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Blind test prediction of an infilled RC building with OpenSees

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The document describes a blind prediction contest involving the seismic response of a reinforced concrete frame structure with infill masonry panels subjected to shake table testing. Participants were asked to predict the structural response based on initial design data and an improved prediction after material and test data was provided. Numerical models were evaluated based on error compared to experimental displacements. The results showed good prediction for lower intensity shaking but increased error for stronger motions. A parametric study examined effects of modeling choices on response prediction.

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H. Rodrigues, A. Furtado, A. Arêde, H. Varum, M. Grubnisic, T.Sipos Hugo.f.rodrigues@ipleiria.pt Blind test prediction of an infilled RC building with OpenSees elemento Não-Linear Bielas
FRAMA–2015 International benchmark Blind Test Challenge Competition FRAMA - Blind Prediction Contest Specimen Construction Preliminary test Seismic test Post treatment Start of contest STEP 1 STEP 2 STEP 3 End of contest Teams challenging 1st report: Preliminary numerical models based on design data 2nd report: Numerical models based on actual material and ground motion data recorded during the test Final Results Optimization of the prediction results based on the experimental data
Motivation • Evaluate the performance of numerical models predicting the seismic behavior of a Reinforced concrete frame, with infill masonry panels • Make a parametric study, with the references model after the experimental test.
• gaining further insight into modelling and seismic design verifications for infill panels • Improvement of the structural strength, stiffness and deformation capacity and consideration of the of openings in infill panels FRAMA–2015 International benchmark Blind Test Challenge Purpose Participants will have: • access to the design data and input ground motion and should predict the expected seismic response • access to the experimental results and could modify their numerical modelling in order to improve the agreement between numerical and experimental results
FRAMA–2015 International benchmark Blind Test Challenge Shake table test 0 35 70 105 140 175 210 245 280 315 350 -1.2 -0.8 -0.4 0.0 0.4 0.8 1.2 -12 -8 -4 0 4 8 12 Acceleration(m/s2 ) Acceleration(g) Time (s) 0.05g 0.1g 0.2g 0.3g 0.4g 0.6g 0.7g 0.8g 1g 1.2g 0 5 10 15 20 25 30 35 -1.5 -1.2 -0.9 -0.6 -0.3 0.0 0.3 0.6 0.9 1.2 1.5 -15 -12 -9 -6 -3 0 3 6 9 12 15 Acceleration(m/s2) pga=0.1g pga=0.6g pga=1.2g Acceleration(g) Time (s) 0.0 0.2 0.4 0.6 0.8 1.0 0 1 2 3 4 Elasticresponsespectra(g) Period of the structure (s) ξ=1% ξ=2% ξ=3% ξ=4% • Shake table tests • 1:2.5 scaled structure • Shake table acceleration (N–S direction) at 10 different intensity levels Start of contest STEP 1 STEP 2 STEP 3 End of contest
FRAMA–2015 International benchmark Blind Test Challenge Start of contest: • Which software? • RC elements modelling strategy? • Infill masonry walls modelling strategy? • Damping? Start of contest STEP 1 STEP 2 STEP 3 End of contest
FRAMA–2015 International benchmark Blind Test Challenge Mass and damping • Weight of the frame structure: 74kN • Weight of the infill masonry walls: 33kN • Additional weights 1st floor: 48kN • Additional weights 2nd floor: 48kN • Additional weights at the roof: 96kN • Total weight:~300kN • Damping: 2% 48kN 48kN 96kN Start of contest STEP 1 STEP 2 STEP 3 End of contest
