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High Strength Concrete Columns under Axial Compression Load: Hybrid Confinement Efficiency of High Strength Transverse Reinforcement and Steel Fibers.

ABSTRACT: Addition of steel fibers to high strength concrete (HSC) improves its post-peak behavior and energy absorbing capability, which can be described well in term of toughness. This paper attempts to obtain both analytically and experimentally the efficiency of steel fibers in HSC columns with hybrid confinement of transverse reinforcement and steel fibers. Toughness ratio (TR) to quantify the confinement efficiency of HSC columns with hybrid confinement is proposed through a regression analysis by involving sixty-nine TRs of HSC without steel fibers and twenty-seven TRs of HSC with hybrid of transverse reinforcement and steel fibers. The proposed TR equation was further verified by compression tests of seventeen HSC columns conducted in this study, where twelve specimens were reinforced by high strength rebars in longitudinal and transverse directions. The results show that the efficiency of steel fibers in concrete depends on transverse reinforcement spacing, where the steel fibers are more effective if the spacing transverse reinforcement becomes larger in the range of 0.25-1 effective depth of the section column. Furthermore, the axial load-strain curves were developed by employing finite element software (OpenSees) for simulating the response of the structural system. Comparisons between numerical and experimental axial load-strain curves were carried out.

SUBMITTER: Perceka W

PROVIDER: S-EPMC5502928 | biostudies-other | 2016 Apr

REPOSITORIES: biostudies-other

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Project description:Concrete-filled steel tubular (CFST) columns have extensive applications in structural engineering due to their exceptional load-bearing capability and ductility. However, existing design code standards often yield different design capacities for the same column properties, introducing uncertainty for engineering designers. Moreover, conventional regression analysis fails to accurately predict the intricate relationship between column properties and compressive strength. To address these issues, this study proposes the use of two machine learning (ML) models-Gaussian process regression (GPR) and symbolic regression (SR). These models accept a variety of input variables, encompassing geometric and material properties of stub CFST columns, to estimate their strength. An experimental database of 1316 specimens was compiled from various research papers, including circular, rectangular, and double-skin stub CFST columns. In addition, a dimensionless output variable, referred to as the strength index, is introduced to enhance model performance. To validate the efficiency of the introduced models, predictions from these models are compared with those from two established standard codes and various ML algorithms, including support vector regression optimized with particle swarm optimization (PSVR), artificial neural networks, XGBoost (XGB), CatBoost (CATB), Random Forest, and LightGBM models. Through performance metrics, the CATB, GPR, PSVR and XGB models emerge as the most accurate and reliable models from the evaluation results. In addition, simple and practical design equations for the different types of CFST columns have been proposed based on the SR model. The developed ML models and proposed equations can predict the compressive strength of stub CFST columns with reliable and accurate results, making them valuable tools for structural engineering. Furthermore, the Shapley additive interpretation (SHAP) technique is employed for feature analysis. The results of the feature analysis reveal that section slenderness ratio and concrete strength parameters negatively impact the compressive strength index.

Dataset Information

High Strength Concrete Columns under Axial Compression Load: Hybrid Confinement Efficiency of High Strength Transverse Reinforcement and Steel Fibers.

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