• Smart Structures and Systems
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• v.17 no.4
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• pp.577-591
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• 2016

In this study, the transfer length of 2400 MPa, seven-wire high-strength steel strands with a 15.2 mm diameter in pretensioned prestressed concrete (PSC) beams utilizing high strength concrete over 58 MPa at prestress release was evaluated experimentally. 32 specimens, which have the variables of concrete compressive strength, concrete cover depth, and the number of PS strands, were fabricated and corresponding transfer lengths were measured. The strands were released gradually by slowly reducing the pressure in the hydraulic stressing rams. The measured results of transfer length showed that the transfer length decreased as the concrete compressive strength and concrete cover depth increased. The number of strands had a very small effect, and the effect varied with both the concrete cover depth and concrete strength. The results were compared to current design codes and transfer lengths predicted by other researchers. The comparison results showed that the current transfer length prediction models in design codes may be conservatively used for 2400 MPa high-strength strands in high-strength concrete beams exceeding 58 MPa at prestress release.

• Journal of the Korea Concrete Institute
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• v.12 no.3
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• pp.57-66
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• 2000

The ductility is an important consideration in the design of reinforced concrete structures. In the seismic design of reinforced concrete columns, it is necessary to allow for relatively large ductilities that the seismic energy be absorbed without shear failure of significant strength degradation after the reinforcement yielding in columns. Therefore, prediction of the ductility should be as accurate as possible. This research investigate the ductile behavior of rectangular reinforced high-strength concrete columns like as bridge piers with confinement steel. The effects on the ductility of axial load, lateral reinforcement ratio, longitudinal reinforcement ratio, shear span ratio, and compressive strength of concrete were investigated analytically using layered section analysis. as the results, it was proposed the proper relationship between ductility and variables and formulated into equations.

• Proceedings of the Korea Concrete Institute Conference
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• 2004.05a
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• pp.446-449
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• 2004

Traditional prediction models have been developed with a fixed equation from based on the limited number of data and parameters. If new data is quite different from original data, then the model should update not only its coefficients but also its equation form. However, artificial neural network dose not need a specific equation form. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. Therefore, the purpose of this study is to verify faith and application of prediction system of concrete strength using artificial neural networks through mock-up test.

• Computers and Concrete
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• v.23 no.4
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• pp.255-265
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• 2019

The potential of using genetic programming to predict engineering data has caught the attention of researchers in recent years. The present paper utilized weighted genetic programming (WGP), a derivative model of genetic programming (GP), to model the compressive strength of concrete. The calculation results of Abrams' laws, which are used as the design codes for calculating the compressive strength of concrete, were treated as the inputs for the genetic programming model. Therefore, knowledge of the Abrams' laws, which is not a factor of influence on common data-based learning approaches, was considered to be a potential factor affecting genetic programming models. Significant outcomes of this work include: 1) the employed design codes positively affected the prediction accuracy of modeling the compressive strength of concrete; 2) a new equation was suggested to replace the design code for predicting concrete strength; and 3) common data-based learning approaches were evolved into knowledge-based learning approaches using historical data and design codes.

• Computers and Concrete
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• v.21 no.4
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• pp.463-470
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• 2018

In the present study, soft computing i.e., machine learning techniques and regression models algorithms have earned much importance for the prediction of the various parameters in different fields of science and engineering. This paper depicts that how regression models can be implemented for the prediction of compressive strength of concrete. Three models are taken into consideration for this; they are Gaussian Process for Regression (GPR), Multi Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR). Contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and age in days have been taken as inputs and compressive strength as output for GPR, MARS and MPMR models. A comparatively large set of data including 1030 normalized previously published results which were obtained from experiments were utilized. Here, a comparison is made between the results obtained from all the above mentioned models and the model which provides the best fit is established. The experimental results manifest that proposed models are robust for determination of compressive strength of concrete.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.05a
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• pp.527-532
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• 2001

The crack of concrete induced by a temperature rise in early age concrete due to the heat of ration of cement is a serious problem for massive or high strength concrete structures. However, re is still no reasonable equations for the prediction of the temperature rising. On this study, the prediction equations of the heat of hydration of different types of binder are pained from the adiabatic temperature rise test, and compared with the results from different nations to obtain the best approximated equation. The strengths of concrete of which specimens were placed in the same chamber for the adiabatic to were compared with those under standard curing.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.1530-1535
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• 2009