FRAMA–2015 International benchmark Blind Test Challenge RC structure modelling • Force-Based Beam-Column Element • 7 integration points • uniaxialMaterial Concrete01 • uniaxialMaterial Steel02 • fc,cub,storey1=39.1MPa • fc,cub,storey2=42.8MPa • fc,cub,storey3=27.6MPa • Ec=38.9GPa • fy,ø4mm=753.5MPa • fy,ø6mm=563.5MPa • fy,ø8mm=590.5MPa Start of contest STEP 1 STEP 2 STEP 3 End of contest
FRAMA–2015 International benchmark Blind Test Challenge IM walls modelling • IP and OOP behaviour • Consideration of the openings • Simplified macro-model elemento Não-Linear Bielas Fc Fy Fmax Fu dudmaxdydc Inter-Storey drift (%) Force (MPa) Start of contest STEP 1 STEP 2 STEP 3 End of contest
FRAMA–2015 International benchmark Blind Test Challenge IM walls modelling • IP and OOP behaviour • Consideration of the openings • Simplified macro-model 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 2 Strength(MPa) Drift (%) Wall 1 Wall 2 Wall 3 W1 W2 W3 Reduction factors - Openings area Start of contest STEP 1 STEP 2 STEP 3 End of contest
0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 2 Strength(MPa) Drift (%) Wall 4 Wall 5 Wall 6 FRAMA–2015 International benchmark Blind Test Challenge IM walls modelling • IP and OOP behaviour • Consideration of the openings • Simplified macro-model W6 W6 W4W3 W5 W5 Reduction factors - Openings area Start of contest STEP 1 STEP 2 STEP 3 End of contest
Rules and evaluation methodology [ ] [ ] [ ]∑∑∑ === −+−+−= N i iinum N i iinum N i iinumRMS IIIXDIIIXD N IIXDIIXD N IXDIXD N Error 1 2 exp,, 1 2 exp,, 1 2 exp,, ____ 1 ____ 1 ____ 1 dtt IIIXDIIIXD IIIXDIIIXD dtt IIXDIIXD IIXDIIXD dtt IXDIXD IXDIXD Error t t inumi t t inumi t t inumi t t inumi t t inumi t t inumi E )( ____ ____ )( ____ ____ )( ____ ____ 0 0 ,exp, 2 0 0 ,exp, 0 0 ,exp, 2 0 0 ,exp, 0 0 ,exp, 2 0 0 ,exp, ∫ ∫ ∫ ∫ ∫ ∫ ∫ ∫ ∫ ∫ ∫ ∫ ⋅       − + ⋅       − + ⋅       − = ERMSRMSE ErrorErrorError −=− Start of contest STEP 1 STEP 2 STEP 3 End of contest The participant teams are classified based on the calculated error of estimated displacements in the monitored points of the structure as compared to the measured displacements.
FRAMA–2015 International benchmark Blind Test Challenge Blind prediction results – Global teams blind prediction results Start of contest STEP 1 STEP 2 STEP 3 End of contest
FRAMA–2015 International benchmark Blind Test Challenge Blind prediction results – Global teams blind prediction results 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 0 20 40 60 80 100 120 140 160 180 200 IndividualErrorE-RMS Pga (g) Team #1 Team #2 Team #3 Team #4 Team #5 Team #6 Team #7 Team #8 Team #9 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 0 100 200 300 400 500 600 Team #1 Team #2 Team #3 Team #4 Team #5 Team #6 Team #7 Team #8 Team #9 CumulativeErrorE-RMS Pga (g) • Similar results between for pga <0.7g. • For pga> 0,7g a larger dispersion of the results is observed Start of contest STEP 1 STEP 2 STEP 3 End of contest
Intensity Level #1 pga=0.05g
Intensity Level #2 pga=0.10g
Intensity Level #3 pga=0.20g
Intensity Level #4 pga=0.30g
Intensity Level #5 pga=0.40g
Intensity Level #6 pga=0.60g
Intensity Level #7 pga=0.70g
Intensity Level #8 pga=0.80g
Intensity Level #9 pga=1g
Intensity Level #10 pga=1.20g
Blind prediction results – maximum inter-storey drift 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 0-1 Maximuminter-storeydrift(%) pga (g) Experimental Numerical 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 1-2 Maximuminter-storeydrift(%) pga (g) Experimental Numerical 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 2-3 Maximuminter-storeydrift(%) pga (g) Experimental Numerical • A good match between the prediction results and the experimental ones for pga lower than 0.7g Start of contest STEP 1 STEP 2 STEP 3 End of contest
