Slope Stabilisation modelling in the island tropics Prof. Malcolm G Anderson PhD DSc CEng FICE A.M.ASCE
Landslide risk analysis is an interdisciplinary problem but Governments forced to act on as need project basis
What is causing the landslides?
Slope stability Factors Hillslope hydrology Ground water Rainfall events Surface runoff Drainage Geotechnics: the application of the laws of mechanics and hydraulics to the mechanical problems relating to soils (and rocks) Material properties Slope geometry Slope stability
Slope stability views Helicopter view - big picture 4x4 view - local practice Cutlass view - single slope
The big picture Disaster losses US$bn
The big picture... but normalisation tells us... (After Pielke et al 2008) that what matters is what we build, where we build and how we build
The big picture Poor infrastructure on steep slopes
The big picture vulnerable communities
Modelling context In soil mechanics the accuracy of computed results never exceeds that of a crude estimate, and the principle function of the theory exists in teaching us what and how to observe in the field Terzaghi (1936).
Modelling context A dynamic hydrology model Rainfall for iteration i evapo-transpiration runoff θ u
Modelling context Basic dynamic hydrology model θ n,1 (iteration n, cell 1) (not in step i=1 since we already have both θ and ψ) ψ from suctionmoisture curve if unsaturated d ( z) dz Hydraulic gradient if saturated dh dl K(θ i ) from M-Q K s if saturated Vertical (1D) unsaturated flow: Richard s eqn. 2D saturated flow: Darcy s Law Loss of water from cell 1 to cell 2 Moisture content for next iteration: θ n+1,1
Modelling context...with slope stability CHASM Combined Hydrology and slope Stability Model Numerical stability of solution to Richard s eqn dependent on mesh size and iteration period. 1x1m grid and iteration period of 60 seconds is usually stable At end of each hour pore-pressure fields (u) are input to 2D stability analysis
Modelling context CHASM parameters Basic CHASM parameters: β Slope angle Number of soils, soil strata location (depth) For each slope material: γ Material bulk density (saturated and unsaturated) c' Cohesion Φ Angle of internal friction Ks Saturated hydraulic conductivity θs Saturated moisture content ψ-θ Suction moisture curve Plus: initial hydraulic boundary conditions, rainfall Optional: Vegetation, Loading, Soil reinforcement parameters
Modelling context Slope stability and rainfall Modelling different rainfall scenarios 1. Single design storms based on Intensity- Duration-Frequency relationships e.g. 1:100, 24-hour storm 2. Observed (or realistic) rainfall scenarios
Modelling context Using software to determine the trigger
Modelling context Creating design charts Choose range of conditions covered by the design charts:
Modelling context Pre-run storm events 350mm 24 hour rainfall
Modelling context 250mm 24 hour rainfall
Modelling context Vegetation influences on stability Vegetation influences: Rainfall interception Evapo-transpiration Hydraulic conductivity Root reinforcement Surcharge
Modelling context Topographic influences on stability Slope angle, 2D Convergence or divergence, 3D (Wilkinson, Anderson, Lloyd, Renaud, 2002. doi:10.1016/s1364-8152(01)00078-0)
Modelling context to determine stability controls Steep upper slopes relatively stable due to lack of water convergence and rock at shallow depth SHALLOWER SLOPES CAN HAVE GREATER INSTABILITY PROBLEMS 60 degrees Significant landslide on shallow slope caused by water convergence within soil mass 20 degrees
Modelling outcomes Complex models and policy uncertainty Not uncommon for one expert to say that there is little to be concerned about whilst another expert will say the same risk is of major significance Wharton School PA. USA
4x4 view Helicopter view 4x4 view Cutlass view
Modelling local practices - homes
4x4 view
Predicting road maintenance
Predicting road maintenance Data integration for landslide risk modelling & planning Soil strength data multi-sourced from Government, consultants and MoSSaiC site investigations Soil permeability data multi-sourced from consultants and MoSSaiC site investigations Primary Road network - Government digital map updated from consultants and MoSSaiC GPS sources
Predicting road maintenance
Cutlass view Helicopter view 4x4 view Cutlass view
Local context and model data
Material properties Complex, spatially variable, residual materials
cohesion kpa Data Soil strength data multi-sourced from Government, consultants and MoSSaiC site investigations 140 120 grade 6 grade 5 grade 1 grade 4 100 grade 1 cohesion range 100-7000kPa grade 3 80 grade 2 60 40 grade 5 grade 3 grade 2 grade 1 mean 20 0 grade 6 grade 4 0 10 20 30 40 50 60 phi degrees exponential regression of c & phi mean values y = 6.1288e 0.0376x R 2 = 0.9224
Inclusion of uncertainty Probability (%) of the failure surface being above illustrated surfaces
Action summary Acquire and consolidate relevant data Use models in helicopter mode to: Determine landslide (hazard) trigger Construct design charts Prioritise maintenance Guide policy Use models in 4x4 mode to: Assess & review local practices E.g. road cut-slope benching, retaining walls etc Use models in cutlass mode to: Estimate specific probable failure depths Incorporate uncertainty