Abstract

All concrete mechanical properties given in design codes of concrete and reinforced concrete structures are correlated with the characteristic compressive strength fck. According to the EN 206-1 code, the characteristic strength is defined as the strength value below which 5 % of the population of all possible strength test results for given volume of concrete can be found. The value of fck can be defined as 5 % quantile of the statistical distribution of the concrete compressive strength, tested on cylindrical samples with a diameter of 150 mm and a height of 300 mm or on cubic samples with dimensions 150 × 150 × 150 mm assuming the conversion factor of 0.8. In order to verify compliance of strength of the considered concrete with the designed characteristic strength (ascribed to concrete class), it should be checked if the test results meet the appropriate statistical criteria. The characteristic strength defined by specifying of concrete class is determined at the designing stage of the structure and it is the basic controlled parameter, providing the requirements are fulfilled by the producer. Despite numerous publications on disadvantages of the code compliance criteria, marginalization of the problem is still visible. Concepts and methods based on probabilistic or statistical approaches are often sourced of skeptical views on quality control because they are not very familiar to most engineers. In the paper the assessment of the code compliance criteria has been made using the numerical simulation on the basis of the programs which has been written in C++. The innovative method for estimation test coefficients values contained in the code compliance criteria has been proposed. This method was developed on the basis of fuzzy methods.

Highlights

  • The class of concrete strength is a value fck that can be defined as the 5 % quantile of the statistical distribution of concrete compressive strength, tested on cylindrical samples with a diameter of 150 mm and a height of 300 mm or on bone samples with dimensions of 150 9 150 9 150 mm with a conversion factor 0.8

  • On the basis of which the quality of the concrete is assessed, is likely to have a probability character of which a random event will be in the area with fuzzy borders, or the fuzzy number (with a known membership function corresponding to the probability that the event will be up to a certain range [7].Concrete compressive strength fc meeting double compliance criteria can be written with a fuzzy set (4): fc 1⁄4 1⁄2lf ;CðfcmÞ; fcŠ; lf ;C : Fc ! 1⁄20; 1Š

  • The use of statistical-fuzzy method for a classification of concrete showed that the proposed standardized classification of concrete contains too many concrete classes with distributions often overlapping

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Summary

Introduction

The class of concrete strength is a value fck that can be defined as the 5 % quantile of the statistical distribution of concrete compressive strength, tested on cylindrical samples with a diameter of 150 mm and a height of 300 mm or on bone samples with dimensions of 150 9 150 9 150 mm with a conversion factor 0.8. In order to verify the compliance of the strength of the considered part of concrete with the designed characteristic strength (assigned to the class of concrete), it needs to be checked whether the results of sample tests meet the required statistic criteria. In the norms of design of concrete and reinforced structures all mechanical properties of concrete are correlated with the characteristic compressive strength fck. The aim of this study is to propose a numerical analysis of the behavior of reinforcing steel depending on the concrete class and to propose an analytical procedure for statistical and fuzzy classification of concrete, which takes into consideration marginal distribution of adjacent classes and limits the number of considered levels of concrete compressive strength. After an increasing tensile force is applied to the smooth rebar embedded in concrete under a certain load, there is a adhesion rupture associated with

Concrete-reinforcement bond versus defined concrete class
Numerical calculations
The calculation model
Theoretical analysis performed on three different models
Fuzzy criteria of compliance of concrete strength
Statistical-fuzzy classification of concrete
Approximation of membership function
Example of the application of statistical-fuzzy compatibility criteria
Findings
Conclusions
Full Text
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