• Computers and Concrete
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• v.16 no.2
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• pp.245-260
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• 2015

In this study, nominal moment-axial load interaction diagrams, moment-curvature relationships, and ductility of rectangular hybrid beam-column concrete sections are analyzed using the modified Hognestad concrete model. The hybrid columns are primarily reinforced with steel bars with additional Glass Fiber Reinforced Polymer (GFRP) control bars. Parameters investigated include amount, pattern, location, and material properties of concrete, steel, and GFRP. The study was implemented using a user defined comprehensive $MATLAB^{(R)}$ simulation model to find an efficient hybrid section design maximizing strength and ductility. Generating lower bond stresses than steel bars at the concrete interface, auxiliary GFRP bars minimize damage in the concrete core of beam-column sections. Their usage prevents excessive yielding of the core longitudinal bars during frequent moderate cyclic deformations, which leads to significant damage in the foundations of bridges or beam-column spliced sections where repair is difficult and expensive. Analytical results from this study shows that hybrid steel-GFRP composite concrete sections where GFRP is used as auxiliary bars show adequate ductility with a significant increase in strength. Results also compare different design parameters reaching a number of design recommendations for the proposed hybrid section.

• Computers and Concrete
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• v.22 no.6
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• pp.573-592
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• 2018

An analytical methodology for the calculation of the flexural and the shear capacity of concrete members with Fibre-Reinforced-Polymer (FRP) bars as tensional reinforcement is proposed. The flexural analysis is initially based on the design provisions of ACI 440.1R-15 which have properly been modified to develop general charts that simplify computations and provide hand calculations. The specially developed charts include non-dimensional variables and can easily be applied in sections with various geometrical properties, concrete grade and FRP properties. The proposed shear model combines three theoretical considerations to facilitate calculations. A unified flexural/shear approach is developed in flow chart which can be used to estimate the ultimate strength and the expected failure mode of a concrete beam reinforced with longitudinal FRP bars, with or without transverse reinforcement. The proposed methodology is verified using existing experimental data of 138 beams from the literature, and it predicts the load-bearing capacity and the failure mode with satisfactory accuracy.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.10
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• pp.11-18
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• 2018

In this study, the cross-sectional image has been acquired to evaluate the aggregate distribution affecting quality of lightweight aggregate concrete, and through the binarization method, the study is to calculate the aggregate area of upper and lower sections to develop the method to assess the aggregate distribution of concrete. The acquisition of cross-section image of concrete for the above was available from the cross-sectional photography of cleavage tension of a normal test specimen, and an easily accessible and convenient image analysis software was used for image analysis. As a result, through such image analyses, the proportion of aggregate distribution of upper and lower sections of the test specien could be calculated, and the proportion of aggregate area U/L value of the upper and lower regions of concrete cross-section was calculated, revealing that it could be used as the comprehensive index of aggregate distribution. Moreover, through such method, relatively easy image acquisition methods and analytic methods have been proposed, and this indicated that the development of modeling to assess aggregate distribution quantitatively is available. Based on these methods, it is expected that the extraction of fundamental data to reconsider the connectivity with processes in concrete will be available through quality assessment of quantitative concrete.

• Steel and Composite Structures
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• v.45 no.6
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• pp.895-905
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• 2022

Composite flooring systems consisting of cold-formed steel joists and reinforced concrete slabs offer an efficient, lightweight solution. However, utilisation of composite action to achieve enhanced strength and economical design has been limited. In this study, finite element modelling was utilised to create a three-dimensional model which was then validated against experimental results for a composite flooring system consisting of cold-formed steel joists, reinforced concrete slab and steel bolt shear connectors. This validated numerical model was then utilised to perform parametric studies on the performance of the structural system. The results from the parametric study demonstrate that increased thickness of the concrete slab and increased thickness of the cold formed steel beam resulted in higher moment capacity and stiffness of the composite flooring system. In addition, reducing the spacing of bolts and spacing of the cold formed steel beams both resulted in enhanced load capacity of the composite system. Increasing the concrete grade was also found to increase the moment capacity of the composite flooring system. Overall, the results show that an efficient, lightweight composite flooring system can be achieved and optimised by selecting suitable concrete slab thickness, cold formed beam thickness, bolt spacing, cold formed beam spacing and concrete grade.

• Computers and Concrete
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• v.28 no.5
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• pp.465-478
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• 2021

On the premise of ensuring that the static performance of the concrete spreader is met, the first-order natural frequency of the concrete spreader is increased, and the weight of the main beam is reduced. ANSYS is used as an analysis tool to perform modal analysis on the concrete spreader. The natural frequency, mode shape and modal test verification will be obtained to ensure the accuracy of finite element model analysis. Using the ANSYS designxplorer module, the size of the main beam is set, and the response surface model between the parameter variables and the optimization objective is established according to the experimental design points. Screening algorithm and MOGA algorithm are used to multi-optimize the stress, first-order natural frequency and girder weight, and the optimal solution is obtained by comparison. The results of modal analysis are consistent with those of the experiment, and a set of optimal solutions is obtained through the optimization algorithm. The optimal solution obtained can meet the purpose of increasing the first-order natural frequency of the concrete spreader and reducing the weight of the main beam under the premise of ensuring the overall dynamic and static performance of the concrete spreader.

