425 research outputs found

    Novel fuzzy-based optimization approaches for the prediction of ultimate axial load of circular concrete-filled steel tubes

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    An accurate estimation of the axial compression capacity of the concrete-filled steel tubular (CFST) column is crucial for ensuring the safety of structures containing them and preventing related failures. In this article, two novel hybrid fuzzy systems (FS) were used to create a new framework for estimating the axial compression capacity of circular CCFST columns. In the hybrid models, differential evolution (DE) and firefly algorithm (FFA) techniques are employed in order to obtain the optimal membership functions of the base FS model. To train the models with the new hybrid techniques, i.e., FS-DE and FS-FFA, a substantial library of 410 experimental tests was compiled from openly available literature sources. The new model\u2019s robustness and accuracy was assessed using a variety of statistical criteria both for model development and for model validation. The novel FS-FFA and FS-DE models were able to improve the prediction capacity of the base model by 9.68% and 6.58%, respectively. Furthermore, the proposed models exhibited considerably improved performance compared to existing design code methodologies. These models can be utilized for solving similar problems in structural engineering and concrete technology with an enhanced level of accuracy

    Buckling resistance of hot‐finished CHS beam‐columns using FE modelling and machine learning

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    The use of circular hollow sections (CHS) has increased in recent years owing to its excellent mechanical behaviour including axial compression and torsional resistance as well as its aesthetic appearance. They are popular in a wide range of structural members including beams, columns, trusses and arches. The behaviour of hot-finished CHS beam-columns made from normal and high strength steel is the main focus of this paper. A particular attention is given to predict the ultimate buckling resistance of CHS beam-columns using the recent advancement of the artificial neural network (ANN). FE models were established and validated to generate an extensive parametric study. The ANN model is trained and validated using a total of 3439 data points collected from the generated FE models and experimental tests available in the literature. A comprehensive comparative analysis with the design rules in Eurocode 3 is conducted to evaluate the performance of the developed ANN model. It is shown that the proposed ANN based design formula provides a reliable means for predicting the buckling resistance of the CHS beam-columns. This formula can be easily implemented in any programming software, providing an excellent basis for engineers and designers to predict the buckling resistance resistance of the CHS beam-columns with a straightforward procedure in an efficient and sustainable manner with least computational time

    An Innovative Forecasting Formula for Axial Compression Capacity of Circular Steel Tubes Filled with Concrete through Neural Networks

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    The manufacturing industry widely employs concrete and steel as building materials. These materials can be cleverly combined to create an efficient and innovative system, commonly referred to as a composite system. Despite the advantages and high performance of circular concrete filled steel tube (CCFST), there is a lack of reliable and accurate relationships for estimating their ultimate capacity. To address this issue, a wide range of valid experimental tests have been collected as a reference for actual data. By utilizing intelligent systems, such as artificial neural networks (ANN), the data can be effectively used to estimate the ultimate capacity of CCFST columns. Selecting the appropriate algorithm is critical for ANNs to eliminate unnecessary errors and produce optimal outputs. This study proposes a relation created by ANN to determine the ultimate capacity of CCFST columns and assesses its accuracy. Finally, a comparison with existing formulas has been conducted. The proposed network introduced enough accuracy compare to other existing methods

    Prediction and analysis of the residual capacity of concrete-filled steel tube stub columns under axial compression subjected to combined freeze-thaw cycles and acid rain corrosion

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    © 2019 by the authors. This paper presents a theoretical investigation on the safety evaluation, stability evaluation, and service life prediction of concrete-filled steel tube (CFST) structures in a Northern China area with acid rain. The finite element software ABAQUS was used to establish a numerical model, which was used to simulate the axial compression behavior of CFST columns subjected to the combined actions of freeze-thaw cycles and acid rain corrosion. The model performance was validated using the experimental results of the evaluation of mechanical properties, including the failure mode and load-displacement curve. Then, the effects of the section size, material strength, steel ratio, and combined times on the residual capacity were studied. The results show that the section size has a smaller influence on the residual strength than the other parameters and can be neglected in the design procedure. However, the other parameters, including the material strength, steel ratio, and combined times have relatively large influences on the axial compressive performance of CFST stub columns subjected to a combination of freeze-thaw cycles and acid rain corrosion. Finally, design formulae for predicting the residual strength of CFST stub columns that are under axial compression and the combined effect of freeze-thaw cycles and acid rain corrosion are proposed, and their results agree well with the numerical results

