brinkgreve technet 2018-2 - tu delft research portal · • software company, developing the plaxis...
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Copyright Plaxis bv, 2018
New developments in PLAXIS:Material Point Method & Reliability Analysis
Dr. Ronald B.J. Brinkgreve, Plaxis bv / Delft University of Technology
(with help of Anita Laera, Markus Bürg)
Content
• Introduction
• Material Point Method (MPM)
– Performance improvements
– 3D modelling facilities
– Applications
• Reliability Analysis
– Sources of uncertainty
– Stochastic parameters
– Limit state function
– Calculations (FORM, Directional Sampling)
– Results
– Applications
• Conclusions
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Copyright Plaxis bv, 2018
Introduction
Who is Plaxis bv?
• Software company, developing the PLAXIS geo-engineering software
• Established in 1993 as a spin-off from TUDelft
• Headquarters in Delft (Delftechpark); offices in Singapore and US
• 60+ professionals in Research, Software Development, Quality,
Marketing, Sales and Services
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Introduction
What is PLAXIS?
• Geo-engineering software based on the Finite Element Method
• Stress, deformation, dynamics (earthquakes), stability, groundwater flow and
thermal analysis of soils, rocks and soil-structure interaction
• Applications: Foundations (onshore, offshore), excavations, embankments,
dams, slopes, tunnels, mining applications, …
• Key words: Efficient, robust, user-friendly, reliable
• Continuous improvements and new developments
• 19000+ licenses world-wide (2018)
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Copyright Plaxis bv, 2018
Introduction
Main topics of New Developments:
• Dynamic analysis, liquefaction (earthquakes)
• Structural design (in the ground), inter-operability, BIM
• Tunnels and rock modelling
• Monopile design for offshore wind turbines (MoDeTo)
• Large deformation analysis: Material Point Method (MPM)
• Reliability Analysis, Probabilistic Analysis (ProbAna)
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Material Point Method
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Material Point Method
Numerical modelling of large deformations:
• Finite Element Method (FEM)
Limitations: distortion of mesh, flow of material, changing contact
� Material Point Method (MPM):
Material points can ‘flow’ through the calculation grid
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Challenges of MPM
Measures needed to overcome numerical difficulties, making MPM applicable for geo-engineering & design in practice:
• Points moving from one cell to another
• Dealing with empty cells
• Determining active boundaries
• Application of loads and boundary conditions
• Smoothing of stresses
• Contact formulation
• Stability and convergence of the calculation
• Efficient use of computer resources
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Soil MPM Soil FEM
Seamless connection of MPM and FEM
Division of geometry into different domains
• Soil MPM:
– Relaxation of mesh in Convective Phase
• Soil FEM:
– Using Updated Lagrange formulation
– Seamless connection to ‘relaxed’
MPM mesh
• Structure FEM
– Independent FEM mesh
• Contact boundaries
– Applied around structures to
‘sense’ contact with material points
– Cohesive-frictional properties
taken from soil in contact
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MPM applications
Typical geo-engineering applications involving large deformations:
• Slope failure, landslide
• Pile and anchor installation
• Spudcan penetration, punch-through, extraction
• Pipeline and cable movements
• Trenching, dredging
• Impact problems
• ...
