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GFRP-concrete-steel composite columns (GCS) are formed by combining different components with excellent mechanical properties. The GCS exhibits good corrosion resistance and can serve as a pile foundation platform for offshore wind turbines. An Artificial Neural Network (ANN) is utilized to swiftly predict the loading capacity of GCS columns with various geometrical parameters. The prediction error is primarily controlled within 10%. The thickness of GFRP tubes (tG), the inner diameter of GFRP tubes (DG,i), the thickness of steel tubes (ts), the outer diameter of steel tubes (DS,o), the strength of GFRP tubes (fG), the strength of concrete (fC), and the strength of steel tubes (fS) were used as independent variables to determine the key factors affecting the loading capacity of GCS columns. The impact of the number of input variables on the final prediction accuracy is analyzed. The proportional influence of different parameters on the loading capacity of GCS column is investigated using the Garson algorithm, among which, the thickness of the GFRP tube (tG) accounts for the largest proportion, approximately 24.57%. The suitability of the artificial neural network for predicting the loading capacity of GCS columns with various geometries is revealed. The results indicate that the ANN can be utilized for high-precision compressive strength prediction, and the thickness of the GFRP tube (tG) is the most influential factor on the loading capacity of GCS columns. [ABSTRACT FROM AUTHOR]

Because the cement content is inexorably growing, CO2 emissions from the cement industry are becoming unavoidable. The use of rice husk ash (RHA) makes the system environmentally benign. This paper investigates the effect of RHA and manufactured sand (M-Sand) on the axial capacity of circular Concrete Filled Steel Tube (CFST) specimens subjected to axial compression. Thirty-two CFST specimens infilled with self-compacting concrete (SCC) with a replacement of RHA and M-Sand to cement and fine aggregate in 0%, 15%, 20% and 25% proportions are subjected to axial compression. Results show that use of RHA beyond 20% decreased the compressive strength of concrete and eventually decreased the axial capacity of CFST and occurred higher local buckling irrespective of their L/D ratios. Failure modes, strength index, confinement along with steel and concrete contribution on axial capacities of CFST specimens are studied. The axial capacities of the specimens were compared with various design code results such as: American concrete institute, Australian Standards, Chinese code, Eurocode, and Japanese code. Among all the codes, Chinese code overestimated the test results and hence, the axial capacity equation is altered and compared the results with test data along with 278 circular CFST data available from literature and found the results to be accurate. [ABSTRACT FROM AUTHOR]

Steel construction is increasingly using thin-walled profiles to achieve lighter, more cost-effective structures. However, analyzing the behavior of these elements becomes very complex due to the combined effects of local buckling in the thin walls and overall global buckling of the entire column. These factors make traditional analytical methods difficult to apply. Hence, in this research work, the strength of bi-axially loaded track and channel cold formed composite column has been estimated by applying three AI-based symbolic regression techniques namely (GP), (EPR) and (GMDH-NN). These techniques were selected because their output models are closed form equations that could be manually used. The methodology began with collecting a 90 records database from previous researches and conducting statistical, correlation and sensitivity analysis, and then the database was used to train and validate the three models. All the models used local and global slenderness ratios (λ, λc, λt) and relative eccentricities (ex/D, ey/B) as inputs and (F/Fy) as output. The performances of the developed models were compared with the predicted capacities from two design codes (AISI and EC3). The results showed that both design codes have prediction error of 33% while the three developed models showed better performance with error percent of 6%, and the (EPR) model is the simplest one. Also, both correlation and sensitivity analysis showed that the global slenderness ratio (λ) has the main influence on the strength, then the relative eccentricities (ex/D, ey/B) and finally the local slenderness ratios (λc, λt). [ABSTRACT FROM AUTHOR]

This study investigates the distortional buckling strength of a partially closed cold-formed steel (CFS) channel section with intermediate web stiffeners and outward lips under axial compression. Generally, a closed CFS cross section has higher torsional resistance when compared to an open cross section. However, an open cross section is more popular in the construction industry. Despite the popularity of CFS open sections, distortional buckling can reduce the load-carrying capacity of such open cross sections. To improve the distortional buckling strength of the proposed selected section, a simple spacer plate is added externally to the channel section in the transverse direction. Initially, a numerical model is developed using the finite element software ANSYS, which is then validated against the experimental results available in the literature. A detailed parametric study was also conducted to investigate the influence of thickness, depth, and spacing of spacer plates on the distortional buckling strength of stiffened CFS channel sections. From the results of the parametric study, it was found that the presence of spacer plates can increase the axial capacity of stiffened CFS channel sections by as much as 32%. Finite element results were compared with the design strengths calculated in accordance with the direct strength method (DSM). [ABSTRACT FROM AUTHOR]