Moisture evaporates, when concrete is exposed to fire, not only at concrete surface but also at inside the concrete to adjust the equilibrium and transfer properties of moisture. The equilibrium properties of moisture are described by means of water vapor sorption isotherms, which illustrate the hysteretical behavior of materials. In this paper, the prediction method of the moisture distribution inside the high strength concrete members under the high temperature is presented. Finite element method is employed to facilitate the moisture diffusion analysis for any position of member. And the moisture diffusivity model of high strength concrete by high temperature is proposed. To demonstrate the validity of this numerical procedure, the prediction by the proposed algorithm is compared with the test result of other researcher. The proposed algorithm shows a good agreement with the experimental results including the vaporization effect inside the concrete.

• Structural Monitoring and Maintenance
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• v.11 no.1
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• pp.41-55
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• 2024

This study aims to determine the characteristics of concrete as identified by Rebound Hammer and Ultrasonic Pulse Velocity (UPV) tests, focusing particularly on their efficacy in estimating compressive strength of concrete material. The study involved three concrete samples designed to achieve a target strength of 29 MPa, comprising normal concrete, instant concrete, and concrete with additives. These were cast into cube specimens measuring 150×150×150 mm. Compressive strength values were determined through both destructive and non-destructive testing on the cubic specimens. As a result, the non-destructive methods yielded varying outcomes for each correlation approach, influenced by the differing constituent materials in the tested concretes. However, normal concrete consistently showed the most reliable correlation, followed by concrete with additives, and lastly, instant concrete. The study found that combining Rebound Hammer and UPV tests enhances the prediction accuracy of compressive strength of concrete. This synergy was quantified through multivariate regression, considering UPV, rebound number, and actual compressive strength. The findings also suggest a more significant influence of the Rebound Hammer measurements on predicting compressive strength for BN and BA, whereas UPV and RN had a similar impact on predicting BI compressive strength.

• Structural Engineering and Mechanics
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• v.91 no.6
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• pp.583-589
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• 2024

Accurately predicting the strength of concrete is vital for ensuring the safety and durability of structures, thereby contributing to time and cost savings throughout the design and construction phases. The compressive strength of concrete is determined by various material factors, including the type of cement, composition ratios of concrete mixtures, curing time, and environmental conditions. While mix design establishes the proportions of each material for concrete, predicting strength before experimental measurement remains a challenging task. In this study, Abrams's law was chosen as a representative investigative approach to estimating concrete compressive strength. Abrams asserted that concrete compressive strength depends solely on the water-cement ratio and proposed a logarithmic linear relationship. However, Abrams's law is only applicable to concrete using cement as the sole binding material and may not be suitable for modern concrete mixtures. Therefore, this research aims to predict concrete compressive strength by applying various conventional regression analyses and machine learning methods. Six models were selected based on performance experiment data collected from various literature sources on different concrete mixtures. The models were assessed using Root Mean Squared Error (RMSE) and coefficient of determination (R²) to identify the optimal model.

• Computers and Concrete
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• v.14 no.3
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• pp.247-262
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• 2014

Partial replacement of Portland cement by slag can reduce the energy consumption and $CO_2$ emission therefore is beneficial to circular economy and sustainable development. Compressive strength is the most important engineering property of concrete. This paper presents a numerical procedure to predict the development of compressive strength of slag blended concrete. This numerical procedure starts with a kinetic hydration model for cement-slag blends by considering the production of calcium hydroxide in cement hydration and its consumption in slag reactions. Reaction degrees of cement slag are obtained as accompanied results from the hydration model. Gel-space ratio of hardening slag blended concrete is determined using reaction degrees of cement and slag, mixing proportions of concrete, and volume stoichiometries of cement hydration and slag reaction. Furthermore, the development of compressive strength is evaluated through Powers' gel-space ratio theory considering the contributions of cement hydration and slag reaction. The proposed model is verified through experimental data on concrete with different water-to-binder ratios and slag substitution ratios.