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Simulation of Crack Propagation in Concrete Hydropower Dam ...

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<strong>Simulation</strong> <strong>of</strong> <strong>Crack</strong> <strong>Propagation</strong> <strong>in</strong><strong>Concrete</strong> <strong>Hydropower</strong> <strong>Dam</strong>StructuresRichard Malm 1,2 , Manouchehr Hassanzadeh 1,3 Tobias Gasch 2 ,Daniel Eriksson 21 KTH Royal Institute <strong>of</strong> Technology / <strong>Concrete</strong> Structures2 Vattenfall Research and Development / Civil Eng<strong>in</strong>eer<strong>in</strong>g3 Lund University / Build<strong>in</strong>g Materials<strong>Crack</strong> <strong>Propagation</strong> <strong>in</strong> <strong>Concrete</strong> <strong>Hydropower</strong><strong>Dam</strong> Structures• Ongo<strong>in</strong>g extensive program to upgrade the Swedishhydropower plants- Generator foundation- Influence <strong>of</strong> cracks on the dam safetyWater levelUpstream sideFront-plateButtress wallDownstream sideInspection gangwayInsulat<strong>in</strong>g wall


2.5 mStorf<strong>in</strong>nforsen hydropower dam• Storf<strong>in</strong>nforsen concrete buttress dam- Total length <strong>of</strong> 1200 m (800 m concrete)- 100 concrete monoliths• Several different types <strong>of</strong> cracks found <strong>in</strong>-situWater levelUpstream sideFront-plateButtress wallDownstream sideInspection gangwayInsulat<strong>in</strong>g wallNumerical simulations• One monolith is modeled with 3D shell elements- Re<strong>in</strong>forced concrete• Nonl<strong>in</strong>ear material model CDP (<strong>Concrete</strong> <strong>Dam</strong>agedPlasticity) <strong>in</strong> ABAQUS v 6.10• Thermal analyses- W<strong>in</strong>ter air temperature – 15 °C- Summer air temperature +25 °C• <strong>Simulation</strong> steps- Static loads (gravity, water pressure)- Cycle w<strong>in</strong>ter and summer temperatures for the <strong>in</strong>itialdesign <strong>of</strong> the monolith- Cycle w<strong>in</strong>ter and summer temperatures after the<strong>in</strong>sulat<strong>in</strong>g wall was <strong>in</strong>stalledc150 mmTwo layers <strong>of</strong>rebars φ19 mmEach 50 mmbelow theconcretesurfacec400 mmc400 mmc400 mmc400 mmc300 mmc400 mm


Steady State Thermal Calculations• Cyclic steady state thermal calculations were performed- Summer – w<strong>in</strong>ter (without an <strong>in</strong>sulat<strong>in</strong>g wall)- Summer – w<strong>in</strong>ter (with an <strong>in</strong>sulat<strong>in</strong>g wall)• Coupled thermo-stress analysis- Import<strong>in</strong>g the steady state temperatures <strong>in</strong>to a model thatcalculate result<strong>in</strong>g stresses and predict crack<strong>in</strong>gW<strong>in</strong>ter conditionsSummer conditions<strong>Simulation</strong> - Seasonal temperature variationBefore the <strong>in</strong>sulat<strong>in</strong>g wallIncl<strong>in</strong>ed cracks <strong>in</strong> the buttressHorizontal cracks <strong>in</strong> the front-plateAfter the <strong>in</strong>sulat<strong>in</strong>g wallIncl<strong>in</strong>ed crack <strong>in</strong> the buttress


Animation <strong>of</strong> the crack propagationCyclic seasonal temperature variation (summer/w<strong>in</strong>ter)Deformation scale factor 400Probabilistic analyses• Study the <strong>in</strong>fluence <strong>of</strong> material properties and materialdistribution on the crack trajectory- Starts from an analysis where the <strong>in</strong>cl<strong>in</strong>ed crack is <strong>in</strong>itiatedand simulate further crack propagation• Monte Carlo <strong>Simulation</strong> - 1000 analyses- Sub-model <strong>of</strong> the area <strong>of</strong> <strong>in</strong>terest with a significantly ref<strong>in</strong>edmeshSub-model


Stochastic material propertiesProbability• Generated 1000 random material properties with log-normaldistribution0.250.200.150.100.05Statistics <strong>of</strong> randomgenerated propertiesN = 1000µ = 2.53 MPaσ = 0.78 MPaCOV = 0.3100 1 2 3 4 5 6 7Tensile strength (MPa)Probability0.200.180.160.140.120.100.080.060.040.02Statistics <strong>of</strong> randomgenerated propertiesN = 1000µ = 120.9 Nm/m22σ = 35.8 Nm/mCOV = 0.30050 100 150 200 250 3002Fracture energy (Nm/m )010 15 20 25 30 35 40 45Elastic modulus (GPa)• Assum<strong>in</strong>g a high correlation between the material properties,above 95%88300Probability0.200.180.160.140.120.100.080.060.040.02Statistics <strong>of</strong> randomgenerated propertiesN = 1000µ = 25.2 GPaσ = 3.8 GPaCOV = 0.15Tensile strength (MPa)642Tensile strength (MPa)642Fracture energy (nm/m 2 )250200150100050 100 150 200 250 300Fracture energy (Nm/m 2 )010 20 30 40 50Elastic modulus (MPa)5010 20 30 40 50Elastic modulus (MPa)Material distribution• Randomly assign<strong>in</strong>g a set <strong>of</strong> material properties (f t , E c , G f ) foreach element <strong>in</strong> the sub-model and simulat<strong>in</strong>g the crackpropagationTensile Strength [MPa]Regions With Low Tensile Strength [MPa]Max6542.532Mean f t= 2.54 MPa1M<strong>in</strong>


Area subjected to crack<strong>in</strong>g• Based on all simulations- All cracked elements summarized <strong>in</strong> one plot- Calculated probability <strong>of</strong> crack<strong>in</strong>g<strong>Crack</strong> PatternCalculated probability <strong>of</strong> crack<strong>in</strong>g0.50.450.40.350.30.250.20.15<strong>Crack</strong>ed element fromthe all simulations<strong>Crack</strong>ed element <strong>in</strong> the orig<strong>in</strong>alanalysis (with mean values)0.10.05Conclusions• Several, more or less cracked dams <strong>in</strong> Sweden.- In many cases due to the new pattern <strong>of</strong> generator operation- Extensive ongo<strong>in</strong>g program to upgrade the dams• In this project, the non-l<strong>in</strong>ear f<strong>in</strong>ite element method have beenused to- Expla<strong>in</strong> the cause <strong>of</strong> cracks <strong>in</strong> a concrete buttress dam- Study the <strong>in</strong>fluence <strong>of</strong> distribution <strong>in</strong> material properties on thecrack trajectory- Prelim<strong>in</strong>ary results shows a difference <strong>in</strong> crack trajectory obta<strong>in</strong>edfrom a determ<strong>in</strong>istic analysis with mean values compared to themost probable crack trajectory obta<strong>in</strong>ed from probabilisticanalyses


Thanks for your attention!Richard Malm, PhDKTH Royal Institute <strong>of</strong> Technology<strong>Concrete</strong> Structures (malm@byv.kth.se)Vattenfall Research and DevelopmentCivil Eng<strong>in</strong>eer<strong>in</strong>g (richard.malm2@vattenfall.com)

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