazioni Qc (MPa) Ei (σ3 = 0) (GPa) Ej (σ3 = 0) (GPa)
  34. 34. Parametrization of rock mass classes by equivalent continuum approach 2) Sridevi & Sitharam method for deformability Vertical zoning CLASSE di AMMASSO ISD Jf σ3 (MPa) σci (MPa) (σ3 =5) σcj (MPa) (σ3 =5) Ej (σ3 = 0) (GPa) Ej (σ3 = 5) (GPa) Q 3,75 119 5 96,8 37,4 13 15 P 2,83 102 5 96,8 42,8 16 18 O 1,90 90 5 96,8 46,9 18 21 N 1,60 68 5 96,8 56,1 23 27 M 1,12 59 5 96,8 60,3 26 30 L 0,64 42 5 96,8 69,4 32 37 I 0,34 59 5 96,8 60,5 26 30 H 0,00 45 5 96,8 67,3 30 35 G -0,38 26 5 96,8 78,8 38 44 F -1,06 49 5 96,8 65,7 29 34 E -1,11 27 5 96,8 77,7 37 43 D -1,22 31 5 96,8 75,6 36 42 C -2,29 15 5 96,8 85,7 43 50 B -2,77 23 5 96,8 80,3 39 46 A -4,46 16 5 96,8 85,1 42 49 Landslide risk reduction by coupling monitoring and numerical modelling CLASSE di AMMASSO ISD Ej (σ3 = 5) (GPa) Q 3,75 15 ν 0,25 P 2,83 18 0,25 O 1,90 21 N 1,60 27 M 1,12 L Gj (σ3 = 5) (GPa) Kj (σ3 = 5) (GPa) 6 10 7 12 0,25 8 14 0,25 11 18 30 0,25 12 20 0,64 37 0,25 15 25 I 0,34 30 0,25 12 20 H 0,00 35 0,25 14 24 G -0,38 44 0,25 18 30 F -1,06 34 0,25 14 23 E -1,11 43 0,25 17 29 D -1,22 42 0,25 17 28 C -2,29 50 0,25 20 33 B -2,77 46 0,25 18 30 A -4,46 49 0,25 20 33
  35. 35. The geomechanical model - 1 Landslide risk reduction by coupling monitoring and numerical modelling
  36. 36. The geomechanical model - 2 Applying the continuum equivalent approach to the time dependent behavior 1) a Burgers visco-plastic model was assumed for the MRS; 2) a Burgers visco-plastic model coupled with a plasticity threshold was assumed for the RL, DRL and Ls. Viscosity values of MRC: the viscosity values of the Kelvin–Voight visco-elastic element was always assumed to be one order of magnitude higher than the ones used for the visco-plastic Maxwell element. For calibrating the viscosity values of RL, DRL and Ls a best fit was performed between the monitored displacements, referred to the different excavation and re-shaping steps within the landslide mass and the numerical modeled ones. Landslide risk reduction by coupling monitoring and numerical modelling
  37. 37. Sequential numerical modeling Landslide risk reduction by coupling monitoring and numerical modelling
  38. 38. Sequential numerical modeling: results Landslide risk reduction by coupling monitoring and numerical modelling
  39. 39. Final remarks 1) Integrated monitoring as a tool for better understanding and constraining the slope instability (refinement of the geological model); 2) Controlling the performance of stabilization countermeasures and management of emergency phases; 3) Testing the suitability of time of failure prediction based on semi-empirical models; 4) Successful attempt of integrating equivalent continuum approaches with visco-plastic constitutive laws; 5) Possible future development: the numerical model validated via backanalysis as a tool for implementing forward analyses, accounting for the work-related stress variations. Landslide risk reduction by coupling monitoring and numerical modelling

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