Parametric study – Methodology • Infilled RC structures vs RC structures • Different viscous damping values were tested (ξ=0%, ξ=1%, ξ=3%, ξ=4% and ξ=5%) • Variations for infill masonry walls properties - initial stiffness (Ko) - maximum strength (Fmax) - combined fc, fy, fmax and fu (-25%; -20%; -15%;-10%-5%;+5%;+10%+15%;+20%;+25%) Start of contest STEP 1 STEP 2 STEP 3 End of contest
Parametric study RC structure vs infilled RC structure • Not considering the IM walls contribution increase the cumulative error significantly (more than 2 times) 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 0-1 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Model RCS 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 1-2 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Model RCS Start of contest STEP 1 STEP 2 STEP 3 End of contest
Parametric study RC structure vs infilled RC structure • Not considering the IM walls contribution increase the cumulative error significantly (more than 2 times) 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 0-1 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Model RCS 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 0 40 80 120 160 200 240 Blind Prediction Model RCS CumulativeErrorE-RMS Pga (g) Start of contest STEP 1 STEP 2 STEP 3 End of contest
• Better results obtained for ξ=5% ?!?!?!?!? • Large errors obtained considering ξ=0% ?!?!?!?!? 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 0 40 80 120 160 200 240 CumulativeErrorE-RMS Pga (g) Blind Prediction ξ=0% ξ=1% ξ=3% ξ=4% ξ=5% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 0-1 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction ξ=0% ξ=1% ξ=3% ξ=4% ξ=5% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 1-2 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction ξ=0% ξ=1% ξ=3% ξ=4% ξ=5% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 2-3 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction ξ=0% ξ=1% ξ=3% ξ=4% ξ=5% Start of contest STEP 1 STEP 2 STEP 3 End of contest Parametric study viscous damping
Parametric study Infill masonry walls mechanical properties • With the increasing and decreasing of the infills maximum strength it occurs an increasing of the cumulative error (around 10-20%) when compared with the original blind prediction response 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 0-1 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Fmax -20% Fmax -15% Fmax -10% Fmax -5% Fmax 5% Fmax 10% Fmax 15% Fmax 20% Start of contest STEP 1 STEP 2 STEP 3 End of contest 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 2 Strength(MPa) Drift (%) Wall 1 Wall 2 Wall 3
Parametric study Infill masonry walls mechanical properties 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 1-2 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Fmax -20% Fmax -15% Fmax -10% Fmax -5% Fmax 5% Fmax 10% Fmax 15% Fmax 20% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 1-2 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Ko -20% Ko -15% Ko -10% Ko -5% Ko 5% Ko 10% Ko 15% Ko 20% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 1-2 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Fc, Fy, Fmax, Fu -20% Fc, Fy, Fmax, Fu -15% Fc, Fy, Fmax, Fu -10% Fc, Fy, Fmax, Fu -5% Fc, Fy, Fmax, Fu 5% Fc, Fy, Fmax, Fu 10% Fc, Fy, Fmax, Fu 15% Fc, Fy, Fmax, Fu 20% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 2-3 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Fmax -20% Fmax -15% Fmax -10% Fmax -5% Fmax 5% Fmax 10% Fmax 15% Fmax 20% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 2-3 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Ko -20% Ko -15% Ko -10% Ko -5% Ko 5% Ko 10% Ko 15% Ko 20% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 2-3 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Fc, Fy, Fmax, Fu -20% Fc, Fy, Fmax, Fu -15% Fc, Fy, Fmax, Fu -10% Fc, Fy, Fmax, Fu -5% Fc, Fy, Fmax, Fu 5% Fc, Fy, Fmax, Fu 10% Fc, Fy, Fmax, Fu 15% Fc, Fy, Fmax, Fu 20% Start of contest STEP 1 STEP 2 STEP 3 End of contest 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 0-1 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Fmax -20% Fmax -15% Fmax -10% Fmax -5% Fmax 5% Fmax 10% Fmax 15% Fmax 20% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 0-1 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Ko -20% Ko -15% Ko -10% Ko -5% Ko 5% Ko 10% Ko 15% Ko 20% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 0-1 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Fc, Fy, Fmax, Fu -20% Fc, Fy, Fmax, Fu -15% Fc, Fy, Fmax, Fu -10% Fc, Fy, Fmax, Fu -5% Fc, Fy, Fmax, Fu 5% Fc, Fy, Fmax, Fu 10% Fc, Fy, Fmax, Fu 15% Fc, Fy, Fmax, Fu 20%
Final comments In the assessment of existing buildings, and design of new buildings… • consideration of the masonry infill walls (based on simple checking rules/procedures after the structural design) should be enforced • particular attention should be given to the openings through the reduction of the infills strength and drift parameters in the hysteretic behaviour • the infills collapse determined the structural response and conditioned the blind prediction results for pga>0,7g Taking into account the results dispersion obtained by all the teams, the numerical modelling of these type of structures needs further research with simplified and refined procedures to improve the future results. Start of contest STEP 1 STEP 2 STEP 3 End of contest
Acknowledgments Project POCI-01-0145-FEDER-007457 - CONSTRUCT - Institute of R&D in Structures and Construction funded by FEDER funds through COMPETE2020 - Programa Operacional Competitividade e Internacionalização (POCI) and by national funds through FCT - Fundação para a Ciência e a Tecnologia, Portugal. Numerical research was developed under financial support provided by FCT - Fundação para a Ciência e Tecnologia, Portugal, namely through the research project P0CI-01-0145-FEDER- 016898 e PTDC/ECM-EST/3790/2014 – ASPASSI - Safety Evaluation and Retrofitting of Infill masonry enclosure Walls for Seismic demands. The FRAMA–2015 International Benchmark / Blind Prediction Contest presented in this report is a part of the research project ”FRAmed–MAsonry Composites for Modeling and Standardization, FRAMA”, IP–11–2013–3013, supported by the Croatian Science Foundation (HrZZ) and its support is gratefully acknowledged.
Thanks for your attention! Obrigado

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Blind test prediction of an infilled RC building with OpenSees

  • 1. H. Rodrigues, A. Furtado, A. Arêde, H. Varum, M. Grubnisic, T.Sipos Hugo.f.rodrigues@ipleiria.pt Blind test prediction of an infilled RC building with OpenSees elemento Não-Linear Bielas
  • 2. FRAMA–2015 International benchmark Blind Test Challenge Competition FRAMA - Blind Prediction Contest Specimen Construction Preliminary test Seismic test Post treatment Start of contest STEP 1 STEP 2 STEP 3 End of contest Teams challenging 1st report: Preliminary numerical models based on design data 2nd report: Numerical models based on actual material and ground motion data recorded during the test Final Results Optimization of the prediction results based on the experimental data
  • 3. Motivation • Evaluate the performance of numerical models predicting the seismic behavior of a Reinforced concrete frame, with infill masonry panels • Make a parametric study, with the references model after the experimental test.
