• Structural Engineering and Mechanics
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• v.39 no.3
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• pp.383-398
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• 2011

This paper investigates the nonlinear analysis of concrete-filled steel composite columns subjected to axial loading to predict the ultimate load capacity and behaviour of the columns. Finite element software LUSAS is used to conduct the nonlinear analyses. The accuracy of the finite element modelling is verified by comparing the result with the corresponding experimental result reported by other researchers. Nonlinear analyses are done to study and develop different shapes and number of cold-formed steel sheeting stiffeners with various thicknesses of cold-formed steel sheets. Effects of the parameters on the ultimate axial load capacity and ductility of the concrete-filled steel composite columns are examined. Effects of variables such as concrete compressive strength $f_c$ and cold-formed steel sheet yield stress $f_{yp}$ on the ultimate axial load capacity of the columns are also investigated. The results are shown in the form of axial load-normalized axial shortening plots. It is concluded from the study that the ultimate axial load capacity and behaviour of the concrete-filled steel composite columns can be accurately predicted by the proposed finite element modelling. Results in this study demonstrate that the ultimate axial load capacity and ductility of the columns are affected with various thicknesses of steel sheets and different shapes and number of stiffeners. Also, compressive strength $f_c$ of the concrete and yield stress $f_{yp}$ of the cold-formed steel sheet influence the performance of the columns significantly.

• Proceedings of the Korean Geotechical Society Conference
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• 2001.03a
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• pp.283-290
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• 2001

In this study, static Pile load tests and PDA for open-ended steel pipe pile($\phi$ = 609.6 mm, t = 14 mm) penetrated into the gravel layer(GP - GM) was accomplished and axial load distribution was measured. Based on the tests results, the ultimate bearing capacity and axial load bearing mode were examined. Also, the ultimate pile capacity was calculated by APIL $E^{PLUS}$./.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.03a
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• pp.589-596
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• 2002

In this study, static pile load tests with load transfer measurement were accomplished in the field. Yield pile capacity (or ultimate pile capacity) determined by load-settlement-time relationship was determined and axial load transfer behavior was analyzed. In the test for the four test piles were behaved as end bearing pile but ratios of skin friction to total pile capacity were 27%∼33%.

• Advances in Computational Design
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• v.8 no.2
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• pp.147-177
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• 2023

Elliptical concrete-filled steel tubular (CFST) column is widely used in modern structures for both aesthetical appeal and structural performance benefits. The ultimate axial load is a critical factor for designing the elliptical CFST short columns. However, there are complications of geometric and material interactions, which make a difficulty in determining a simple model for predicting the ultimate axial load of elliptical CFST short columns. This study aims to propose an efficient adaptive neuro-fuzzy inference system (ANFIS) model for predicting the ultimate axial load of elliptical CFST short columns. In the proposed method, the ANFIS model is used to establish a relationship between the ultimate axial load and geometric and material properties of elliptical CFST short columns. Accordingly, a total of 188 experimental and simulation datasets of elliptical CFST short columns are used to develop the ANFIS models. The performance of the proposed ANFIS model is compared with that of existing design formulas. The results show that the proposed ANFIS model is more accurate than existing empirical and theoretical formulas. Finally, an explicit formula and a Graphical User Interface (GUI) tool are developed to apply the proposed ANFIS model for practical use.

• 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.

• Steel and Composite Structures
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• v.33 no.4
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• pp.583-594
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• 2019

In the areas highly exposed to earthquakes, concrete-filled steel tube columns (CFSTCs) are known to provide superior structural aspects such as (i) high strength for good seismic performance (ii) high ductility (iii) enhanced energy absorption (iv) confining pressure to concrete, (v) high section modulus, etc. Numerous studies were reported on behavior of CFSTCs under axial compression loadings. This paper presents an analytical model to predict ultimate load capacity of CFSTCs with circular sections under axial load by using multivariate adaptive regression splines (MARS). MARS is a nonlinear and non-parametric regression methodology. After careful study of literature, 150 comprehensive experimental data presented in the previous studies were examined to prepare a data set and the dependent variables such as geometrical and mechanical properties of circular CFST system have been identified. Basically, MARS model establishes a relation between predictors and dependent variables. Separate regression lines can be formed through the concept of divide and conquers strategy. About 70% of the consolidated data has been used for development of model and the rest of the data has been used for validation of the model. Proper care has been taken such that the input data consists of all ranges of variables. From the studies, it is noted that the predicted ultimate axial load capacity of CFSTCs is found to match with the corresponding experimental observations of literature.

• Structural Engineering and Mechanics
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• v.88 no.1
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• pp.95-107
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• 2023

In this study, circular thin-walled reinforced high strength concrete-filled steel tube (RHSCFST) stub columns with various tube thicknesses (i.e., 1.8, 2.5 and 3.0mm) and reinforcement ratios (i.e., 0, 1.6%, 2.4% and 3.2%) were fabricated to explore the influence of these factors on the axial compressive behavior of RHSCFST. The obtained test results show that the failure mode of RHSCFST transforms from outward buckling and tearing failure to drum failure with the increasing tube thickness. With the tube thickness and reinforcement ratio increased, the ultimate load-carrying capacity, compressive stiffness and ductility of columns increased, while the lateral strain in the stirrup decreased. Comparisons were also made between test results and the existing codes such as AIJ (2008), BS5400 (2005), ACI (2019) and EC4 (2010). It has been found that the existing codes provide conservative predictions for the ultimate load-carrying capacity of RHSCFST. Therefore, an accurate model for the prediction of the ultimate load-carrying capacity of circular thin-walled RHSCFST considering the steel reinforcement is developed, based on the obtained experimental results. It has been found that the model proposed in this study provides more accurate predictions of the ultimate load-carrying capacity than that from existing design codes.

• Computers and Concrete
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• v.33 no.6
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• pp.739-754
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• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• Journal of Ocean Engineering and Technology
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• v.11 no.2
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• pp.11-17
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• 1997

Simple and efficient way of nonlinear analysis considering elasto-plastic large deformation is introduced to calculate the strength of ring-stiffened cylinears subject to combined load of axial compression and lateral pressure. Parametric study gives various collapse modes according to the combination ratio of axial compression and lateral pressure, interaction between axial compression and lateral pressure and imperfection sensitivity of ultimate strength.

• Steel and Composite Structures
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• v.38 no.6
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• pp.617-635
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• 2021

This paper numerically investigated the behavior of built-up square concrete-filled steel tubular (CFST) columns under combined preload and axial compression. The finite element (FE) models of target columns were verified in terms of failure mode, axial load-deformation curve and ultimate strength. A full-range analysis on the axial load-deformation response as well as the interaction behavior was conducted to reveal the composite mechanism. The parametric study was performed to investigate the influences of material strengths and geometric sizes. Subsequently, influence of construction preload on the full-range behavior and confinement effect was investigated. Numerical results indicate that the axial load-deformation curve can be divided into four working stages where the contact pressure of curling rib arc gradually disappears as the steel tube buckles; increasing width-to-thickness (B/t) ratio can enhance the strength enhancement index (e.g., an increment of 1.88% from B/t=40 to B/t=100), though ultimate strength and ductility are decreased; stiffener length and lip inclination angle display a slight influence on strength enhancement index and ductility; construction preload can degrade the plastic deformation capacity and postpone the origin appearance of contact pressure, thus making a decrease of 14.81%~27.23% in ductility. Finally, a revised equation for determining strain εscy corresponding to ultimate strength was proposed to evaluate the plastic deformation capacity of built-up square CFST columns.