• Structural Engineering and Mechanics
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• v.16 no.1
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• pp.83-99
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• 2003

The aim of this paper is to develop the ISCOSTFUN (Intelligent System for Prediction of Concrete Strength by Functional Networks) in order to provide in-place strength information of the concrete to facilitate concrete from removal and scheduling for construction. For this purpose, the system is developed using Functional Network (FN) by learning functions instead of weights as in Artificial Neural Networks (ANN). In serial functional network, the functions are trained from enough input-output data and the input for one functional network is the output of the other functional network. Using ISCOSTFUN it is possible to predict early strength as well as 7-day and 28-day strength of concrete. Altogether seven functional networks are used for prediction of strength development. This study shows that ISCOSTFUN using functional network is very efficient for predicting the compressive strength development of concrete and it takes less computer time as compared to well known Back Propagation Neural Network (BPN).

• Smart Structures and Systems
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• v.25 no.2
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• pp.183-195
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• 2020

Mineral admixtures have been widely used to produce concrete. Pozzolans have been utilized as partially replacement for Portland cement or blended cement in concrete based on the materials' properties and the concrete's desired effects. Several environmental problems associated with producing cement have led to partial replacement of cement with other pozzolans. Furnace slag and fly ash are two of the pozzolans which can be appropriately used as partial replacements for cement in concrete. However, replacing cement with these materials results in significant changes in the mechanical properties of concrete, more specifically, compressive strength. This paper aims to intelligently predict the compressive strength of concretes incorporating furnace slag and fly ash as partial replacements for cement. For this purpose, a database containing 1030 data sets with nine inputs (concrete mix design and age of concrete) and one output (the compressive strength) was collected. Instead of absolute values of inputs, their proportions were used. A hybrid artificial neural network-genetic algorithm (ANN-GA) was employed as a novel approach to conducting the study. The performance of the ANN-GA model is evaluated by another artificial neural network (ANN), which was developed and tuned via a conventional backpropagation (BP) algorithm. Results showed that not only an ANN-GA model can be developed and appropriately used for the compressive strength prediction of concrete but also it can lead to superior results in comparison with an ANN-BP model.

• Magazine of the Korea Concrete Institute
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• v.11 no.2
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• pp.95-103
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• 1999

The reduction phenomena of concrete compressive strength with the size of specimens have been extensively investigated, but till now the adequate analysis technique is not fixed. The existing research results show that the bigger the member size, the smaller the strength. This means the nonlinear fracture mechanics theory is needed in order to analyze the fracture behaviors of concrete and the size effect. There is a few model equations that is to predict the size effect of compressive strength of standard and non-standard cylindrical specimen. However, theses equations did not considered the difference of fracturing mechanism which depends on the strength level. In this paper, model equations to predict compressive strength of concrete considering the size effect and strength level are suggested. The size effect model suggested in this paper shows good prediction compared with the existing test data of various concrete size and strength level.

• Journal of the Korea Institute of Building Construction
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• v.4 no.4
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• pp.63-70
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• 2004

The objective of this study is to evaluate the compressive strength of recycled aggregate concrete by the non-destructive testing. Main experimental variables were the replacement level of recycled aggregate and blast-furnace slag, which were divided into two series according to recycled aggregate maximum size. Test results showed that a recycled aggregate had a significant influence on the non-destructive testing results, such as rebound number, Ultrasonic pulse velocity, and frequency. A prediction model of compressive strength considering the replacement level of recycled aggregate was suggested by multi-regression analysis and was compared with test results.

• Computers and Concrete
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• v.20 no.6
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• pp.627-634
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• 2017

Predicting the compressive strength of concrete is important to assess the load-carrying capacity of a structure. However, the use of blended cements to accrue the technical, economic and environmental benefits has increased the complexity of prediction models. Artificial Neural Networks (ANNs) have been used for predicting the compressive strength of ordinary Portland cement concrete, i.e., concrete produced without the addition of supplementary cementing materials. In this study, models to predict the compressive strength of blended cement concrete prepared with a natural pozzolan were developed using regression models and single- and 2-phase learning ANNs. Back-propagation (BP), Levenberg-Marquardt (LM) and Conjugate Gradient Descent (CGD) methods were used for training the ANNs. A 2-phase learning algorithm is proposed for the first time in this study for predictive modeling of the compressive strength of blended cement concrete. The output of these predictive models indicates that the use of a 2-phase learning algorithm will provide better results than the linear regression model or the traditional single-phase ANN models.