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PLAXIS MPM: Pre-processing
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PLAXIS MPM: Pre-processing
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PLAXIS MPM: Post-processing
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MPM application: Spudcan penetration
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Reliability Analysis
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Reliability Analysis
Probability of failure against certain criteria
• Sources of uncertainty
• Stochastic parameters
• Limit State function
• Calculation methods
• Results
• Applications:
- Reliability of dykes
- Lifetime reliability of quay walls
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Copyright Plaxis bv, 2018
Sources of uncertainty
Aleatoric uncertainty (natural variation):
• E.g. variation of soil properties, weather conditions
Epistemic uncertainty
(lack of knowledge or inaccuracy):
• E.g. limitations of models, measurement errors
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Stochastic parameters
In Geo-engineering, stochastic parameters can be:
• Model parameters (soil, structures)
• Loads
• Water levels
• Geometric dimensions:
- Layer thickness
- Water depth
- Pile penetr. depth)
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Copyright Plaxis bv, 2018
Stochastic parameters
Water levels:
• Stochastic distribution of water level (per segment)
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Stochastic parameters
Correlation between parameters:
For example:
• Soil stiffness is related
to soil strength
Definition of correlation
matrix as column per
parameter
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Stochastic parameters > Stochastic results
Distribution of INPUT parameters leads to distribution of OUTPUT results
(INPUT and OUTPUT can be loads or resistances)
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Limit State Function
Limit State function (Z):
Z = R – S
R = measure of Resistance
S = measure of Load
Failure is defined as Z<0
Probability of failure =
overlapping area between
distributions of R and S
µ = mean value
σ = standard deviation
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Copyright Plaxis bv, 2018
Limit State Function
Selecting parameters to define limit state criteria (based on result types):
Z = Criterion – Result
• Stresses or pore pressures in a point in the soil
• Displacements or strains in a point in the soil
• Anchor force
• Maximum displacement or force (as for example the bending moment) in a
plate or shell
• Shear or lateral traction in an embedded beam
• Global safety factor
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Limit State Function
Selecting parameters to define limit state criteria (based on result types):
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Copyright Plaxis bv, 2018
Calculation methods
Monte-Carlo Method (MC)
• Random selection of stochastic parameters
• Requires very many calculations
First Order Reliability Method (FORM)
• Gradient type iterative calculation method (COBYLA or Abdo-Rackwitz)
• Highly reduced number of calculations
Directional Sampling (DS)
• Monte-Carlo type sampling method
• Smart selection of stochastic parameters (different strategies)
• Reduced number of calculations
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Calculation methods – FORM
Graphical representation of the FORM approach
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MPP = Most Probable Point or Design Point
Copyright Plaxis bv, 2018
Calculation methods
Comparing different
methods and
strategies
� Different accuracies
� Different number of
evaluations
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Results
Results
• Probability of Failure Pf
• Reliability Index β
• Design Point
(critical values of stochastic variables)
• Importance factors
• Histogram of results
• …
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Results
Results
• …
• Distribution of results
(Point cloud)
• Convergence:
• # iterations
• Errors
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Applications
Copyright Plaxis bv, 2018
Applications
Reliability analysis of quay walls
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(based on MSc thesis Herm-Jan Wolters, 2012)
Applications
Reliability analysis of quay walls
Advantage of finite element method:
Different failure mechanisms can be considered simultaneously
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(courtesy of Alfred Roubos)
Copyright Plaxis bv, 2018
Applications
Reliability analysis of quay walls
• Supporting research by Alfred Roubos (Havenbedrijf Rotterdam, TUDelft)
• Analysing reliability and rehabilitation of quay walls over their lifetime under
changing conditions (changing harbour depth, water levels, steel corrosion,
different loading conditions)
• Stochastic variables: soil parameters, layer thickness, retaining height, water
level differences, surface load, steel thickness (corrosion!)
• ‘Failure’ criteria: Global safety factor, bending moment in wall, steel stress
• Results also show influence of parameters on probability of failure
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Applications
Reliability of quay walls over time
• Including corrosion of combi-walls
• Probabilistic analysis can help and optimise the decision making
(maintenance, upgrading, demolish and rebuild)
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Copyright Plaxis bv, 2018
Applications
Reliability analysis of river dykes
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(based on MSc thesis Job Janssen, 2016)
Applications
Reliability analysis of river dykes
• Reinforcement by including retaining wall in the dyke
• Conventional design method leads to unrealistically heavy wall
(ULS design bending moment 2159 kNm/m vs. SLS design 247 kNm/m)
• Probabilistic analysis:
• Stochastic variables: soil parameters
• Results show that dyke is safe with less heavy (lower cost) structure
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Copyright Plaxis bv, 2018
Applications
Reliability analysis of river dykes - Results
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Conclusions
PLAXIS
• World-leading finite element software for geo-engineering applications
• Continuous new developments
Material Point Method
• Large deformation analysis, material flow, installation effects, contact
• Making MPM applicable for geo-engineering & design
Reliability analysis (Probabilistic analysis)
• Parameters as stochastic variables, limit state criteria, probability of failure
• Applications (so far) in quay walls and river dykes
• Reliability analysis gives insight in failure causes and can help reducing cost
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Copyright Plaxis bv, 2018
Thank you