• Computers and Concrete
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• v.29 no.6
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• pp.433-444
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• 2022

Concrete's compressive strength is widely studied in order to understand many qualities and the grade of the concrete mixture. Conventional civil engineering tests involve time and resources consuming laboratory operations which results in the deterioration of concrete samples. Proposing efficient non-destructive models for the prediction of concrete compressive strength will certainly yield advancements in concrete studies. In this study, the efficiency of using radial basis function neural network (RBFNN) which is not common in this field, is studied for the concrete compressive strength prediction. Complementary studies with back propagation neural network (BPNN), which is commonly used in this field, have also been carried out in order to verify the efficiency of RBFNN for compressive strength prediction. A total of 13 input parameters, including novel ones such as cement's and fly ash's compositional information, have been employed in the prediction models with RBFNN and BPNN since all these parameters are known to influence concrete strength. Three different train: test ratios were tested with both models, while different hidden neurons, epochs, and spread values were introduced to determine the optimum parameters for yielding the best prediction results. Prediction results obtained by RBFNN are observed to yield satisfactory high correlation coefficients and satisfactory low mean square error values when compared to the results in the previous studies, indicating the efficiency of the proposed model.

• Advances in nano research
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• v.11 no.6
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• pp.595-606
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• 2021

This paper conducted mainly for forecasting the behavior of the shear connectors in steel-concrete composite beams based on the different factors. The main goal was to analyze the influence of variable parameters on the shear strength of C-shaped and L-shaped angle shear connectors. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data in order to select the most influential factors for the mentioned shear strength forecasting. Five inputs are considered: height, length, thickness of shear connectors together with concrete strength and respective slip of the shear connectors after testing. The ANFIS process for variable selection was also implemented in order to detect the predominant factors affecting the forecasting of the shear strength of C-shaped and L-shaped angle shear connectors. The results show that the forecasting methodology developed in this research is useful for enhancing the multiple performances characterizing in the shear strength prediction of C and L shaped angle shear connectors analyzing.

• Structural Engineering and Mechanics
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• v.12 no.5
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• pp.459-474
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• 2001

The complete moment-curvature curves of doubly reinforced concrete beams made of normal- or high-strength concrete have been evaluated using a newly developed analytical method that takes into account the stress-path dependence of the constitutive properties of the materials. From the moment-curvature curves and the strain distribution results obtained, the post-peak behavior and flexural ductility of doubly reinforced normal- and high-strength concrete beam sections are studied. It is found that the major factors affecting the flexural ductility of reinforced concrete beam sections are the tension steel ratio, compression steel ratio and concrete grade. Generally, the flexural ductility decreases as the amount of tension reinforcement increases, but increases as the amount of compression reinforcement increases. However, the effect of the concrete grade on flexural ductility is fairly complicated, as will be explained in the paper. Quantitative analysis of such effects has been carried out and a formula for direct evaluation of the flexural ductility of doubly reinforced concrete sections developed. The formula should be useful for the ductility design of doubly reinforced normal- and high-strength concrete beams.

• Advances in concrete construction
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• v.9 no.4
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• pp.367-374
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• 2020

Concrete surfaces, industrial floors, sidewalks, roads and parking lots are typically subjected to abrasions. Many studies indicated that the abrasion resistance is directly related to the ultimate strength of the cured concrete. Chemical reactions, freeze-thaw cycles, and damages under abrasion are among many factors negatively affecting the concrete strength and durability. One of the major solutions to address the abrasive resistance of the concrete is to use fibers. Fibers are used in the concrete mix to improve the mechanical properties, strength and limit the crack propagations. In this study, implementation of the steel fibers in concrete to enhance the abrasive resistance of the concrete is investigated in details. The abrasive resistance of the concrete with and without steel fibers is studied with the sandblasting technique. For this purpose, different concrete samples are made with various hooked steel fiber ratios and investigated with the sandblasting method for two different strike angles. In total, 144 ASTM verified cube samples are investigated and it is shown that those samples with the highest steel fiber ratios have the highest abrasive resistance. In addition, the experiments determine that there is a meaningful correlation between the steel fiber percentage in the mix, strike angle and curing time which could be considered for improving structural behavior of the fiber-reinforced concrete.