Due to the corrosion problem in reinforced concrete structures, the use of fiber-reinforced polymer (FRP) bars may be preferred in place of traditional reinforcing steel. FRP bars are used in concrete constructions to boost the strength of structural elements and retain their longevity. In this study, the axial load carrying capacity (ALCC) of the FRP-reinforced concrete columns has been evaluated using analytical, as well as machine learning, models. A total of fourteen popular analytical models and one proposed machine learning-based model were used to estimate the ALCC of the concrete columns. The proposed machine learning model is based on an artificial neural network (ANN) method. The performance of the ANN, as well as the analytical models, are assessed using six different performance indices. The R-value of the developed ANN model is 0.9758, followed by an NS value of 0.9513. It has been found that the mean absolute percentage error of the best-fitted analytical model is 328.71% higher than the ANN model, and the root-mean-square error value of the best-fitted analytical model is 211.97% higher than the ANN model. The evaluated data demonstrate that the proposed ANN model performs better than the other analytical models. The developed model is quick and easy-to-use to estimate the axial capacity of the FRP-reinforced concrete columns. [ABSTRACT FROM AUTHOR]

Several past studies have shown the use of glass fibre-reinforced polymer (GFRP) bars to alleviate the reinforced steel rusting issue in different concrete structures. However, the practise of GFRP bars in concrete columns has not yet achieved a sufficient confidence level due to the lack of a theoretical model found in the literature. The objective of the current study is to introduce a novel prediction model for the axial capability of concrete columns made with bars of GFRP. For this purpose, two different approaches, such as data envelopment analysis (DEA) and artificial neural networks (ANNs) modelling, are used on a collected dataset of 266 concrete column specimens made with GFRP bars from previous literature works. Eight parameters were used to predict the axial performance of GFRP-based RC columns. The proposed DEA and ANNs predictions demonstrated a good correlation with the testing dataset, having R2 values of 0.811 and 0.836, respectively. A comparative analysis of the DEA and ANNs models is undertaken, and it was found that the suggested models are capable of accurately forecasting the structural response of GFRP-made RC column structures. Then, a comprehensive parametric analysis of 266 GFRP-based columns was performed to study the effect of different materials and their geometrical shape. [ABSTRACT FROM AUTHOR]

Fiber-reinforced polymer (FRP) rebars are increasingly being used as an alternative to steel rebars in reinforced concrete (RC) members due to their excellent corrosion resistance capability and enhanced mechanical properties. Extensive research works have been performed in the last two decades to develop predictive models, codes, and guidelines to estimate the axial load-carrying capacity of FRP-RC columns. This study utilizes the power of artificial intelligence and develops an alternative approach to predict the axial capacity of FRP-RC columns more accurately using data-driven machine learning (ML) algorithms. A database of 117 tests of axially loaded FRP-RC columns is collected from the literature. The geometric and material properties, column shape and slenderness ratio, reinforcement details, and FRP types are used as the input variables, while the load-carrying capacity is used as the output response to develop the ML models. Furthermore, the input-output relationship of the ML model is explained through feature importance analysis and the SHapely Additive exPlanations (SHAP) approach. Eight ML models, namely, Kernel Ridge Regression, Lasso Regression, Support Vector Machine, Gradient Boosting Machine, Adaptive Boosting, Random Forest, Categorical Gradient Boosting, and Extreme Gradient Boosting, are used in this study for capacity prediction, and their relative performances are compared to identify the best-performing ML model. Finally, predictive equations are proposed using the harmony search optimization and the model interpretations obtained through the SHAP algorithm. [ABSTRACT FROM AUTHOR]

In this study, artificial neural network analysis is introduced to aid in estimating the axial capacity of concrete-filled steel tube stub columns. A wide range of experimental database of rectangular concrete-filled steel tube columns available in the literature is used here in order to construct and verify the model. The Levenberg-Marquardt algorithm is employed in this backpropagation training procedure. The present artificial neural network model has been created using six input neurons that have different geometric and material properties of the column including the length of the column, the width and the depth of the section, the thickness of the steel tube, the compressive strength of the concrete core, and the yield strength of the steel tube. Six hidden neurons have been employed, in addition to one output neuron that gives the ultimate capacity of the column. The verification process has showed that the present model is very accurate in predicting the ultimate capacities of the columns. This model is tested against other methods found in codes and standards and a method that has been previously proposed by the authors. With the limitations set by the codes and standards, the present model can be used safely and with sufficient accuracy. A parametric study using the artificial neural network model has been conducted to explore the effect of different parameters on axial capacities of the composite columns. The results have showed that careful optimizing techniques should be followed in order to get the most efficient sections. [ABSTRACT FROM AUTHOR]