  • 4. • gaining further insight into modelling and seismic design verifications for infill panels • Improvement of the structural strength, stiffness and deformation capacity and consideration of the of openings in infill panels FRAMA–2015 International benchmark Blind Test Challenge Purpose Participants will have: • access to the design data and input ground motion and should predict the expected seismic response • access to the experimental results and could modify their numerical modelling in order to improve the agreement between numerical and experimental results
  • 5. FRAMA–2015 International benchmark Blind Test Challenge Shake table test 0 35 70 105 140 175 210 245 280 315 350 -1.2 -0.8 -0.4 0.0 0.4 0.8 1.2 -12 -8 -4 0 4 8 12 Acceleration(m/s2 ) Acceleration(g) Time (s) 0.05g 0.1g 0.2g 0.3g 0.4g 0.6g 0.7g 0.8g 1g 1.2g 0 5 10 15 20 25 30 35 -1.5 -1.2 -0.9 -0.6 -0.3 0.0 0.3 0.6 0.9 1.2 1.5 -15 -12 -9 -6 -3 0 3 6 9 12 15 Acceleration(m/s2) pga=0.1g pga=0.6g pga=1.2g Acceleration(g) Time (s) 0.0 0.2 0.4 0.6 0.8 1.0 0 1 2 3 4 Elasticresponsespectra(g) Period of the structure (s) ξ=1% ξ=2% ξ=3% ξ=4% • Shake table tests • 1:2.5 scaled structure • Shake table acceleration (N–S direction) at 10 different intensity levels Start of contest STEP 1 STEP 2 STEP 3 End of contest
  • 6. FRAMA–2015 International benchmark Blind Test Challenge Start of contest: • Which software? • RC elements modelling strategy? • Infill masonry walls modelling strategy? • Damping? Start of contest STEP 1 STEP 2 STEP 3 End of contest
  • 7. FRAMA–2015 International benchmark Blind Test Challenge Mass and damping • Weight of the frame structure: 74kN • Weight of the infill masonry walls: 33kN • Additional weights 1st floor: 48kN • Additional weights 2nd floor: 48kN • Additional weights at the roof: 96kN • Total weight:~300kN • Damping: 2% 48kN 48kN 96kN Start of contest STEP 1 STEP 2 STEP 3 End of contest
  • 8. FRAMA–2015 International benchmark Blind Test Challenge RC structure modelling • Force-Based Beam-Column Element • 7 integration points • uniaxialMaterial Concrete01 • uniaxialMaterial Steel02 • fc,cub,storey1=39.1MPa • fc,cub,storey2=42.8MPa • fc,cub,storey3=27.6MPa • Ec=38.9GPa • fy,ø4mm=753.5MPa • fy,ø6mm=563.5MPa • fy,ø8mm=590.5MPa Start of contest STEP 1 STEP 2 STEP 3 End of contest
  • 9. FRAMA–2015 International benchmark Blind Test Challenge IM walls modelling • IP and OOP behaviour • Consideration of the openings • Simplified macro-model elemento Não-Linear Bielas Fc Fy Fmax Fu dudmaxdydc Inter-Storey drift (%) Force (MPa) Start of contest STEP 1 STEP 2 STEP 3 End of contest
  • 10. FRAMA–2015 International benchmark Blind Test Challenge IM walls modelling • IP and OOP behaviour • Consideration of the openings • Simplified macro-model 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 2 Strength(MPa) Drift (%) Wall 1 Wall 2 Wall 3 W1 W2 W3 Reduction factors - Openings area Start of contest STEP 1 STEP 2 STEP 3 End of contest
  • 11. 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 2 Strength(MPa) Drift (%) Wall 4 Wall 5 Wall 6 FRAMA–2015 International benchmark Blind Test Challenge IM walls modelling • IP and OOP behaviour • Consideration of the openings • Simplified macro-model W6 W6 W4W3 W5 W5 Reduction factors - Openings area Start of contest STEP 1 STEP 2 STEP 3 End of contest
  • 12. Rules and evaluation methodology [ ] [ ] [ ]∑∑∑ === −+−+−= N i iinum N i iinum N i iinumRMS IIIXDIIIXD N IIXDIIXD N IXDIXD N Error 1 2 exp,, 1 2 exp,, 1 2 exp,, ____ 1 ____ 1 ____ 1 dtt IIIXDIIIXD IIIXDIIIXD dtt IIXDIIXD IIXDIIXD dtt IXDIXD IXDIXD Error t t inumi t t inumi t t inumi t t inumi t t inumi t t inumi E )( ____ ____ )( ____ ____ )( ____ ____ 0 0 ,exp, 2 0 0 ,exp, 0 0 ,exp, 2 0 0 ,exp, 0 0 ,exp, 2 0 0 ,exp, ∫ ∫ ∫ ∫ ∫ ∫ ∫ ∫ ∫ ∫ ∫ ∫ ⋅       − + ⋅       − + ⋅       − = ERMSRMSE ErrorErrorError −=− Start of contest STEP 1 STEP 2 STEP 3 End of contest The participant teams are classified based on the calculated error of estimated displacements in the monitored points of the structure as compared to the measured displacements.
