• Journal of the Korea Concrete Institute
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• v.19 no.3
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• pp.253-263
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• 2007

Previous researches showed that confined concrete with Fiber-Reinforced Plastic (FRP) sheets significantly improves the strength and ductility of concrete compared with unconfined concrete. However, the retrofit design of concrete with FRP materials requires an accurate estimate of the performance enhancement due to the confinement mechanism. The object of this research is to predict the compressive strength and strain of concrete confined with FRP wraps. For the purpose of this research, 102 test specimens were fabricated and loaded statically under uniaxial compression. Axial load, axial and lateral strains were investigated to predict the ultimate stress and strain. Also, to achieve reliability of proposed strength and strain models for FRP-confined concrete, another series of uniaxial compression test results were used. This paper presents strength and strain models for FRP-confined concrete. The proposed models to estimate the ultimate stresses and failure strains produce satisfactory predictions as compared to current design equations. In conclusion, it is proposed that the modified stress-strain model of concrete cylinders could be effectively used for the repair and retrofit of concrete columns.

• Computers and Concrete
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• v.21 no.5
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• pp.513-523
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• 2018

Time dependent strain due to creep is a significant factor in structural design. Multi-gene genetic programming (MGGP) and artificial neural network (ANN) are used to develop two models for prediction of creep compliance in concrete. The first model was developed by MGGP technique and the second model by hybridized MGGP-ANN. In the MGGP-ANN, the ANN is working in parallel with MGGP to predict errors in MGGP model. A total of 187 experimental data sets that contain 4242 data points are filtered from the NU-ITI database. These data are used in developing the MGGP and MGGP-ANN models. These models contain six input variables which are: average compressive strength at 28 days, relative humidity, volume to surface ratio, cement type, age at start of loading and age at the creep measurement. Practical equation based on MGGP was developed. A parametric study carried out with a group of hypothetical data generated among the range of data used to check the generalization ability of MGGP and MGGP-ANN models. To confirm validity of MGGP and MGGP-ANN models; two creep prediction code models (ACI209 and CEB), two empirical models (B3 and GL 2000) are used to compare their results with NU-ITI database.

• 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.

• Smart Structures and Systems
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• v.33 no.1
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• pp.65-91
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• 2024

The use of five optimization techniques for the prediction of a strength-based concrete mixture's best-fit model is examined in this work. Five optimization techniques are utilized for this purpose: Slime Mold Algorithm (SMA), Black Hole Algorithm (BHA), Multi-Verse Optimizer (MVO), Vortex Search (VS), and Whale Optimization Algorithm (WOA). MATLAB employs a hybrid learning strategy to train an artificial neural network that combines least square estimation with backpropagation. Thus, 72 samples are utilized as training datasets and 31 as testing datasets, totaling 103. The multi-layer perceptron (MLP) is used to analyze all data, and results are verified by comparison. For training datasets in the best-fit models of SMA-MLP, BHA-MLP, MVO-MLP, VS-MLP, and WOA-MLP, the statistical indices of coefficient of determination (R²) in training phase are 0.9603, 0.9679, 0.9827, 0.9841 and 0.9770, and in testing phase are 0.9567, 0.9552, 0.9594, 0.9888 and 0.9695 respectively. In addition, the best-fit structures for training for SMA, BHA, MVO, VS, and WOA (all combined with multilayer perceptron, MLP) are achieved when the term population size was modified to 450, 500, 250, 150, and 500, respectively. Among all the suggested options, VS could offer a stronger prediction network for training MLP.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.1
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• pp.9-17
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• 2016

In this study, binary blended concrete mix with fly ash and ground granulated blast furnace slag was prepared according to 3 level of water reduction performance of chemical admixture (0%, 8% and 16%) and 3 level of water-cement ratio (40%, 45% and 50%) for evaluation of quality properties of binary blended concrete according to performance of chemical admixture. concrete mix was carried out repetition test of three times in order to secure the reliability. As a result, compressive strength according to performance of chemical admixture was found that difference of strength was about 20% occurred, chemical admixture was showed that a great influence on qualities of concrete. In addition, reflected the effect of performance of chemiacal admixture, prediction model equations for concrete compressive strength was proposed, it was found that more than 85% of the high correlation.

• Structural Engineering and Mechanics
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• v.18 no.1
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• pp.21-40
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• 2004

In this paper, we modify the L-L model (Li et al. 2003) and extend the application of this model to concrete confined by both steel reinforcement and CFRP. Thirty-six concrete cylinders with a dimension of ${\varphi}30{\times}60$ cm were tested to verify the effectiveness of the proposed model. The experimental test results show that different types of steel reinforcement have a great effect on the compressive strength of concrete cylinders confined by steel reinforcement, but the different types of steel reinforcement have very little effect on concrete cylinders confined by both steel reinforcement and CFRP. Compared with the stress-strain curves of confined concrete cylinders, we can conclude that the proposed model can provide more effective prediction than others models.

