• Smart Structures and Systems
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• v.29 no.3
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• pp.433-444
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• 2022

Concrete is a most utilized material in the construction industry that have main components. The strength of concrete can be improved by adding some admixtures. Evaluating the impact of fly ash (FA) and silica fume (SF) on the long-term compressive strength (CS) of concrete provokes to find the significant parameters in predicting the CS, which could be useful in the practical works and would be extensible in the future analysis. In this study, to evaluate the effective parameters in predicting the CS of concrete containing admixtures in the long-term and present a fitted equation, the multivariate adaptive regression splines (MARS) method has been used, which could find a relationship between independent and dependent variables. Next, for optimizing the output equation, biogeography-based optimization (BBO), particle swarm optimization (PSO), and hybrid PSOBBO methods have been utilized to find the most optimal conclusions. It could be concluded that for CS predictions in the long-term, all proposed models have the coefficient of determination (R²) larger than 0.9243. Furthermore, MARS-PSOBBO could be offered as the best model to predict CS between three hybrid algorithms accurately.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.04a
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• pp.635-638
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• 2000

This experimental were carried out in order to investigate the equivalent strength of SFRC (Steel Fiber Reinforced Concrete). Tow hundred and tenh SFRC beeam (size: 150x150x550) were used in the tests the relationships between loading and mid-point deflections of the beams were observed four oint bending loading. From the test results, prediction formulas for the equivalent strength of SFRC beams are suggested.

• Proceedings of the Korea Concrete Institute Conference
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• 2010.05a
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• pp.443-444
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• 2010

In this study, It is confirmed the validity for determination of the form removal time, utilizing prediction expression for early-stage strength which depended on the variation of curing history for early strength concrete which be passible to construction work period reduction, in apartment.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2004.05a
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• pp.101-104
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• 2004

If predicting of compressive strength of construction in construction field at early age is possibile, rational strength management & schedule plan is possible. With method for predicting strength of concrete, many researchers have been making study of maturity method. On the other hand, nowadays rationalization of construction capacity and reduction of a term of works due to improvement of construction capacity and application of a new method of construction is gathering strength with important issue. In accordance with this present condition, construction is being progressed in winter, but proper construction mothed and countermeasure for strength management is not established in case of winter construction. Therefore to investigate application in construction field at winter of strength prediction model that developed at former study, this study aim to measure application of developed strength prediction model through manufacture of mock-up concrete according to kind of strength level at 5$^{\circ}C$.

• Computers and Concrete
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• v.11 no.6
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• pp.603-620
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• 2013

This paper improves genetic programming (GP) and weight genetic programming (WGP) and proposes soft-computing polynomials (SCP) for accurate prediction and visible polynomials. The proposed genetic programming system (GPS) comprises GP, WGP and SCP. To represent confined compressive strength and strain of circular concrete columns in meaningful representations, this paper conducts sensitivity analysis and applies pruning techniques. Analytical results demonstrate that all proposed models perform well in achieving good accuracy and visible formulas; notably, SCP can model problems in polynomial forms. Finally, concrete compressive strength and lateral steel ratio are identified as important to both confined compressive strength and strain of circular concrete columns. By using the suggested formulas, calculations are more accurate than those of analytical models. Moreover, a formula is applied for confined compressive strength based on current data and achieves accuracy comparable to that of neural networks.

• Computers and Concrete
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• v.28 no.6
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• pp.621-634
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• 2021

For the reliable strut-and-tie model (STM) design of disturbed regions of concrete members, structural designers must accurately determine the strength of concrete struts to check the strength conditions of a selected STM el and the anchorage of reinforcing bars in nodal zones. In this study, the author proposed a consistent numerical method for strut strength, applicable to all two-dimensional STMs. The proposed method includes the effects of a biaxial stress state associated with tensile strains in reinforcing bars crossing a strut, deviation angle between strut orientation and compressive principal stress flow, and degree of confinement provided by reinforcement. The author examined the method's validity through the STM prediction of the ultimate strengths of 517 reinforced concrete (RC) deep beams, 24 RC panels, and 258 RC corbels, all tested to failure.

• International Journal of Concrete Structures and Materials
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• v.19 no.1E
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• pp.11-16
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• 2007

Recently, probabilistic neural network (PNN) has been proposed to predict the compressive strength of concrete for the known effect of improvement on PNN by the iteration method. However, an empirical method has been incorporated in the PNN technique to specify its smoothing parameter, which causes significant uncertainty in predicting the compressive strength of concrete. In this study, a modified probabilistic neural network (MPNN) approach is hence proposed. The global probability density function (PDF) of variables is reflected by summing the heterogeneous local PDFs which are automatically determined by the individual standard deviation of each variable. The proposed MPNN is applied to predict the compressive strength of concrete using actual test data from a concrete company. The estimated results of MPNN are compared with those of the conventional PNN. MPNN showed better results than the conventional PNN in predicting the compressive strength of concrete and provided promising results for the probabilistic approach to predict the concrete strength by using the individual standard deviation of a variable.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.05a
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• pp.591-596
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• 2001

Nine steel fiber reinforced high strength concrete beams and three steel fiber reinforced normal strength concrete beams without stirrups were tested by two point load. The variables studied in this investigation are the shear span/depth ratios of a/d = 2, 3 and 4, steel fiber volume fractions of V$_{f}$ : 0, 0.5% and 0.75% and concrete compressive strengths of f$_{ck}$: 630kgf/$cm^{2}$, and 310kgf/$cm^{2}$. Based on these tests and on tests by previous investigators, predictive equation is proposed for evaluating the ultimate shear strength of steel fiber reinforced concrete beams without stirrups. The proposed equation gave good prediction for the ultimate shear strength of the tested beams.

• Journal of the Korea Concrete Institute
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• v.23 no.6
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• pp.741-747
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• 2011

In this study, autogenous shrinkage strain and prediction models of concrete specimens were compared with strength level and development rate. Also, concrete autogeneous shrinkage under various curing conditions was investigated. The results showed that autogeneous shrinkage increased as concrete strength increased. However, when the concrete strength was almost identical, the initial autogeneous shrinkage of OPC was larger than BFS, but the final autogeneous shrinkage of BFS was larger than OPC. Early wet curing reduced autogeneous shrinkage strain. Especially, when the early wet curing was applied for more than 24 hours, final autogeneous shrinkage was significantly reduced. The results showed that the existing EC2 models do not reflect concrete properties properly. Therefore, the revised model was proposed to better predict autogeneous shrinkage.

• Computers and Concrete
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• v.22 no.4
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• pp.355-363
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• 2018

In the following paper, a socio-political heuristic search approach, named the imperialist competitive algorithm (ICA) has been used to improve the efficiency of the multi-layer perceptron artificial neural network (ANN) for predicting the compressive strength of concrete. 173 concrete samples have been investigated. For this purpose the values of slump flow, the weight of aggregate and cement, the maximum size of aggregate and the water-cement ratio have been used as the inputs. The compressive strength of concrete has been used as the output in the hybrid ICA-ANN model. Results have been compared with the multiple-linear regression model (MLR), the genetic algorithm (GA) and particle swarm optimization (PSO). The results indicate the superiority and high accuracy of the hybrid ICA-ANN model in predicting the compressive strength of concrete when compared to the other methods.