SlideShare a Scribd company logo
2
Most read
4
Most read
17
Most read
PREDICTION OF COMPRESSIVE STRENGTH
OF CONCRETE FROM EARLY AGE TEST
RESULT
M. Monjurul Hasan
Undergraduate Student (Level-4, Term-2)
Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh
Dr. Ahsanul Kabir
Professor, Dept. of Civil Engineering
Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh
Outline
 Introduction
 Objective
 Previous Approaches.
 Proposed Approach
 Mathematical Model
 Performance
 Conclusion
Introduction
• Concrete has versatile use in the construction
practice.
• The compressive strength is one of the most
important and useful properties of concrete.
• The design strength of the concrete normally
represents its 28th day strength.
• 28 days is a considerable time to wait for the test
results of concrete strength, while it is mandatory to
represent the process of quality control.
Introduction (Contd..)
• For every mix one has to wait a long time for the
assurance of its quality.
• Hence, the need for an easy and suitable means for
estimating the strength at an early age of concrete is
being felt all the time.
Objective
• To develop a simple relation which has the potential
to predict the compressive strength of the concrete
from early days strength.
• To evaluate nature of concrete strength gain pattern
with time for a particular type of mix.
• To formulate a quick, handy & flexible computational
method to asses the nature of concrete strength gain
with time.
Previous Approaches
• Traditional empirical formula
• Linear Regression model
• Artificial neural network
• Genetic algorithm
• Support vector mechanism
• M5P Tree model
Proposed Approach
(Nonlinear Regression model)
• Data used for this study was taken from
previous study (Garg, 2003).
Proposed Approach ( cont. …)
• Concrete Data Ranges
(without Admixture, ordinary Portland cement)
Proposed Approach ( cont. …)
• First step : to understand the strength gaining
pattern of the concrete with age
Proposed Mathematical Model
The Mathematical Model:
where, Stn = Strength of the concrete at nth day.(n = 1,2,3,…..); Dn = Number of
days; p and q are constants for each curve but different for different data sets
(curves). Though this equation (Eq. 1) is formed independently, it is similar to
the equation (Eq. 2) proposed by ACI committee ( ACI 209-71) for predicting
compressive strength at any time.
Here a and b are constants, = 28-day strength and t is time and this
equation (Eq. 2) can be recast to similar form of Equation 1.
dcf 28
'
)(
Mathematical Model ( Cont. ...)
To utilize the above equation (Eq. 1), just value of two constants
(p and q) are to be determined.
It was observed that, , values of p can be expressed as the
function of q and (Stn) [which is a polynomial surface equation].
The equation of the correlation is given below:
p = a + b.q + c.Stn + d.q.Stn + e.Stn
2 (3)
Where, Stn= Strength of the concrete at nth day. (n = 1, 2, 3, ……)
and a, b, c, d and e are the coefficients.
Mathematical Model ( Cont. ...)
As we build up the correlation for 7th day test result of concrete [n=7],
the values of the coefficients were derived as, a = 10.23; b = -0.9075;
c = 0.3412; d = 0.1721; e = 0.0112 from regression analysis of the
available data for concrete with stone chips as course aggregate
Putting these values in Equation 3 the following equation was obtained:
p = 10.23 - 0.9075q + 0.3412St7 + 0.1721q.St7 + 0.0112St7
2 (4)
For 14th day strength results [n=14] the coefficients are, a = -4.527;
b = -1.041; c = 1.373; d = 0.1406; e = -0.0125. Putting these values into
Equation 3 the following equation was obtained:
p = -4.527- 1.041q + 1.373St14 + 0.1406q.St14 - 0.0125St14
2 (5)
Mathematical Model ( Cont. ...)
Represented surface ….
Mathematical Model ( Cont. ...)
Represented surface ….
Performance
Performance ( Cont. ...)
Performance ( Cont. ...)
Conclusion
• This paper represents a simple mathematical model
• In this study, the concrete strength gain characteristic
with age is modeled by a simple mathematical
equation (rational polynomial) and a polynomial
surface equation
• The proposed equations have the potential to predict
strength data for every age.
• This will help in making quick decision for accidental
poor concreting at site and reduce delay.
That’s it,
Thank You