• Structural Engineering and Mechanics
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• v.73 no.5
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• pp.515-527
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• 2020

This paper presents a stress field approach for the shear capacity of stirrup-reinforced concrete beams that explicitly incorporates the contribution of principal tensile stresses in concrete. This formulation represents an extension of the variable strut inclination method adopted in the Eurocode 2. In this model, the stress fields in web concrete consist of principal compressive stresses inclined at an angle θ combined with principal tensile stresses oriented along a direction orthogonal to the former (the latter being typically neglected in other formulations). Three different failure mechanisms are identified, from which the strut inclination angle and the corresponding shear strength are determined through equilibrium principles and the static theorem of limit analysis, similar to the EC-2 approach. It is demonstrated that incorporating the contribution of principal tensile stresses of concrete slightly increases the ultimate inclination angle of the compression struts as well as the shear capacity of reinforced concrete beams. The proposed stress field approach improves the prediction of the shear strength in comparison with the Eurocode 2 model, in terms of both accuracy (mean) and precision (CoV), as demonstrated by a broad comparison with more than 200 published experimental results from the literature.

• Steel and Composite Structures
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• v.46 no.2
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• pp.153-173
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• 2023

Rectangular concrete-filled steel tubular (RCFST) column, a type of concrete-filled steel tubular (CFST), is widely used in compression members of structures because of its advantages. This paper proposes a robust machine learning-based framework for predicting the ultimate compressive strength of RCFST columns under both concentric and eccentric loading. The gradient boosting neural network (GBNN), an efficient and up-to-date ML algorithm, is utilized for developing a predictive model in the proposed framework. A total of 890 experimental data of RCFST columns, which is categorized into two datasets of concentric and eccentric compression, is carefully collected to serve as training and testing purposes. The accuracy of the proposed model is demonstrated by comparing its performance with seven state-of-the-art machine learning methods including decision tree (DT), random forest (RF), support vector machines (SVM), deep learning (DL), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and categorical gradient boosting (CatBoost). Four available design codes, including the European (EC4), American concrete institute (ACI), American institute of steel construction (AISC), and Australian/New Zealand (AS/NZS) are refereed in another comparison. The results demonstrate that the proposed GBNN method is a robust and powerful approach to obtain the ultimate strength of RCFST columns.

• Steel and Composite Structures
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• v.42 no.4
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• pp.459-475
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• 2022

In this paper a model for the prediction of the ultimate axial compressive capacity of square and rectangular Concrete Filled Steel Tubes, based on an Artificial Neural Network modeling procedure is presented. The model is trained and tested using an experimental database, compiled for this reason from the literature that amounts to 1193 specimens, including long, thin-walled and high-strength ones. The proposed model was selected as the optimum from a plethora of alternatives, employing different activation functions in the context of Artificial Neural Network technique. The performance of the developed model was compared against existing methodologies from design codes and from proposals in the literature, employing several performance indices. It was found that the proposed model achieves remarkably improved predictions of the ultimate axial load.

• Computers and Concrete
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• v.31 no.3
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• pp.253-265
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• 2023

In this research, the gene expression programming (GEP) technique was employed to provide a new model for predicting the maximum loading capacity of concrete-encased steel (CES) columns. This model was developed based on 96 CES column specimens available in the literature. The six main parameters used in the model were the compressive strength of concrete (f_c), yield stress of structural steel (f_ys), yield stress of steel rebar (f_yr), and cross-sectional areas of concrete, structural steel, and steel rebar (A_c, A_s and A_r respectively). The performance of the prediction model for the ultimate load-carrying capacity was investigated using different statistical indicators such as root mean square error (RMSE), correlation coefficient (R), mean absolute error (MAE), and relative square error (RSE), the corresponding values of which for the proposed model were 620.28, 0.99, 411.8, and 0.01, respectively. Here, the predictions of the model and those of available codes including ACI ITG, AS 3600, CSA-A23, EN 1994, JGJ 138, and NZS 3101 were compared for further model assessment. The obtained results showed that the proposed model had the highest correlation with the experimental data and the lowest error. In addition, to see if the developed model matched engineering realities and corresponded to the previously developed models, a parametric study and sensitivity analysis were carried out. The sensitivity analysis results indicated that the concrete cross-sectional area (A_c) has the greatest effect on the model, while parameter (f_yr) has a negligible effect.