This is the slide of my thesis defense. It's on Ultra-high Performance Fiber-Reinforced concrete.

1. A presentation for defending the thesis on aforementioned topic
Size-Dependent Stress-strain Modeling And Evaluation of Energy
Performance of Ultra-High-Performance Fiber-reinforced Concrete
Arafat Alam (1523030)
Thesis Supervisor: Dr. Md. Habibur Rahman Sobuz
BECM 4000 : Project and Thesis

2. Motivation – Ultra-High-Performance Concrete
• Ultra-High Strength (>150 MPa) and Ductility
• Lack of application due to complex mix, initial cost & limited research
• Prediction of UHPFRC behavior
• Evaluate structural and energy performance

3. • Slenderness ratio ≥ 2
• Increase of size = Steeper Descending branch
• Snap-back effect
• Doubling prism length = Halves the effective strain
• Behavior for other sizes
Background – Size-Effect

4. • Enhance ductility and strength.
• 2% optimum.
Background – Fiber-Volume Effect
*Graph from Kazemi, Sadegh & Lubell, Adam. (2012). Influence of Specimen Size and Fiber Content on Mechanical Properties
of Ultra-High-Performance Fiber-Reinforced Concrete. Aci Materials Journal. 109. 675-684. 10.14359/51684165.

5. Background – Analytical Modeling
• Popovics’s Model
• CEB Model
• Sargin Model
No Analytical model for complete stress-strain
behavior in compression for UHPFRC
Predicts stress-strain curve from a known peak stress-strain value.

6. • To predict the stress-strain responses of UHPFRC incorporating size-dependent
characteristics with varying fiber-content.
• To simulate the behavior of RC members incorporating size-dependent
UHPFRC by a numerical segmental moment-rotation approach.
• To assess the thermal & energy performance of UHPFRC structural member.
Background – Objective

7. Methodology – Analytical Modeling
𝑓 = 𝑓𝑐𝑜
𝜀 𝑎𝑥 𝑝𝑜𝑝
𝜀 𝑐𝑜
𝑟
𝑟−1+
𝜀 𝑎𝑥 𝑝𝑜𝑝
𝜀 𝑐𝑜
𝑟 (1)
𝑅𝑒𝑞𝑢𝑖𝑟𝑒𝑑 𝒇 𝒄𝒐 & 𝐸 𝑐
Popovics’s Modified Equation εax pop = Incremental strain
𝜀 𝑐𝑜 = Peak Strain
𝑓𝑐𝑜 = Exp. Peak Stress
𝑟 =
𝐸𝑐
[𝐸𝑐 − ( 𝑓𝑐𝑜 𝜀 𝑐𝑜)]
Size Dependency – Strain Adjustment Factor
𝜺 𝒄𝒐−𝟐𝟎𝟎 = 𝜀 𝑐𝑜−𝑡𝑒𝑠𝑡 −
𝑓𝑐𝑜
𝐸𝑐
𝐿 𝑝𝑟−𝑡𝑒𝑠𝑡
200
+
𝑓𝑐𝑜
𝐸𝑐
(2)

8. Methodology – Analytical Modeling
For 1%
𝜺 𝒄𝒐−𝟐𝟎𝟎−𝟏 = 6.42 × 10−6 𝑓𝑐𝑜 + 2.88 × 10−3
For 2%
𝜺 𝒄𝒐−𝟐𝟎𝟎−𝟐 = −1.78 × 10−6 𝑓𝑐𝑜 + 3.88 × 10−3
For 3%
𝜺 𝒄𝒐−𝟐𝟎𝟎−𝟑 = 1.08 × 10−6
𝑓𝑐𝑜 + 3.44 × 10−3
Data Analysis
• 121 published tests
• Filtered by fiber-volume percentage 1%-3%
• Data Extraction, Anomaly Detection, Regression.
(3)
(4)
(5)

9. Methodology – Predicting Unknown Sizes
Exp. 100x200 mm
Num. 100x200 mm
Num. 100x300 mm
Num. 100x400 mm
Prediction from Model
Exp. 100x300 mm
Exp. 100x400 mm
Comparison
ComparisonOnly one size required for
any size conversion
No exp. Data in the
model for 100x300 mm
and 100x400 mm
*Exp. Data from Sobuz, Habibur & Visintin, Phillip & Ali, Mohamed & Singh, Manpreet & Griffith, M. & Sheikh, Abdul. (2016). Manufacturing
ultra-high performance concrete utilising conventional materials and production methods. Construction and Building Materials. 111. 251-261.
10.1016/j.conbuildmat.2016.02.102.