More Related Content

PDF
M.tech thesis
PPTX
Prediction of compressive strength of concrete with a
PDF
Predicting 28 Days Compressive Strength of Concrete from 7 Days Test Result
PPTX
structure control system
PPTX
Post tension Floor System
PPTX
MIVAN_An Aluminum Formwork Construction Technique
PPTX
Chapter 4 repair, rehabilitation & retrofiiting
DOCX
SITE VISIT REPORT
M.tech thesis
Prediction of compressive strength of concrete with a
Predicting 28 Days Compressive Strength of Concrete from 7 Days Test Result
structure control system
Post tension Floor System
MIVAN_An Aluminum Formwork Construction Technique
Chapter 4 repair, rehabilitation & retrofiiting
SITE VISIT REPORT

What's hot (20)

DOCX
project report on earth quake resisting building
PPTX
Prestressed Concrete Structures
PPTX
Base isolation of structures
PPTX
Rcc jacketing
DOC
My Resume (Facade Designer)
PDF
2 Brick thick wall flemish bond Assignment
DOC
Raja kumar Resume (Senior Civil Engineer)
PPTX
Design of residential building
PPTX
Prefabrication construction
PPTX
Presentation on MIVAN -- A versatile aluminum formwork construction technique
PDF
Aluminium Formwork Vs Conventional Formwork
PPT
Causes and prevention of cracks in buildings
PDF
Structural Engineering Project Proposal PowerPoint Presentation Slides
PPTX
Final Year Project Presentation
PPTX
PREFABRICATED STRUCTURES
DOC
RESUME
PPTX
Fibre Reinforced Concrete
PPTX
What is post tensioning
DOCX
Quality control of concrete
PDF
Defects in buildings & remedies
project report on earth quake resisting building
Prestressed Concrete Structures
Base isolation of structures
Rcc jacketing
My Resume (Facade Designer)
2 Brick thick wall flemish bond Assignment
Raja kumar Resume (Senior Civil Engineer)
Design of residential building
Prefabrication construction
Presentation on MIVAN -- A versatile aluminum formwork construction technique
Aluminium Formwork Vs Conventional Formwork
Causes and prevention of cracks in buildings
Structural Engineering Project Proposal PowerPoint Presentation Slides
Final Year Project Presentation
PREFABRICATED STRUCTURES
RESUME
Fibre Reinforced Concrete
What is post tensioning
Quality control of concrete
Defects in buildings & remedies
Ad

Viewers also liked (7)

PPTX
Data Compression Project Presentation
PPTX
SHEAR STRENGTH THEORY
PDF
High Performance Concrete
PPT
Mekanika Tanah - Triaxial shear test
PPTX
Vane shear test
DOCX
Buckling test engt110
PDF
Class 6 Shear Strength - Direct Shear Test ( Geotechnical Engineering )
Data Compression Project Presentation
SHEAR STRENGTH THEORY
High Performance Concrete
Mekanika Tanah - Triaxial shear test
Vane shear test
Buckling test engt110
Class 6 Shear Strength - Direct Shear Test ( Geotechnical Engineering )
Ad

Similar to PREDICTION OF COMPRESSIVE STRENGTH OF CONCRETE FROM EARLY AGE TEST RESULT (20)