Concrete Filled Tube (CFT) columns represent an effective alternative to conventional reinforced concrete columns. Recently, there have been trials to replace steel encasing tubes with Polyvinyl-Chloride (PVC) tubes for the enhancement of columns performance. The use of fibrous concrete as the core of such columns still needs more focus. In this study, glass fibrous concrete was used to fabricate PVC CFT (PCFT) short columns. PVC tubes thickness, tube roughness and lateral PVC supporting ties were the adopted parameters herein. These columns were tested in compression and their vertical and lateral deformations were monitored during loading. It was concluded that PCFT columns have generally shown higher axial capacity and ductility in comparison to concrete core. The best performance was related to PCFT columns with rough interface which showed axial capacity of about two thirds and ductility of about three folds higher than concrete core. Also, the use of lateral ties along the column height showed reasonable results, but the installation method of these ties needs to be modified to reach better results. Based on the experimental program, a design formula was deduced to predict short PCFT columns capacity with an error of about 8%. [ABSTRACT FROM AUTHOR]

Fiber Reinforced Polymers (FRPs) have wide applications in the field of concrete construction due to their superior performance over conventional materials. This research focuses on the structural behavior of steel tube FRP jacket--confined concrete (STFC) columns under axial concentric loading and proposes a new empirical equation for predicting the axial load-carrying capacity of STFC columns having thickness of FRP-fabric ranging from 0.09 mm to 5.9 mm. A large database of 700 FRP-confined concrete specimens is developed with the detailed information of critical parameters, i.e. elastic modulus of FRPs (Ef), compressive strength of unconfined concrete (f'co), diameter of specimen (D), height of specimen (H), total thickness of FRPs (N.tf), and the ultimate strength of confined concrete (f'cc). After the preliminary evaluation of constructed database, a new empirical model is proposed for the prediction of axial compressive strength of FRP-confined specimens using general regression analysis by minimizing the error functions such as root mean squared error (RMSE) and coefficient of determination (R²). The proposed FRP-confinement strength model presented higher accuracy as compared with previously proposed models. Finally, an equation is proposed for the predictions of axial load carrying capacity of STFC columns. For the validation of proposed equation, an extensive parametric study is performed using the proposed nonlinear finite element model (FEM). The FEM is calibrated using the load-deflection results of STFC columns from literature. A close agreement was observed between the predictions of proposed finite element model and proposed capacity equation. [ABSTRACT FROM AUTHOR]

• A new model is proposed to calculate the axial capacity of FRP confined CFST columns. • Experimental test results are used for the development of the model. • GEP based technique is implemented to propose the model. • Prediction performance of the model is benchmarked against the existing equations. • Developed model exhibits good agreement with experimental results and provides accurate predictions. Fiber reinforced polymer (FRP) jackets have been recently used for strengthening purpose of concrete filled steel tube (CFST) composite columns and also for the suppression of outward local buckling that CFST columns may suffer. This study intends to investigate the axial compression capacity of circular CFST columns transversely strengthened by FRP. For this, an attempt has been made to implement gene expression programming (GEP) technique for the development of axial capacity of confined CFST prediction model. The database required for the derivation of the GEP model is based on the extensive experimental results of the FRP confined CFST columns tested in compression (92 test specimens). Moreover, these test specimens wrapped with the carbon or glass fiber sheets have different length to diameter ratios of 2.0–4.5. The input predictor variables used in the study are the properties of the FRP (the type, thickness and sheet wrap layers, tensile strength and elastic modulus of FRP), the steel tube (outer diameter, thickness, length and tensile strength of steel tube) and unconfined concrete (compressive strength of in-filled concrete). To verify the effectiveness of the model, the values from GEP were examined statistically against those of existing equations given by various researchers. After analyzing and comparing the proposed model with the existing formulas, it was found that the results obtained by GEP have significantly less errors and far more accurate. [ABSTRACT FROM AUTHOR]