10. Methodology – Segmental Method
Euler-Bernoulli
Deformation
Size-Dependent
Rotation (R)
S-D
Curvature (C)
S-D
Flexural Rigidity (EI)

11. Methodology – Energy Modeling
UHPC vs NWC Roofs
One-storied (single-family)
building modeled in Revit 2020
Cooling Load Setup
Cooling Load Calculation
from Revit
Exported to gbXML
Imported into Green Building Studio
Energy Requirement
Comparison

12. Methodology – Energy Modeling
Space 1
Space 3
Space 2
NWC
UHPFRC

13. Result & Discussion – Influence of Size
Accurate Peak Stress-Strain
Linear behavior till peak stress
Larger size = Lesser Peak Strain &
Post-Peak ductility
Snap-Back effect!

14. Result & Discussion – Influence of Fiber-Content
Aberrant when 3%
Increase of Fiber = Ductile Post-Peak+ Higher
compressive Strength
• Restrains lateral strain + Large axial deformation
• Less entrapped air, higher density

15. Result & Discussion – Model Validation
Conservative Estimate
Predicts High-Residual Stress
Ductile Response
Comparison with Literature Data for 1% Comparison with Literature Data for 2%

16. Result & Discussion – Model Validation
Comparison with Literature Data for 3%
Lower accuracy for 3%
Fiber-bundling and irregular distribution

17. Result & Discussion – Segmental (M-R)
100x200 mm property 100x300 mm property 100x400 mm property
Brittle Behavior – Higher
size and lower Fiber
Higher Fiber = Larger Rotation = Higher Ductility =
Ample Warning before Failure
• Prevents intergrowth of micro-crack and macro-cracks

18. Result & Discussion – Segmental (M-R)
1% fiber
No Snap-backHigher Size = Brittle Behavior & Snap-back Effect
Smaller beam more ductile due to improved fiber-orientation
2% fiber 3% fiber

19. Result & Discussion – Segmental (M-C)
Large Continued fatigue,
UHPFRC redistribute stress and strain
100x200 mm – 3% fiber = Max Curvature
100x400 mm – 1% fiber = Min Curvature
100x200 mm property 100x300 mm property 100x400 mm property

20. Result & Discussion – Segmental (M-EI)
Size-dependent when derived
from M-R
1% Fiber 100x300 mm property
Higher EI with Increased Fiber
amount

21. Result & Discussion – Energy Modeling
• 18 % increase cooling
load demand
• UHPC higher thermal
conductivity and lower
specific heat
• Higher density, not much
effective

22. Result & Discussion – Energy Modeling

23. Conclusion
• Influence of size modest. Increased size exhibits slightly lower compressive strength.
• Ductility increased with fiber-volume content but decreased with specimen size.
• Established model of this study presents a conservative estimate with literature test data for
complete stress-strain curves in compression.
• Appropriate to simulate UHPFRC beam using M-R with size-dependent material property.
• Rotational capacity of UHPFRC beam increased with fiber-content but decreased with specimen
size. High Cyclic load, Earthquake Resistance ,and Ample Warning time.
• UHPFRC structure exhibit slightly higher cooling load demand and energy consumption than
NWC. Thermal coating required.

24. Acknowledgement
• Thanks to my supervisor Dr. Md. Habibur Rahman Sobuz for guiding through
the whole process.
• Thanks to respected faculty Md. Jewel Rana for teaching me Energy Modeling
and Analysis.

25. Thank You