Comprehensive experimental database and analysis of circular concrete-filled double-skin tube stub columns: A review

Tang, Hongyuan; Tan, Hongfei; Ge, Sisi; Qin, Jieyu; Wang, Yuzhuo

doi:10.1007/s11709-023-0970-1

Front. Struct. Civ. Eng.

2023

DOI: 10.1007/s11709-023-0970-1

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Comprehensive experimental database and analysis of circular concrete-filled double-skin tube stub columns: A review

Hongyuan Tang,

Hongfei Tan,

Sisi Ge

et al.

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Cited by 2 publications

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An efficient procedure for prediction of the load-displacement curve of CFDST columns

Vu,

Le,

Pham

et al. 2025

Journal of Constructional Steel Research

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An efficient procedure for prediction of the load-displacement curve of CFDST columns

Vu,

Le,

Pham

et al. 2025

Journal of Constructional Steel Research

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Study on Compression Bearing Capacity of Tapered Concrete-Filled Double-Skin Steel Tubular Members Based on Heuristic-Algorithm-Optimized Backpropagation Neural Network Model

Liu,

Dangi,

Yang

et al. 2024

Buildings

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A tapered concrete-filled double-skin steel tubular (TCFDST) structure has been used as the main framework in transmission towers, offshore facility platforms, and turbine towers owing to its excellent mechanical properties. In order to solve the difficulties of calculating the axial compressive capacity of TCFDST members due to the variations in cross-section, this paper applied heuristic optimization algorithms such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Ant Colony Optimization (ACO) to enhance a Backpropagation Neural Network (BPNN) model. A predictive model incorporating both global and local optimization strategies for the axial compressive capacity of a TCFDST structure is proposed. A comprehensive axial database for TCFDST members, comprising 1327 sets of experimental and finite element analysis results, was established, with ten types of component dimensions and material parameters selected as input variables and compressive bearing capacity as the output variable. This study developed and assessed four BPNN models, each optimized by a different heuristic algorithm, against various machine learning algorithms and standards. The heuristic-algorithm-optimized BPNN models demonstrated superior accuracy in predicting the axial compressive capacity of TCFDST members. Through parametric analysis, this study identified the relationship between the model’s bearing capacity predictions and each input parameter, confirming the model’s broad applicability. The optimized BPNN model, refined with heuristic algorithms, provides a significant reference for addressing the computational challenges associated with the load-bearing capacity of TCFDST structures and facilitating their application.

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Comprehensive experimental database and analysis of circular concrete-filled double-skin tube stub columns: A review

Cited by 2 publications

References 55 publications

An efficient procedure for prediction of the load-displacement curve of CFDST columns

An efficient procedure for prediction of the load-displacement curve of CFDST columns

Study on Compression Bearing Capacity of Tapered Concrete-Filled Double-Skin Steel Tubular Members Based on Heuristic-Algorithm-Optimized Backpropagation Neural Network Model

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