PDF
Evaluation of compressive strength of cement using rayleigh’s dimensional ana...
PDF
Strength Prediction Model for Concrete
PPTX
Research Presentation.pptx
PPTX
PHASE IIIkjbihhhhhhhghhhhhhhhhhhhhhhhhhhhhh.pptx
PPTX
Compressive stregnth of concerte using image processing ANM & ML
PDF
International Refereed Journal of Engineering and Science (IRJES)
PDF
International Refereed Journal of Engineering and Science (IRJES)
PDF
Compressive Strength Estimation of Mesh Embedded Masonry Prism Using Empirica...
PDF
Application of ANN and ANFIS Models in Determining Compressive Strength of Co...
PPTX
CONSTRUCTION MATERIALS OF CIVIL ENGG.pptx
PDF
Module2 stiffness- rajesh sir
PDF
Module2 stiffness- rajesh sir
PDF
Probabilistic Design of Hollow Circular Composite Structure by using Finite E...
PDF
Assessing Uncertainty of Pushover Analysis to Geometric Modeling
PDF
Cost Optimization of Elevated Circular Water Storage Tank
PDF
Cost Optimization of Elevated Circular Water Storage Tank
PDF
International Journal of Engineering Research and Development
PDF
Predicting the strength of self compacting self curing concrete using artific...
PDF
MOS Report Rev001
PPT
Capstone
Evaluation of compressive strength of cement using rayleigh’s dimensional ana...
Strength Prediction Model for Concrete
Research Presentation.pptx
PHASE IIIkjbihhhhhhhghhhhhhhhhhhhhhhhhhhhhh.pptx
Compressive stregnth of concerte using image processing ANM & ML
International Refereed Journal of Engineering and Science (IRJES)
International Refereed Journal of Engineering and Science (IRJES)
Compressive Strength Estimation of Mesh Embedded Masonry Prism Using Empirica...
Application of ANN and ANFIS Models in Determining Compressive Strength of Co...
CONSTRUCTION MATERIALS OF CIVIL ENGG.pptx
Module2 stiffness- rajesh sir
Module2 stiffness- rajesh sir
Probabilistic Design of Hollow Circular Composite Structure by using Finite E...
Assessing Uncertainty of Pushover Analysis to Geometric Modeling
Cost Optimization of Elevated Circular Water Storage Tank
Cost Optimization of Elevated Circular Water Storage Tank
International Journal of Engineering Research and Development
Predicting the strength of self compacting self curing concrete using artific...
MOS Report Rev001
Capstone

Recently uploaded (20)

PPT
Total quality management ppt for engineering students
PDF
Exploratory_Data_Analysis_Fundamentals.pdf
PPTX
UNIT 4 Total Quality Management .pptx
PDF
The CXO Playbook 2025 – Future-Ready Strategies for C-Suite Leaders Cerebrai...
PPTX
Fundamentals of safety and accident prevention -final (1).pptx
PDF
BIO-INSPIRED HORMONAL MODULATION AND ADAPTIVE ORCHESTRATION IN S-AI-GPT
PPT
INTRODUCTION -Data Warehousing and Mining-M.Tech- VTU.ppt
PPTX
Information Storage and Retrieval Techniques Unit III
PDF
SMART SIGNAL TIMING FOR URBAN INTERSECTIONS USING REAL-TIME VEHICLE DETECTI...
PDF
A SYSTEMATIC REVIEW OF APPLICATIONS IN FRAUD DETECTION
PPTX
Safety Seminar civil to be ensured for safe working.
PPTX
MET 305 2019 SCHEME MODULE 2 COMPLETE.pptx
PDF
BIO-INSPIRED ARCHITECTURE FOR PARSIMONIOUS CONVERSATIONAL INTELLIGENCE : THE ...
PPTX
Current and future trends in Computer Vision.pptx
PPTX
Nature of X-rays, X- Ray Equipment, Fluoroscopy
PDF
737-MAX_SRG.pdf student reference guides
PDF
Soil Improvement Techniques Note - Rabbi
PDF
COURSE DESCRIPTOR OF SURVEYING R24 SYLLABUS
PPT
Occupational Health and Safety Management System
PDF
Enhancing Cyber Defense Against Zero-Day Attacks using Ensemble Neural Networks
Total quality management ppt for engineering students
Exploratory_Data_Analysis_Fundamentals.pdf
UNIT 4 Total Quality Management .pptx
The CXO Playbook 2025 – Future-Ready Strategies for C-Suite Leaders Cerebrai...
Fundamentals of safety and accident prevention -final (1).pptx
BIO-INSPIRED HORMONAL MODULATION AND ADAPTIVE ORCHESTRATION IN S-AI-GPT
INTRODUCTION -Data Warehousing and Mining-M.Tech- VTU.ppt
Information Storage and Retrieval Techniques Unit III
SMART SIGNAL TIMING FOR URBAN INTERSECTIONS USING REAL-TIME VEHICLE DETECTI...
A SYSTEMATIC REVIEW OF APPLICATIONS IN FRAUD DETECTION
Safety Seminar civil to be ensured for safe working.
MET 305 2019 SCHEME MODULE 2 COMPLETE.pptx
BIO-INSPIRED ARCHITECTURE FOR PARSIMONIOUS CONVERSATIONAL INTELLIGENCE : THE ...
Current and future trends in Computer Vision.pptx
Nature of X-rays, X- Ray Equipment, Fluoroscopy
737-MAX_SRG.pdf student reference guides
Soil Improvement Techniques Note - Rabbi
COURSE DESCRIPTOR OF SURVEYING R24 SYLLABUS
Occupational Health and Safety Management System
Enhancing Cyber Defense Against Zero-Day Attacks using Ensemble Neural Networks

