Application of deep learning method in web crippling strength prediction of cold-formed stainless steel channel sections under end-two-flange loading

Fang, Zujie; Roy, Krishanu; Quincy, Tsun Ming; Uzzaman, Asraf; Lim, James B.P.

doi:10.1016/j.istruc.2021.05.097

Cited by 56 publications

(17 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep Belief Network (DBN) has been demonstrated to be a useful approach for studying the structural performance of CFS sections [107][108][109][110]. DBN is a representative and effective deep-learning method [107].…”

Section: Other Methodsmentioning

confidence: 99%

A Critical Review on Optimization of Cold-Formed Steel Members for Better Structural and Thermal Performances

et al. 2022

Self Cite

View full text Add to dashboard Cite

The construction and building sectors are currently responsible globally for a significant share of the total energy consumption and energy-related carbon dioxide emissions. The use of Modern Methods of Construction can help reduce this, one example being the use of cold-formed steel (CFS) construction. CFS channel sections have inherent advantages, such as their high strength-to-weight ratio and excellent potential for recycling and reusing. CFS members can be rolled into different cross-sectional shapes and optimizing these shapes can further improve their load-bearing capacities, resulting in a more economical and efficient building solution. Conversely, the high thermal conductivity of steel can lead to thermal bridges, which can significantly reduce the building’s thermal performance and energy efficiency. Hence, it is also essential to consider the thermal energy performance of the CFS structures. This paper reviews the existing studies on the structural optimization of CFS sections and the thermal performance of such CFS structures. In total, over 160 articles were critically reviewed. The methodologies used in the existing literature for optimizing CFS members for both structural and thermal performances have been summarized and presented systematically. Research gaps from the existing body of knowledge have been identified, providing guidelines for future research.

show abstract

Section: Other Methodsmentioning

confidence: 99%

A Critical Review on Optimization of Cold-Formed Steel Members for Better Structural and Thermal Performances

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Because of the specific node connection pattern and automatic FE, it no longer depends on the hierarchical processing of VI. Therefore, to introduce DL into Viscom courses, there is a need to innovate the current teaching content system [ 16 – 20 ].…”

Section: DL Algorithm and Viscom Coursementioning

confidence: 99%

Exploring Multiple Application Scenarios of Visual Communication Course Using Deep Learning Under the Digital Twins

Liu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The emergence of intelligent technology has brought a particular impact and allows for virtuality-reality interaction in the educational field. In particular, digital twins (DTs) feature virtuality-reality symbiosis, solid virtual simulation, and high real-time interaction. It has also seen extended applications to the field of education. This study optimizes the design of the visual communication (Viscom) course based on the deep learning (DL) algorithm. Firstly, the theory of DL is analyzed following the relevant literature, and the typical DL networks, network structures, and related algorithms are introduced. Secondly, Viscom technology is expounded, and DL technology is applied to the Viscom course. Then, the applicability and feasibility of DL in the Viscom course are analyzed through a questionnaire survey (QS) design by collecting students’ attitudes towards Viscom courses before and after the experiment. After introducing DL into the Viscom course, the results show that students’ learning interest and satisfaction with the practical knowledge mastery have increased. However, the satisfaction with theoretical knowledge mastery before practical courses has decreased; overall, the teaching effect of the Viscom course has been improved. Therefore, the introduction of DL into the DT-enabled Viscom can provide a reference for developing the Viscom course. The research content offers technical support (TS) for integrating DT technology and modern education.

show abstract

“…A detailed reliability analysis was carried out using the methods outlined by Hsiao et al [57] and Fang et al [58][59][60]. In accordance with the American standard [48], when the reliability index of any equation is higher than or equal to the target reliability index 2.5, the equation can be considered reliable:…”

Section: Reliability Analysismentioning

confidence: 99%

Numerical Simulation and Design Recommendations for Web Crippling Strength of Cold-Formed Steel Channels with Web Holes under Interior-One-Flange Loading at Elevated Temperatures

et al. 2021

Self Cite

View full text Add to dashboard Cite

This paper investigates the interior-one-flange web crippling strength of cold-formed steel channels at elevated temperatures. The stress-strain curves of G250 and G450 grade cold-formed steel (CFS) channels at ambient and elevated temperatures were taken from the literature and the temperatures were varied from 20 to 700 °C. A detailed parametric analysis comprising 3474 validated finite element models was undertaken to investigate the effects of web holes and bearing length on the web crippling behavior of these channels at elevated temperatures. From the parametric study results, it was found that the web crippling strength reduction factor is sensitive to the changes of the hole size, hole location, and the bearing length, with the parameters of hole size and hole location having the largest effect on the web crippling reduction factor. However, the web crippling strength reduction factor remains stable when the temperature is changed from 20 to 700 °C. Based on the parametric analysis results, the web crippling strength reduction factors for both ambient and elevated temperatures are proposed, which outperformed the equations available in the literature and in the design guidelines of American standard (AISI S100-16) and Australian/New Zealand standard (AS/NZS 4600:2018) for ambient temperatures. Then, a reliability analysis was conducted, the results of which showed that the proposed design equations could closely predict the reduced web crippling strength of CFS channel sections under interior-one-flange loading conditions at elevated temperatures.

show abstract

Application of deep learning method in web crippling strength prediction of cold-formed stainless steel channel sections under end-two-flange loading

Cited by 56 publications

References 53 publications

A Critical Review on Optimization of Cold-Formed Steel Members for Better Structural and Thermal Performances

A Critical Review on Optimization of Cold-Formed Steel Members for Better Structural and Thermal Performances

Exploring Multiple Application Scenarios of Visual Communication Course Using Deep Learning Under the Digital Twins

Numerical Simulation and Design Recommendations for Web Crippling Strength of Cold-Formed Steel Channels with Web Holes under Interior-One-Flange Loading at Elevated Temperatures

Contact Info

Product

Resources

About