    Nonlinear finite element and analytical modelling of reinforced concrete filled steel tube columns under axial compression loading

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    Local buckling of steel and excessive spalling of concrete have necessitated the need for the evaluation of reinforced concrete columns subjected to axial compression loading. Thus, this study investigates the behaviour of concrete filled steel tube (CFST) columns and reinforced concrete filled steel tube (RCFST) columns under the axial compression using the finite element modelling and machine learning (ML) techniques. To achieve this aim, a total of 85 columns from existing studies were analysed utilising the finite element modelling. The ultimate load of the generated datasets was predicted employing various ML techniques. The findings showed that the columns' compressive strength, ductility, and toughness were improved by reducing transverse reinforcement spacing, increasing the number of reinforcing bars, and increasing the thickness and yield strength of outer steel tube. Under the axial compression loading, the finite element modelling analysis provided an accurate assessment of the structural performance of the RCFST columns. Compared to other ML approaches, gradient boosting exhibited the best performance metrics with R2 and root mean square error values of 99.925% and 0.00708 and 99.863% and 0.00717 respectively in training and testing stages, to predict the columns' ultimate load. Overall, gradient boosting can be applied in the ultimate load prediction of CFST and RCFST columns under the axial compression, conserving resources, time, and cost in the investigation of the ultimate load of columns through laboratory testing

    Nonlinear finite element and analytical modelling of reinforced concrete-filled steel tube columns under axial compression loading

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    Local buckling of steel and excessive spalling of concrete have necessitated the need for the evaluation of reinforced concrete columns subjected to axial compression loading. Thus, this study investigates the behaviour of concrete filled steel tube (CFST) columns and reinforced concrete filled steel tube (RCFST) columns under the axial compression using the finite element modelling and machine learning (ML) techniques. To achieve this aim, a total of 85 columns from existing studies were analysed utilising the finite element modelling. The ultimate load of the generated datasets was predicted employing various ML techniques. The findings showed that the columns' compressive strength, ductility, and toughness were improved by reducing transverse reinforcement spacing, increasing the number of reinforcing bars, and increasing the thickness and yield strength of outer steel tube. Under the axial compression loading, the finite element modelling analysis provided an accurate assessment of the structural performance of the RCFST columns. Compared to other ML approaches, gradient boosting exhibited the best performance metrics with R2 and root mean square error values of 99.925% and 0.00708 and 99.863% and 0.00717 respectively in training and testing stages, to predict the columns' ultimate load. Overall, gradient boosting can be applied in the ultimate load prediction of CFST and RCFST columns under the axial compression, conserving resources, time, and cost in the investigation of the ultimate load of columns through laboratory testing

    Finite element, analytical, artificial neural network models for carbon fibre reinforced polymer confined concrete filled steel columns with elliptical cross sections

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    In the present era of architecture, different cross-sectional shapes of structural concrete elements have been utilized. However, this change in shape has a significant effect on load-carrying capacity. To restore this, the use of column confinements with elliptical sections has gained attention. This paper aim to investigate the effect of elliptical shape sections of confined concrete reinforced with Carbon Fiber Reinforced Polymer (CFRP) and steel tube on axial load-carrying capacity. This study is achieved using following tools Finite Element (FE) in Abaqus and Artificial Neural Networks (ANN) modeling. The study involved a 500-mm-high column with three sets of aspect ratios: 1.0, 1.5, and 2.0. In each aspect ratio, three different layers of CFRP were used, i.e., .167, .334, and .501-mm. Analytical results showed that with the increase in aspect ratio from 1 to 2, there is a decrease in ultimate axial load of about 23.2% on average. In addition, the combined confining pressure of steel tube and CFRP increases with a decrease in dilation angle as the number of CFRP layers increases. The failure mode for the column with a large aspect ratio is local buckling at its mid-height along the minor axis. The result showed a good correlation between FE and experimental results of ultimate stress and strains, with a mean squared error of 2.27 and .001, respectively. Moreover, ANN and analytical models showed a delightful correlation of R2 of .97 for stress models and .88 for strain models, respectively. The elliptical concrete section of the column confined with steel tubes can be adopted for a new architectural type of construction; however, with more than three aspect ratios, the wrapping of the section with CFRP jackets is highly recommended
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