• International Journal of Concrete Structures and Materials
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• v.6 no.2
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• pp.87-100
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• 2012

The process of selecting a repair material is a typical one of multi-criteria decision-making (MCDM) problems. In this study Analytical Hierarch Process was applied to solve this MCDM problem. Many factors affecting a process to select an optimal repair material can be classified into quantitative and qualitative requirements and this study handled only quantitative items. Quantitative requirements in the optimal selection model for repair material were divided into two parts, namely, the required chemical performance and the required physical performance. The former is composed of alkali-resistance, chloride permeability and electrical resistivity. The latter is composed of compressive strength, tensile strength, adhesive strength, drying shrinkage, elasticity and thermal expansion. The result of the study shows that this method is the useful and rational engineering approach in the problem concerning the selection of one out of many candidate repair materials even if this study was limited to repair material only for chloride-deteriorated concrete.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05b
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• pp.521-524
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• 2006

In this paper, admixture factors affecting the properties of lightweight foamed concrete incorporating cement kiln dust(CKD) and fly ash(FA), respectively are discussed. Increase in CKD contents resulted in loss of fluidity and decrease in settlement of concrete noticeably. Moreover, the higher the unit weight is, the smaller the settlement depth is. The use of CKD resulted in slight decrease in compressive strength and tensile strength compared to that with other admixture. However, all mixtures met the requirement of strength prescribed in Korean Industrial Standards.

• International Journal of Concrete Structures and Materials
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• v.8 no.2
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• pp.99-116
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• 2014

The American Concrete Institute (ACI) 318-11 permits the use of the moment magnifier method for computing the design ultimate strength of slender reinforced concrete columns that are part of braced frames. This computed strength is influenced by the column effective length factor K, the equivalent uniform bending moment diagram factor $C_m$ and the effective flexural stiffness EI among other factors. For this study, 2,960 simple braced frames subjected to short-term loads were simulated to investigate the effect of using different methods of calculating the effective length factor K when computing the strength of columns in these frames. The theoretically computed column ultimate strengths were compared to the ultimate strengths of the same columns computed from the ACI moment magnifier method using different combinations of equations for K and EI. This study shows that for computing the column ultimate strength, the current practice of using the Jackson-Moreland Alignment Chart is the most accurate method for determining the effective length factor. The study also shows that for computing the column ultimate strength, the accuracy of the moment magnifier method can be further improved by replacing the current ACI equation for EI with a nonlinear equation for EI that includes variables affecting the column stiffness and proposed in an earlier investigation.

• Journal of Korean Association for Spatial Structures
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• v.23 no.4
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.3
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• pp.149-160
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• 2015

This study reports the interfacial bond strength between Ultra High Performance Concrete (UHPC) and Normal Strength Concrete (NSC). While previous studies have focused on the interfacial strength between NSC substrate and UHPC overlay, this study use precast UHPC for enhanced constructability and replacement of formwork. The factors affecting the interface strength are comprehensively reviewed. It can be classified into: interface shape, degree of hardening and moisture condition of UHPC before combining with NSC, and curing condition of composite materials. Conducted experiments verify the effects of each factor on the interface strength and, accordingly show different failure modes. In particular, a new failure mode of the failure of a part of UHPC was firstly found in the case of sample with rough interface between UHPC and NSC. The other factors of the degree of hardening and the moisture and curing conditions of UHPC were discussed. This research will provide a valuable foundation to utilize the UHPC as a composite material.

• Structural Engineering and Mechanics
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• v.76 no.3
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• pp.337-354
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• 2020

Reinforced concrete beam-column (RCBC) joints of laterally loaded unbraced frames are sometimes controlled by their shear behavior. This behavior relies on multiple and interdependent complex mechanisms. There are already several studies on the influence of some parameters on the shear strength of reinforced concrete joints. However, there are no studies methodically tackling all the most relevant parameters and quantifying their influence on the overall joint behavior, not just on its shear strength. Hence, considering the prohibitive cost of a comprehensive parametric experimental investigation, a nonlinear finite element analysis (NLFEA) was undertaken to identify the key factors affecting the shear behavior of such joints and quantify their influence. The paper presents and discusses the models employed in this NLFEA and the procedure used to deduce the joint behavior from the NLFEA results. Three alternative, or complementary, quantities related to shear are considered when comparing results, namely, the maximum shear stress supported by the joint, the secant shear stiffness at maximum shear stress and the secant shear stiffness in service conditions. Depending on which of these is considered, the lower or higher the relevance of each of the six parameters investigated: transverse reinforcement in the joint, intermediate longitudinal bars and diagonal bars in the column, concrete strength, column axial load and confining elements in transverse direction.