Concrete-filled steel tubular (CFST) columns are increasingly used around the world because they offer two significant advantages. The first one is the composite action of the steel tube and infilled concrete, which enhances the strength and ductility of the columns. The second one is the use of the steel tube as a permanent formwork for concrete casting, which allows saving construction time and costs. Although considerable research and several experimental tests have been carried out on CFST columns, there is not a probabilistic model for the evaluation of their axial capacity yet. Additionally, the effects of the axial load eccentricity and debonding have received little attention so far. Within this framework, the present paper proposes a physics-based probabilistic model to predict the ultimate axial capacity of CFST columns, which is developed as the sum of a deterministic part and a probabilistic correction term, together with two additional corrective models to describe its reduction due to load eccentricity and debonding, which are developed coherently with the axial capacity model as probabilistic correction terms. The accuracy of the proposed models is compared with that of existing capacity equations already in use within technical standards and available literature. Additionally, this study provides uncertainty factors to allow using the proposed capacity model for design applications. Thanks to their very good accuracy and compact form, the proposed models are suitable to be included within technical standards. On the other hand, differently from common deterministic models, they can also be updated as new experimental data are available. A case study with the derivation of fragility curves for a CFST bridge column shows a possible application and the advantages of the proposed model. • Probabilistic model for the axial capacity of Concrete Filled Steel Tubes (CFST). • Capacity reduction factors for load eccentricity and debonding. • Bayesian model calibration using all available data from the literature. • Comparison between formulations from literature and technical standards. • Calibration of uncertainty factors corresponding to different reliability levels. [ABSTRACT FROM AUTHOR]

Ultra high performance cementitious composites filled steel tube (UHPCC-FST) column has been widely applied as the load bearing members for long-span bridges, which are the potential bomb explosion attacking targets in terroristic activities. In the authors' previous work, the residual axial load bearing capacity of UHPCC-FST specimens under contact explosion was investigated experimentally, and this paper further perform the corresponding numerical simulation study. By utilizing the multi-material Arbitrary Lagrange-Euler (ALE) algorithm, Fluid-Structure Interaction (FSI) method, the restart input data method and element erosion algorithm implemented in the finite element (FE) code LS-DYNA, the numerical simulations corresponding to the contact charge explosion and the following axial compression tests are carried out. The damage and failure modes of UHPCC-FST specimen are reproduced and validated by comparing with the experimental data. Furthermore, the related parametric influences, e.g., thickness and strength of steel tube, compressive strength of core concrete and column diameter, on the local blast formed crater depth, the post-blast residual axial load bearing capacity, as well as the corresponding damage index of UHPCC-FST specimen under contact detonation are discussed. The influential degree of above parameters on the residual axial capacity of UHPCC-FST specimen under contact explosion is clarified. The present work can provide helpful references for evaluating the post-blast performance as well as the design of UHPCC-FST specimen under contact explosion. • High-fidety numerical simulations of contact explosion and subsequent axial compression tests on UHPCC-FST are performed. • Validations of the adopted numerical algorithms, FE model, and modified constitutive model parameters of UHPCC are verified. • Parametric influences on local crater depth and post-blast residual axial load bearing capacity, etc. are discussed. [ABSTRACT FROM AUTHOR]

This paper investigates the axial load capacity of concrete encased steel composite stub columns with high strength concrete and steel materials. A total of 14 column specimens with varying material strengths and different steel section shapes were tested under concentric compression load. The test results revealed that the design methods in EN 1994-1-1 and JGJ 138-2016 overestimate the axial load capacity of high strength concrete encased steel columns. Adding a small percentage of steel fiber in the high strength concrete was found to improve the compression resistance of the composite section. A new test database consisting of 51 partially encased composite sections and 82 fully encased composite sections was established, covering a wide range of section geometric and material grades. Parametric study was then carried out to assess current design methods in predicting the axial capacity of such composite columns with respect to material strengths, steel contribution ratio, reinforcement ratio, section slenderness ratio, confined concrete area ratio, and concrete confinement efficiency. The effectiveness of concrete confinement in partially encased composite column was evaluated and a simplified method was proposed to compute the enhanced concrete strength based on regression analysis. For fully concrete encased composite columns, a concrete strength reduction factor was proposed to be used with EN 1994-1-1 to predict the compression resistance. Design recommendation was made considering the early cover spalling of high strength concrete and the material compatibility between steel and concrete in composite column design. • Test is performed on CES stub columns with C90/C130 concrete and S500/S690 steel. • A database is collected to perform a comprehensive assessment of current design codes. • A simple equation is proposed to evaluate confining effect of PEC columns. • Design recommendations are proposed for FEC column. [ABSTRACT FROM AUTHOR]