• Journal of the Korea Concrete Institute
• /
• v.11 no.3
• /
• pp.23-34
• /
• 1999

In designing PSC box girder bridge, the dead load, prestressing force, creep and shrinkage of concrete are the main factors which influence the camber and deflection of segmental concrete structure under construction. Among these factors the creep and shrinkage are the functions of the time-dependent property which, therefore, must considered with time. The prediction model for estimating creep and shrinkage of concrete has been suggested by ACI, CEB/FIP, JSCE and KSCE design code. In this study the creep and shrinkage test were carried out for four curing ages of concrete which was applied to the pretressed concrete box-girder bridge at a construction site, and the results of test were compared to the values of prediction by the design code. Shrinkage test shows that the test results are similar to KSCE-96 and JSCE-96 but very higher than other prediction model and creep test results are generally similar to ACI-209 and DSCE-96 but lower than other prediction models in contrast to shrinkage test.

• Computers and Concrete
• /
• v.21 no.6
• /
• pp.697-703
• /
• 2018

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

• Journal of the Korea Concrete Institute
• /
• v.15 no.1
• /
• pp.87-94
• /
• 2003

Construction plan and strength control have limitations in construction production field because it is difficult to predict the form removal strength and development of specified concrete strength. However, we can have reasonable construction plan and strength control if prediction of concrete strength is available. In this study, firstly, the newly proposed strength prediction model with maturity method was compared with the logistic model to test the adaptability. Secondly, the determination of time of form removal was verified through the new strength prediction model. As the results, it is found that investigation of the activation energy that are used to calculate equivalent age is necessary, and new strength prediction model was proved to be more accurate in the strength prediction than logistic model in the early age. Moreover, the use of new model was more reasonable because it has low SSE and high decisive factor. If we adopt new strength prediction model at construction field, we can expect the reduced period of work through the reduced time of form removal.

• International Journal of Concrete Structures and Materials
• /
• v.2 no.2
• /
• pp.115-122
• /
• 2008

Ductility is important in the design of reinforced concrete structures. In seismic design of reinforced concrete members, it is necessary to allow for relatively large ductility so that the seismic energy is absorbed to avoid shear failure or significant degradation of strength even after yielding of reinforcing steels in the concrete member occurs. Therefore, prediction of the ductility should be as accurate as possible. The principal aim of this paper is to present the basic data for the ductility evaluation of reinforced high-strength concrete beams. Accordingly, 23 flexural tests were conducted on full-scale structural concrete beam specimens having concrete compressive strength of 40, 60, and 70MPa. The test results were then reviewed in terms of flexural capacity and ductility. The effect of concrete compressive strength, web reinforcement ratio, tension steel ratio, and shear span to beam depth ratio on ductility were investigated experimentally.

• Journal of the Korea Concrete Institute
• /
• v.23 no.2
• /
• pp.185-193
• /
• 2011

In the present study, a unified shear design method was developed to evaluate the shear strength of concrete beams with and without FRP shear reinforcement. The contributions of FRP and concrete on shear strength were defined separately. By comparing the current design method calculated results with the existing test results, it was found that Triantafillou model shows a reliable prediction of FRP effective strain and FRP shear strength contributions. The concrete shear strength contribution was defined by the strain-based shear strength model developed in the previous study. The shear strength of concrete compression zone was evaluated based on the material failure criteria of the concrete subjected to the compressive normal and shear stresses. The proposed strength model was verified by comparing its prediction results to prior test results. The comparisons showed that the proposed method accurately predicts the strengths of the test specimens for both FRP shear reinforced and unreinforced concrete beams.

• Magazine of the Korea Concrete Institute
• /
• v.8 no.3
• /
• pp.197-207
• /
• 1996

Maturity is expressed as the integral of time and temperature of concrete above a datum temperature. The maturity concept proposes that concrete of the same mix at the same maturity has the same strength, whatever combination of temperature and time makes up that maturity. In this study, the Nurse-Saul function which was proposed to account for the effects of temperature and time on strength developrnent is used in computing maturity. After existing various functions are considered to relate concrete strength to the maturity value, new strength-maturity function is proposed. Tests ;ire conducted in order to determine d datum temperature and compare prechction value with measured concrete strength. The constants in proposed prediction equation are determined from test results, and the equation is adopted to predict the strength of slab. The slab was cast in the laboratory from the same batch of mold, and cores are cut from slab in order to estimate the actual strength. These values are used to compare with predicted value. The present study allows more realistic determination of early-age strength of concrete and can be efficiently used to control the quality in actual construction.