PREDICTION OF COMPRESSIVE STRENGTH OF CONCRETE FROM EARLY AGE TEST RESULT

  • 1. PREDICTION OF COMPRESSIVE STRENGTH OF CONCRETE FROM EARLY AGE TEST RESULT M. Monjurul Hasan Undergraduate Student (Level-4, Term-2) Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh Dr. Ahsanul Kabir Professor, Dept. of Civil Engineering Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh
  • 2. Outline  Introduction  Objective  Previous Approaches.  Proposed Approach  Mathematical Model  Performance  Conclusion
  • 3. Introduction • Concrete has versatile use in the construction practice. • The compressive strength is one of the most important and useful properties of concrete. • The design strength of the concrete normally represents its 28th day strength. • 28 days is a considerable time to wait for the test results of concrete strength, while it is mandatory to represent the process of quality control.
  • 4. Introduction (Contd..) • For every mix one has to wait a long time for the assurance of its quality. • Hence, the need for an easy and suitable means for estimating the strength at an early age of concrete is being felt all the time.
  • 5. Objective • To develop a simple relation which has the potential to predict the compressive strength of the concrete from early days strength. • To evaluate nature of concrete strength gain pattern with time for a particular type of mix. • To formulate a quick, handy & flexible computational method to asses the nature of concrete strength gain with time.
  • 6. Previous Approaches • Traditional empirical formula • Linear Regression model • Artificial neural network • Genetic algorithm • Support vector mechanism • M5P Tree model
  • 7. Proposed Approach (Nonlinear Regression model) • Data used for this study was taken from previous study (Garg, 2003).
  • 8. Proposed Approach ( cont. …) • Concrete Data Ranges (without Admixture, ordinary Portland cement)
  • 9. Proposed Approach ( cont. …) • First step : to understand the strength gaining pattern of the concrete with age
  • 10. Proposed Mathematical Model The Mathematical Model: where, Stn = Strength of the concrete at nth day.(n = 1,2,3,…..); Dn = Number of days; p and q are constants for each curve but different for different data sets (curves). Though this equation (Eq. 1) is formed independently, it is similar to the equation (Eq. 2) proposed by ACI committee ( ACI 209-71) for predicting compressive strength at any time. Here a and b are constants, = 28-day strength and t is time and this equation (Eq. 2) can be recast to similar form of Equation 1. dcf 28 ' )(
  • 11. Mathematical Model ( Cont. ...) To utilize the above equation (Eq. 1), just value of two constants (p and q) are to be determined. It was observed that, , values of p can be expressed as the function of q and (Stn) [which is a polynomial surface equation]. The equation of the correlation is given below: p = a + b.q + c.Stn + d.q.Stn + e.Stn 2 (3) Where, Stn= Strength of the concrete at nth day. (n = 1, 2, 3, ……) and a, b, c, d and e are the coefficients.
  • 12. Mathematical Model ( Cont. ...) As we build up the correlation for 7th day test result of concrete [n=7], the values of the coefficients were derived as, a = 10.23; b = -0.9075; c = 0.3412; d = 0.1721; e = 0.0112 from regression analysis of the available data for concrete with stone chips as course aggregate Putting these values in Equation 3 the following equation was obtained: p = 10.23 - 0.9075q + 0.3412St7 + 0.1721q.St7 + 0.0112St7 2 (4) For 14th day strength results [n=14] the coefficients are, a = -4.527; b = -1.041; c = 1.373; d = 0.1406; e = -0.0125. Putting these values into Equation 3 the following equation was obtained: p = -4.527- 1.041q + 1.373St14 + 0.1406q.St14 - 0.0125St14 2 (5)
  • 13. Mathematical Model ( Cont. ...) Represented surface ….
  • 14. Mathematical Model ( Cont. ...) Represented surface ….
  • 18. Conclusion • This paper represents a simple mathematical model • In this study, the concrete strength gain characteristic with age is modeled by a simple mathematical equation (rational polynomial) and a polynomial surface equation • The proposed equations have the potential to predict strength data for every age. • This will help in making quick decision for accidental poor concreting at site and reduce delay.