Unified Approach for Estimating Axial-Load Capacity of Concrete-Filled Double-Skin Steel Tubular Columns of Multiple Shapes Using Nonlinear FE Models and Artificial Neural Networks

Joo, Mohammad Rafiq; Sofi, Fayaz Ahmad

doi:10.1061/(asce)sc.1943-5576.0000752

Cited by 8 publications

(2 citation statements)

References 65 publications

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“…It has also been observed that the performance of CFDST columns, is better than RCC columns (Hassan and Bhat, 2021). The CFDST columns provided with circular profile for inner and outer tube have performed better than the CFDST columns provided with square, rectangular and hexagonal (Rafiq Joo and Ahmad Sofi, 2022). Due to these advantages of CFDST columns, over RCC and CFT columns, CFDST columns have been used for high-pressure vessels, during submerged tube-tunnel construction in the UK and bridge pier construction in Japan (Goode et al, 1996;Narayanan and Roberts, 1992;Uenaka et al, 2010;Zhao et al, 2018).…”

Section: Introductionmentioning

confidence: 91%

Behavior of partial-length stiffened and full-length stiffened CFDST columns under axial load

Hassan

Bhat

2023

WJE

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Purpose Concrete-filled double skin tube (CFDST) columns are considered one of the most effective steel-concrete composite sections owing to the higher load carrying capacity as compared to its counterpart concrete-filled tube (CFT) columns. This paper aims to numerically investigate the performance of axially loaded, circular CFDST short columns, with the innovative strengthening technique of providing stiffeners in outer tubes. Circular steel hollow sections have been adopted for inner as well as outer tubes, while varying the length of rectangular steel stiffeners, fixed inside the outer tubes only, to check the effect of stiffeners in partially and full-length stiffened CFDST columns. Design/methodology/approach The behaviour of these CFDST columns is investigated numerically by using a verified finite element analysis (FEA) model from the ABAQUS. The behaviour of 20-unstiffened, 80-partially stiffened and 20-full-length stiffened CFDST columns is studied, while varying the strength of steel (fyo = 250–750 MPa) and concrete (30–90 MPa). Findings The FEA results are verified by comparing them with the previous test results. FEA study has exhibited that, there is a 7%–25% and 39%–49% increase in peak-loads in partially stiffened and full-length stiffened CFDST columns, respectively, compared to unstiffened CFDST columns. Originality/value Enhanced strength has been observed in partially stiffened and full-length stiffened CFDST columns as compared to unstiffened CFDST columns. Also, a significant effect of strength of concrete has not been observed as compared to the strength of steel.

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Section: Introductionmentioning

confidence: 91%

Behavior of partial-length stiffened and full-length stiffened CFDST columns under axial load

Hassan

Bhat

2023

WJE

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“…ANNs can provide both linear and nonlinear modeling capabilities, all without necessitating prior knowledge of the connections between input and output variables [13]. ANNs have been used for several applications in civil engineering, including the prediction of mechanical properties [14,15], carbonation depth estimations [16], crack detection [17], the prediction of stress-strain behavior [18], deterioration prediction [19], and many other applications.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Tuff Stones Content in Lightweight Concrete Using Artificial Neural Networks

Yasin,

Awwad,

Malkawi

et al. 2023

Civ Eng J

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Tuff stones are volcanic sedimentary rocks formed by the consolidation of volcanic ash. They possess unique geological properties that make them attractive for a variety of construction and architectural applications. Considerable amounts and various types of Tuff stones exist in the eastern part of Jordan. However, the use of Tuff stones often requires experimental investigations that can significantly impact the accuracy of their physical and mechanical characteristics. To ensure consistent and predictable properties in their mix design, it is essential to minimize the effects of these experimental procedures. Artificial neural networks (ANNs) have emerged as a promising tool to address such challenges, leveraging their ability to analyze complex data and optimize concrete mix design. In this research, ANNs have been used to predict the optimum content of Tuff fine aggregate to produce structural lightweight concrete with a wide range (20 to 50 MPa) of compressive strength. Three different types of Tuff aggregates, namely gray, brown, and yellow Tuff, were experimentally investigated. A set of 68 mixes was produced by varying the fine-tuff aggregate content from 0 to 50%. Concrete cubes were cast and tested for their compressive strength. These samples were then used to form the input dataset and targets for ANN. ANN was created by incorporating the recent advancements in deep learning algorithms, and then it was trained, validated using data collected from the literature, and tested. Both experimental and ANN results showed that the optimum content of the various types of used Tuff fine aggregate ranges between 20 to 25%. The results revealed that there is a clear agreement between the predicted values using ANN and the experimental ones. The use of ANNs may help to cut costs, save time, and expand the applications of Tuff aggregate in lightweight concrete production. Doi: 10.28991/CEJ-2023-09-11-013 Full Text: PDF

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Machine Learning Application of Structural Engineering Problems

Ocak

Niğdeli

Bekdaş

et al. 2023

Studies in Systems, Decision and Control

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Unified Approach for Estimating Axial-Load Capacity of Concrete-Filled Double-Skin Steel Tubular Columns of Multiple Shapes Using Nonlinear FE Models and Artificial Neural Networks

Cited by 8 publications

References 65 publications

Behavior of partial-length stiffened and full-length stiffened CFDST columns under axial load

Behavior of partial-length stiffened and full-length stiffened CFDST columns under axial load

Optimization of Tuff Stones Content in Lightweight Concrete Using Artificial Neural Networks

Machine Learning Application of Structural Engineering Problems

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