• Journal of the Korea Concrete Institute
• /
• v.14 no.4
• /
• pp.457-466
• /
• 2002

An artificial neural network was applied to predict compressive strength, slump value and mix proportion of a concrete. Standard mixed tables were trained and estimated, and the results were compared with those of the experiments. To consider variabilities of material properties, the standard mixed fables from two companies of Ready Mixed Concrete were used. And they were trained with the neural network. In this paper, standard back propagation network was used. The mix proportion factors such as water cement ratio, sand aggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse aggregate and air entraining admixture were used. For the arrangement on the approval of prediction of mix proportion factor, the standard compressive strength of $180kgf/cm^2{\sim}300kgf/cm^2$, and target slump value of 8 cm, 15 cm were used. For the arrangement on the approval of prediction of compressive strength and slump value, the standard compressive strength of $210kgf/cm^2{\sim}240kgf/cm^2$, and target slump value of 12 cm and 15 cm wore used because these ranges are most frequently used. In results, in the prediction of mix proportion factor, for all of the water cement ratio, sand aggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse aggregate, air entraining admixture, the predicted values and the values of standard mixed tables were almost the same within the target error of 0.10 and 0.05, regardless of two companies. And in the prediction of compressive strength and slump value, the predicted values were converged well to the values of standard mixed fables within the target error of 0.10, 0.05, 0.001. Finally artificial neural network is successfully applied to the prediction of concrete mixture and compressive strength.

• Proceedings of the Korea Concrete Institute Conference
• /
• 1998.04b
• /
• pp.463-468
• /
• 1998

This paper presents the shear behavior in reinforced normal and high-strength concrete beams with Belite cement due to the increase of concrete compressive strength. The shear tests were conducted on thirty two beam specimens having concrete compressive strengths of 350 and 600kg/$\textrm{cm}^2$. The major experimental variables are compressive strength of concrete, shear span to depth ratio, and shear reinforcement ratio. The shear responses as to each variable are discussed in terms of shear capacity. The comparison of prediction equations with test results is also presented.

• Journal of the Korea Institute of Building Construction
• /
• v.19 no.6
• /
• pp.503-510
• /
• 2019

Recently, construction technology of RC structures must be examined for creep in concrete. The factors affecting the creep prediction of concrete and the results of creep in domestic construction field were reviewed. The longer the creep test period and the higher the compressive strength, the higher the creep prediction accuracy. The higher the curing temperature, the higher the initial strength development of the concrete, but the difference in the creep coefficients increased over time. Based on the results of creep evaluation in the domestic construction field and lab. tests, a modified predictive model that complements the ACI-209 model was proposed. In the creep prediction of real members using general to high strength concrete, the test period and temperature should be considered precisely.

• Computers and Concrete
• /
• v.32 no.2
• /
• pp.149-163
• /
• 2023

One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R²). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R²=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.

• Smart Structures and Systems
• /
• v.30 no.2
• /
• pp.195-207
• /
• 2022

Computational drawbacks associated with regular predictive models have motivated engineers to use hybrid techniques in dealing with complex engineering tasks like simulating the compressive strength of concrete (CSC). This study evaluates the efficiency of tree potential metaheuristic schemes, namely shuffled complex evolution (SCE), multi-verse optimizer (MVO), and beetle antennae search (BAS) for optimizing the performance of a multi-layer perceptron (MLP) system. The models are fed by the information of 1030 concrete specimens (where the amount of cement, blast furnace slag (BFS), fly ash (FA1), water, superplasticizer (SP), coarse aggregate (CA), and fine aggregate (FA2) are taken as independent factors). The results of the ensembles are compared to unreinforced MLP to examine improvements resulted from the incorporation of the SCE, MVO, and BAS. It was shown that these algorithms can considerably enhance the training and prediction accuracy of the MLP. Overall, the proposed models are capable of presenting an early, inexpensive, and reliable prediction of the CSC. Due to the higher accuracy of the BAS-based model, a predictive formula is extracted from this algorithm.

• Composites Research
• /
• v.17 no.5
• /
• pp.39-46
• /
• 2004

In this study, the strength of spatially reinforced composites (SRC) are predicted by using stiffness reduction for each structural element composed of a rod stiffness in each direction and a matrix stiffness proportional to its rod volume fraction. Maximum failure strain criteria is applied to rod failure, and modified Tsai-Wu failure criteria to matrix failure. The material properties composed of the tensile failure strain of a rod, the compressive failure strain of 3D SRC, the tensile and compressive strength of the 3D SRC in the $45^{\cir}$ rotated direction from a rod and the shear strength of the 3D SRC are measured to predict the SRC strength. The strength distributions of the 3D/4D SRC in rod and off-rod direction have the largest and the smallest values, respectively. A variable load step is selected to increase an efficiency of strength distribution calculation. Uniform load step is applied when a load history is needed. The results of compressive strength from analysis and experiment show the 18 % difference though the initial slop is coincident with each other.