• Computers and Concrete
• /
• v.21 no.4
• /
• pp.407-417
• /
• 2018

Concrete which is a composite material is one of the most important construction materials. Compressive strength is a commonly used parameter for the assessment of concrete quality. Accurate prediction of concrete compressive strength is an important issue. In this study, we utilized an experimental procedure for the assessment of concrete quality. Firstly, the concrete mix was prepared according to C 20 type concrete, and slump of fresh concrete was about 20 cm. After the placement of fresh concrete to formworks, compaction was achieved using a vibrating screed. After 28 day period, a total of 100 core samples having 75 mm diameter were extracted. On the core samples pulse velocity determination tests and compressive strength tests were performed. Besides, Windsor probe penetration tests and Schmidt hammer tests were also performed. After setting up the data set, twelve artificial intelligence (AI) models compared for predicting the concrete compressive strength. These models can be divided into three categories (i) Functions (i.e., Linear Regression, Simple Linear Regression, Multilayer Perceptron, Support Vector Regression), (ii) Lazy-Learning Algorithms (i.e., IBk Linear NN Search, KStar, Locally Weighted Learning) (iii) Tree-Based Learning Algorithms (i.e., Decision Stump, Model Trees Regression, Random Forest, Random Tree, Reduced Error Pruning Tree). Four evaluation processes, four validation implements (i.e., 10-fold cross validation, 5-fold cross validation, 10% split sample validation & 20% split sample validation) are used to examine the performance of predictive models. This study shows that machine learning regression techniques are promising tools for predicting compressive strength of concrete.

• Computers and Concrete
• /
• v.25 no.1
• /
• pp.83-94
• /
• 2020

The use of fly ash in modern-day concrete technology aiming sustainable constructions is on rapid rise. Fly ash, a spinoff from coal calcined thermal power plants with pozzolanic properties is used for cement replacement in concrete. Fly ash concrete is cost effective, which modifies and improves the fresh and hardened properties of concrete and additionally addresses the disposal and storage issues of fly ash. Soft computing techniques have gained attention in the civil engineering field which addresses the drawbacks of classical experimental and computational methods of determining the concrete compressive strength with varying percentages of fly ash. In this study, models based on soft computing techniques employed for the prediction of the compressive strengths of fly ash concrete are collected from literature. They are classified in a categorical way of concrete strengths such as control concrete, high strength concrete, high performance concrete, self-compacting concrete, and other concretes pertaining to the soft computing techniques usage. The performance of models in terms of statistical measures such as mean square error, root mean square error, coefficient of correlation, etc. has shown that soft computing techniques have potential applications for predicting the fly ash concrete compressive strengths.

• Structural Engineering and Mechanics
• /
• v.41 no.3
• /
• pp.407-420
• /
• 2012

This study investigated possible ways to replace conventional stirrups used on high-strength concrete members with improved reinforcing materials. Headed bar and high-strength steel were chosen to substitute for conventional stirrups, and an experimental comparison between the shear behavior of high-strength concrete large beams reinforced with conventional stirrups and the chosen stirrup substitutes was made. Test results indicated that the headed bar and the high-strength steel led to a significant reserve of shear strength and a good redistribution of shear between stirrups after shear cracking. This is due to the headed bar providing excellent end anchorage and the high-strength steel successfully resisting higher and sudden shear transmission from the concrete to the shear reinforcement. Experimental results presented in this paper were also compared with various prediction models for shear strength of concrete members.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2002.05a
• /
• pp.897-902
• /
• 2002

The failure of flat plate connection is successive failure process accompanying with stress redistribution, hence it is necessary to compute the contributions of each resistance components at ultimate state. In the present study, the interactions of resultant forces at each faces of connection, i.e. shear, bending moment and torsional moment are considered in the assessment of strength of slab. As a result the strength prediction model for connection is made up as combination of bending resistance, shear resistance and torsional resistance. The proposed method is verified by the experimental data and numerical data of continuous slabs.