  • 13. FRAMA–2015 International benchmark Blind Test Challenge Blind prediction results – Global teams blind prediction results Start of contest STEP 1 STEP 2 STEP 3 End of contest
  • 14. FRAMA–2015 International benchmark Blind Test Challenge Blind prediction results – Global teams blind prediction results 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 0 20 40 60 80 100 120 140 160 180 200 IndividualErrorE-RMS Pga (g) Team #1 Team #2 Team #3 Team #4 Team #5 Team #6 Team #7 Team #8 Team #9 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 0 100 200 300 400 500 600 Team #1 Team #2 Team #3 Team #4 Team #5 Team #6 Team #7 Team #8 Team #9 CumulativeErrorE-RMS Pga (g) • Similar results between for pga <0.7g. • For pga> 0,7g a larger dispersion of the results is observed Start of contest STEP 1 STEP 2 STEP 3 End of contest
  • 25. Blind prediction results – maximum inter-storey drift 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 0-1 Maximuminter-storeydrift(%) pga (g) Experimental Numerical 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 1-2 Maximuminter-storeydrift(%) pga (g) Experimental Numerical 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 2-3 Maximuminter-storeydrift(%) pga (g) Experimental Numerical • A good match between the prediction results and the experimental ones for pga lower than 0.7g Start of contest STEP 1 STEP 2 STEP 3 End of contest
  • 26. Parametric study – Methodology • Infilled RC structures vs RC structures • Different viscous damping values were tested (ξ=0%, ξ=1%, ξ=3%, ξ=4% and ξ=5%) • Variations for infill masonry walls properties - initial stiffness (Ko) - maximum strength (Fmax) - combined fc, fy, fmax and fu (-25%; -20%; -15%;-10%-5%;+5%;+10%+15%;+20%;+25%) Start of contest STEP 1 STEP 2 STEP 3 End of contest
  • 27. Parametric study RC structure vs infilled RC structure • Not considering the IM walls contribution increase the cumulative error significantly (more than 2 times) 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 0-1 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Model RCS 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 1-2 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Model RCS Start of contest STEP 1 STEP 2 STEP 3 End of contest
  • 28. Parametric study RC structure vs infilled RC structure • Not considering the IM walls contribution increase the cumulative error significantly (more than 2 times) 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 0-1 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Model RCS 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 0 40 80 120 160 200 240 Blind Prediction Model RCS CumulativeErrorE-RMS Pga (g) Start of contest STEP 1 STEP 2 STEP 3 End of contest
  • 29. • Better results obtained for ξ=5% ?!?!?!?!? • Large errors obtained considering ξ=0% ?!?!?!?!? 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 0 40 80 120 160 200 240 CumulativeErrorE-RMS Pga (g) Blind Prediction ξ=0% ξ=1% ξ=3% ξ=4% ξ=5% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 0-1 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction ξ=0% ξ=1% ξ=3% ξ=4% ξ=5% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 1-2 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction ξ=0% ξ=1% ξ=3% ξ=4% ξ=5% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 2-3 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction ξ=0% ξ=1% ξ=3% ξ=4% ξ=5% Start of contest STEP 1 STEP 2 STEP 3 End of contest Parametric study viscous damping
  • 30. Parametric study Infill masonry walls mechanical properties • With the increasing and decreasing of the infills maximum strength it occurs an increasing of the cumulative error (around 10-20%) when compared with the original blind prediction response 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 0-1 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Fmax -20% Fmax -15% Fmax -10% Fmax -5% Fmax 5% Fmax 10% Fmax 15% Fmax 20% Start of contest STEP 1 STEP 2 STEP 3 End of contest 0 0.2 0.4 0.6 0.8 1 1.2 0 0.5 1 1.5 2 Strength(MPa) Drift (%) Wall 1 Wall 2 Wall 3