• Advances in concrete construction
• /
• v.13 no.1
• /
• pp.11-23
• /
• 2022

Conventional concrete needs some improvement in the mechanical properties, which can be obtained by different admixtures. However, making concrete samples costume always time and money. In this paper, different types of hybrid algorithms are applied to develop predictive models for forecasting compressive strength (CS) of concretes containing metakaolin (MK) and fly ash (FA). In this regard, three different algorithms have been used, namely multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVR), to predict CS of concretes by considering most influencers input variables. These algorithms integrated with the grey wolf optimization (GWO) algorithm to increase the model's accuracy in predicting (GWMLP, GWRBF, and GWSVR). The proposed MLP models were implemented and evaluated in three different layers, wherein each layer, GWO, fitted the best neuron number of the hidden layer. Correspondingly, the key parameters of the SVR model are identified using the GWO method. Also, the optimization algorithm determines the hidden neurons' number and the spread value to set the RBF structure. The results show that the developed models all provide accurate predictions of the CS of concrete incorporating MK and FA with R² larger than 0.9972 and 0.9976 in the learning and testing stage, respectively. Regarding GWMLP models, the GWMLP1 model outperforms other GWMLP networks. All in all, GWSVR has the worst performance with the lowest indices, while the highest score belongs to GWRBF.

• Steel and Composite Structures
• /
• v.29 no.5
• /
• pp.659-667
• /
• 2018

This paper deals with the experimental study on strength the strength and deformation characteristics of short circular Concrete Filled Steel Tube (CFST) columns. Effect of vertical stiffeners on the behavior of the column is studied under axial compressive loading. Intermittently welded vertical stiffeners are used to strengthen the tubes. Stiffeners are attached to the inner surface of tube by welding through pre drilled holes on the tube. The variable of the study is the spacing of the weld between stiffeners and circular tube. A total of 5 specimens with different weld spacing (60 mm, 75 mm, 100 mm, 150 mm and 350 mm) were prepared and tested. Short CFST columns of height 350 mm, outer tube diameter of 165 mm and thickness of 4.5 mm were used in the study. Concrete of cube compressive strength $41N/mm^2$ and steel tubes with yield strength $310N/mm^2$ are adopted. The test results indicate that the strength and deformation of the circular CFST column is found to be significantly influenced by the weld spacing. The ultimate axial load carrying capacity was found to increase by 11% when the spacing of weld is reduced from 350 mm to 60 mm. The vertical stiffeners are found to effective in enhancing the initial stiffness and ductility of CFST columns. The prediction models were developed for strength and deformation of CFST columns. The prediction is found to be in good agreement with the corresponding test data.

• Advances in concrete construction
• /
• v.6 no.1
• /
• pp.87-102
• /
• 2018

Utilization of waste tires may be considered as one of the solution to the problems faced by the local authorities in disposing them. Reclaimed rubber (RR) is being used in concrete for replacing conventional aggregates. This research work is focused on the strength prediction of reclaimed rubber concrete using a Genetic Algorithm (GA) for M40 grade of concrete and comparing it with experimental results. 1000 sets were taken and 100 iterations were run during training of GA models. A base study has been carried out in this research work partially replacing cement with three types of fillers such as Plaster of Paris (POP), Fly Ash (FA) and Silica Fume (SF). A total of 243 cubes were cast and tested for compression using a Universal Testing Machine. It was found that SF produced maximum strength in concrete and was used in the main study with reclaimed rubber. Tests were conducted on 81 cube samples with a combination of optimum SF percent and various proportions of RR replacing coarse aggregates in concrete mix. Compressive strength tests of concrete at 7, 14 and 28 days reveal that the maximum strength is obtained at 12 percent replacement of cement and 9 percent replacement of coarse aggregates respectively. Moreover the GA results were found to be in line with the experimental results obtained.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2004.05a
• /
• pp.834-837
• /
• 2004

In this study, the electric accelerated reinforcing bar corrosion test was carried out to estimate the coefficient of electrolytic corrosion based on the concept of Faraday's law according to rebar corrosion rate and concrete compressive strength which had an effect on the actual corrosion mass loss. The results of this paper allow the prediction of corrosion amount in the electric accelerated reinforcing bar corrosion test method.