• Computers and Concrete
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• v.15 no.1
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• pp.89-101
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• 2015

In this paper, Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) models were discussed to determine the compressive strength of clinker mortars cured for 1, 2, 7 and 28 days. In the experimental stage, 1288 mortar samples were produced from 322 different clinker specimens and compressive strength tests were performed on these samples. Chemical properties of the clinker samples were also determined. In the modeling stage, these experimental results were used to construct the models. In the models tricalcium silicate ($C_3S$), dicalcium silicate ($C_2S$), tricalcium aluminate ($C_3A$), tetracalcium alumina ferrite ($C_4AF$), blaine values, specific gravity and age of samples were used as inputs and the compressive strength of clinker samples was used as output. The approximate reasoning ability of the models compared using some statistical parameters. As a result, ANN has shown satisfying relation with experimental results and suggests an alternative approach to evaluate compressive strength estimation of clinker mortars using related inputs. Furthermore MLR model showed a poor ability to predict.

• Computers and Concrete
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• v.11 no.2
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• pp.149-167
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• 2013

The complete stress-strain behavior of steel fiber reinforced concrete in compression is needed for the analysis and design of structures. An experimental investigation was carried out to generate the complete stress-strain curve of high-performance steel fiber reinforced concrete (HPSFRC) with a strength range of 52-80 MPa. The variation in concrete strength was achieved by varying the water-to-cementitious materials ratio of 0.40-0.25 and steel fiber content (Vf = 0.5, 1.0 and 1.5% with l/d = 80 and 55) in terms of fiber reinforcing parameter, at 10% silica fume replacement. The effects of these parameters on the shape of stress-strain curves are presented. Based on the test data, a simple model is proposed to generate the complete stress-strain relationship for HPSFRC. The proposed model has been found to give good correlation with the stress-strain curves generated experimentally. Inclusion of fibers into HPC improved the ductility considerably. Equations to quantify the effect of fibers on compressive strength, strain at peak stress and toughness of concrete in terms of fiber reinforcing index are also proposed, which predicted the test data quite accurately. Compressive strength prediction model was validated with the strength data of earlier researchers with an absolute variation of 2.1%.

• Earthquakes and Structures
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• v.8 no.5
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• pp.1171-1190
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• 2015

Enhancement of strength and ductility is the main reason for the extensive use of FRP jackets to provide external confinement to reinforced concrete columns especially in seismic areas. Therefore, numerous researches have been carried out in order to provide a better description of the behavior of FRP-confined concrete for practical design purposes. This study presents a new approach to obtain strength enhancement of CFRP (carbon fiber reinforced polymer) confined concrete cylinders by applying artificial neural networks (ANNs). The proposed ANN model is based on experimental results collected from literature. It represents the ultimate strength of concrete cylinders after CFRP confinement which is also given in explicit form in terms of geometrical and mechanical parameters. The accuracy of the proposed ANN model is quite satisfactory when compared to experimental results. Moreover, the results of the proposed ANN model are compared with five important theoretical models proposed by researchers so far and considered to be in good agreement.

• Journal of the Korea Concrete Institute
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• v.18 no.3 s.93
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• pp.361-370
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• 2006

The results of previous tests by many researchers have been compiled to evaluate the flexural strength of steel-fiber reinforced concrete beams. Existing prediction equations for flexural strength of such beams were examined, and a new equation based on mechanical and empirical observations, was proposed. In other words, the constitutive models for steel fiber reinforced concrete(SFRC) were proposed, which incorporate compressive and tensile strength. A steel model might also exhibit stain-hardening characteristics. Predictions based on the model are compared with the experimental data. For the collection of tests, a variation of the Henager equations, modified to apply to fiber-reinforced concrete beams, provided reliable estimates of flexural strength. The proposed equations accounted for the influence of fiber-volume fraction, fiber aspect ratio, concrete compressive strength and flexural steel reinforcement ratio. The proposed equations gave a good estimation for 129 flexural specimens evaluated.

• Proceedings of the Korea Concrete Institute Conference
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• 1992.10a
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• pp.111-118
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• 1992

The mechanical behaviors related to the strain rate effect and the tensile strength of high-strength concrete were investigated in this study. For this purpose, concrete cylinder specimens with 4 different compressive strengths from 232kg/$\textrm{cm}^2$ to 1113kgf/$\textrm{cm}^2$ were tested and analysed on the mechanical properties(stress-strain relationship, compressive, modulus of elasticity, strain at peak compressive stress). From this experimental and analytical study, it seems that the current prediction model(ACI) for modulus of rupture need to be refined. Therefore, more refined equations for evaluation tensile strength of concrete are proposed.