  • 31. Parametric study Infill masonry walls mechanical properties 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 1-2 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Fmax -20% Fmax -15% Fmax -10% Fmax -5% Fmax 5% Fmax 10% Fmax 15% Fmax 20% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 1-2 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Ko -20% Ko -15% Ko -10% Ko -5% Ko 5% Ko 10% Ko 15% Ko 20% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 1-2 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Fc, Fy, Fmax, Fu -20% Fc, Fy, Fmax, Fu -15% Fc, Fy, Fmax, Fu -10% Fc, Fy, Fmax, Fu -5% Fc, Fy, Fmax, Fu 5% Fc, Fy, Fmax, Fu 10% Fc, Fy, Fmax, Fu 15% Fc, Fy, Fmax, Fu 20% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 2-3 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Fmax -20% Fmax -15% Fmax -10% Fmax -5% Fmax 5% Fmax 10% Fmax 15% Fmax 20% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 2-3 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Ko -20% Ko -15% Ko -10% Ko -5% Ko 5% Ko 10% Ko 15% Ko 20% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 2-3 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Fc, Fy, Fmax, Fu -20% Fc, Fy, Fmax, Fu -15% Fc, Fy, Fmax, Fu -10% Fc, Fy, Fmax, Fu -5% Fc, Fy, Fmax, Fu 5% Fc, Fy, Fmax, Fu 10% Fc, Fy, Fmax, Fu 15% Fc, Fy, Fmax, Fu 20% Start of contest STEP 1 STEP 2 STEP 3 End of contest 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 0-1 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Fmax -20% Fmax -15% Fmax -10% Fmax -5% Fmax 5% Fmax 10% Fmax 15% Fmax 20% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 0-1 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Ko -20% Ko -15% Ko -10% Ko -5% Ko 5% Ko 10% Ko 15% Ko 20% 0.0 0.3 0.6 0.9 1.2 0 2 4 6 8 Storey Level: 0-1 Maximuminter-storeydrift(%) pga (g) Experimental Blind Prediction Fc, Fy, Fmax, Fu -20% Fc, Fy, Fmax, Fu -15% Fc, Fy, Fmax, Fu -10% Fc, Fy, Fmax, Fu -5% Fc, Fy, Fmax, Fu 5% Fc, Fy, Fmax, Fu 10% Fc, Fy, Fmax, Fu 15% Fc, Fy, Fmax, Fu 20%
  • 32. Final comments In the assessment of existing buildings, and design of new buildings… • consideration of the masonry infill walls (based on simple checking rules/procedures after the structural design) should be enforced • particular attention should be given to the openings through the reduction of the infills strength and drift parameters in the hysteretic behaviour • the infills collapse determined the structural response and conditioned the blind prediction results for pga>0,7g Taking into account the results dispersion obtained by all the teams, the numerical modelling of these type of structures needs further research with simplified and refined procedures to improve the future results. Start of contest STEP 1 STEP 2 STEP 3 End of contest
  • 33. Acknowledgments Project POCI-01-0145-FEDER-007457 - CONSTRUCT - Institute of R&D in Structures and Construction funded by FEDER funds through COMPETE2020 - Programa Operacional Competitividade e Internacionalização (POCI) and by national funds through FCT - Fundação para a Ciência e a Tecnologia, Portugal. Numerical research was developed under financial support provided by FCT - Fundação para a Ciência e Tecnologia, Portugal, namely through the research project P0CI-01-0145-FEDER- 016898 e PTDC/ECM-EST/3790/2014 – ASPASSI - Safety Evaluation and Retrofitting of Infill masonry enclosure Walls for Seismic demands. The FRAMA–2015 International Benchmark / Blind Prediction Contest presented in this report is a part of the research project ”FRAmed–MAsonry Composites for Modeling and Standardization, FRAMA”, IP–11–2013–3013, supported by the Croatian Science Foundation (HrZZ) and its support is gratefully acknowledged.
  • 34. Thanks for your attention! Obrigado