• Advances in concrete construction
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• v.13 no.6
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• pp.411-422
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• 2022

Aggregates mineralogical, and petrographic properties directly affect the mechanical properties of the produced high strength. This study is focused on the effects of magmatic, sedimentary, and metamorphic aggregates on the performance of high strength concrete. In this study, the effect of the mineralogical properties of aggregates on the compressive strength and modulus of elasticity of high-strength concrete was estimated by Artifical Neural Network (ANN). To estimate the compressive strength and elasticity modules, 96 test specimens were produced. After 28 days under suitable conditions, tests were carried out to determine the compressive strength and modulus of elasticity of the test specimens. This study also focused on the application of artificial neural networks (ANN) to predict the 28-day compressive strength and the modulus of elasticity of high-strength concrete. An ANN model is developed, trained, and tested by using the available test data obtained from the experimental studies. The ANN model is found to predict the modulus of elasticity, and 28 days compressive strength of high strength concrete well, within the ranges of the input parameters. These comparisons show that ANNs have a strong potential to predict the compressive strength and modulus of elasticity of high-strength concrete over the range of input parameters considered.

• Structural Monitoring and Maintenance
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• v.10 no.1
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• pp.43-62
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• 2023

Damage monitoring is a prerequisite step to ensure the safety and performance of concrete structures. Smart aggregate (SA) technique has been proven for its advantage to detect early-stage internal cracks in concrete. In this study, a 1-D CNN-based method is developed for autonomously classifying the damage feature in a concrete anchorage zone using the raw impedance signatures of the embedded SA sensor. Firstly, an overview of the developed method is presented. The fundamental theory of the SA technique is outlined. Also, a 1-D CNN classification model using the impedance signals is constructed. Secondly, the experiment on the SA-embedded concrete anchorage zone is carried out, and the impedance signals of the SA sensor are recorded under different applied force levels. Finally, the feasibility of the developed 1-D CNN model is examined to classify concrete damage features via noise-contaminated signals. The results show that the developed method can accurately classify the damaged features in the concrete anchorage zone.

• Computers and Concrete
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• v.31 no.2
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• pp.123-137
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• 2023

To fully understand shear mechanisms and composite effects of circular concrete-filled steel tube (CFST) columns, systematic numerical investigations were conducted in this paper by improved finite element models. The triaxial plastic-damage constitutive model of the concrete and the interactions between the concrete and steel tube were considered. Afterwards, the critical and upper bound shear span ratios of the circular CFST column under lateral shear loading were determined. The composite effects between the two materials were analyzed by comparing the shear resistance with plain concrete column and hollow steel tube. In addition, a method that predicts the shear bearing capacity of a circular CFST column was proposed. The confining effects on the concrete core and the restraining effects on the steel tube were considered in this method. The proposed formula can predict more accurate results than the methods in different codes and references.

• Smart Structures and Systems
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• v.31 no.1
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• pp.1-11
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• 2023

Assessment of the compressive strength of concrete plays a major role during formwork removal and in the prestressing process. In concrete, temperature changes occur due to hydration which is an influencing factor that decides the compressive strength of concrete. Many methods are available to find the compressive strength of concrete, but the maturity method has the advantage of prognosticating strength without destruction. The temperature-time factor is found using a LM35 temperature sensor through the IoT technique. An experimental investigation was carried out with 56 concrete cubes, where 35 cubes were for obtaining the compressive strength of concrete using a universal testing machine while 21 concrete cubes monitored concrete's temperature by embedding a temperature sensor in each grade of M25, M30, M35, and M40 concrete. The mathematical prediction model equation was developed based on the temperature-time factor during the early age compressive strength on the 1^st, 2^nd, 3^rd and 7^th days in the M25, M30, M35, and M40 grades of concrete with their temperature. The 14^th, 21^st and 28^th day's compressive strength was predicted with the mathematical predicted equation and compared with conventional results which fall within a 2% difference. The compressive strength of concrete at any desired age (day) before reaching 28 days results in the discovery of the prediction coefficient. Comparative analysis of the results found by the predicted mathematical model show that, it was very close to the results of the conventional method.

• Steel and Composite Structures
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• v.39 no.3
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• pp.319-335
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• 2021

This study proposes a new and highly-accurate artificial intelligence model, namely ANN-IP, which combines an interior-point (IP) algorithm and artificial neural network (ANN), to improve the axial compression capacity prediction of elliptical concrete-filled steel tubular (CFST) columns. For this purpose, 145 tests of elliptical CFST columns extracted from the literature are used to develop the ANN-IP model. In this regard, axial compression capacity is considered as a function of the column length, the major axis diameter, the minor axis diameter, the thickness of the steel tube, the yield strength of the steel tube, and the compressive strength of concrete. The performance of the ANN-IP model is compared with the ANN-LM model, which uses the robust Levenberg-Marquardt (LM) algorithm to train the ANN model. The comparative results show that the ANN-IP model obtains more magnificent precision (R² = 0.983, RMSE = 59.963 kN, a20 - index = 0.979) than the ANN-LM model (R² = 0.938, RMSE = 116.634 kN, a20 - index = 0.890). Finally, a new Graphical User Interface (GUI) tool is developed to use the ANN-IP model for the practical design. In conclusion, this study reveals that the proposed ANN-IP model can properly predict the axial compression capacity of elliptical CFST columns and eliminate the need for conducting costly experiments to some extent.