Parametric study and equations of the maximum SCF for concrete filled steel tubular T-joints under in-plane and out-of-plane bending

Musa, Idris; Mashiri, Fidelis Rutendo

doi:10.1016/j.tws.2018.11.017

Cited by 14 publications

(6 citation statements)

References 5 publications

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“…In comparison to current parametric equations, Mohammed et al's proposed equation provides a more accurate forecast of the SIF in cracked tubular T/Y joints. According to the proposed equation's error analysis, 90% of the results have errors of less than 15%, and 99.7% have errors of less than 30% [30]. The fatigue fracture of austenitic stainless steel LCFW cruciform connections was discovered to be a semi-elliptic crack, one that was distinct from the fracture of LCFW structural steel, which implies higher fatigue and lower SIF [28].…”

Section: Resultsmentioning

confidence: 95%

“…In a parametric investigation, Musa et al [30] developed parametric equations for the maximum stress concentration factors (SCF) for concrete filled steel tubular T-connections by simulation with three-dimensional (3D) finite element (FE) models together with multiple non-linear regression analysis under in-plane and out-of-plane bending. Equally, Mohamed et al [31] also developed parametric equations to predict the SIF of cracked tubular T/Y-joints through finite element analysis and non-linear regression analysis by varying crack propagation angles.…”

Section: Regression Based Methodsmentioning

confidence: 99%

“…Steel [21][22][23][24][25][28][29][30]38,45], metal composite [29], stainless steel [34,35], titanium [39].…”

Section: Regressionmentioning

confidence: 99%

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Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Gbagba,

Maccioni,

Concli

2023

Applied Sciences

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In the shipbuilding, construction, automotive, and aerospace industries, welding is still a crucial manufacturing process because it can be utilized to create massive, intricate structures with exact dimensional specifications. These kinds of structures are essential for urbanization considering they are used in applications such as tanks, ships, and bridges. However, one of the most important types of structural damage in welding continues to be fatigue. Therefore, it is necessary to take this phenomenon into account when designing and to assess it while a structure is in use. Although traditional methodologies including strain life, linear elastic fracture mechanics, and stress-based procedures are useful for diagnosing fatigue failures, these techniques are typically geometry restricted, require a lot of computing time, are not self-improving, and have limited automation capabilities. Meanwhile, following the conception of machine learning, which can swiftly discover failure trends, cut costs, and time while also paving the way for automation, many damage problems have shown promise in receiving exceptional solutions. This study seeks to provide a thorough overview of how algorithms of machine learning are utilized to forecast the life span of structures joined with welding. It will also go through their drawbacks and advantages. Specifically, the perspectives examined are from the views of the material type, application, welding method, input parameters, and output parameters. It is seen that input parameters such as arc voltage, welding speed, stress intensity factor range, crack growth parameters, stress histories, thickness, and nugget size influence output parameters in the manner of residual stress, number of cycles to failure, impact strength, and stress concentration factors, amongst others. Steel (including high strength steel and stainless steel) accounted for the highest frequency of material usage, while bridges were the most desired area of application. Meanwhile, the predominant taxonomy of machine learning was the random/hybrid-based type. Thus, the selection of the most appropriate and reliable algorithm for any requisite matter in this area could ultimately be determined, opening new research and development opportunities for automation, testing, structural integrity, structural health monitoring, and damage-tolerant design of welded structures.

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Section: Resultsmentioning

confidence: 95%

Section: Regression Based Methodsmentioning

confidence: 99%

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Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Gbagba,

Maccioni,

Concli

2023

Applied Sciences

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“…This is because parameter 1 τ ≈ for all the specimens tested in the current study. It is worth to mention that the authors (Musa et al [20]), in an extensive finite element simulation study, found that for 0.2 0.4 τ ≤ ≤ the SCF on the tension side is always greater on the brace than that on the chord. Figures 22 -23 show that the distribution of the SCF on the chord and the brace is rather similar in the three specimens which indicates that parameter β has no remarkable effect on the SCF under in-plane bending.…”

Section: In-plane Bendingmentioning

confidence: 99%

Experimental Stress Concentration Factor in Concrete-Filled Steel Tubular T-Joints

Musa

Mashiri

Zhu

et al. 2018

Journal of Constructional Steel Research

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Experimental investigation of stress concentration factor (SCF) in Steel circular hollow section brace welded to concrete-filled circular hollow section chord (CHS-to-CFCHS) T-joints has been performed under axial tension, axial compression, in-plane bending and out-of-plane bending. The distribution of SCF around the welded brace-to-chord intersection on both the brace and chord has been investigated using three CHS-to-CFCHS T-joint specimens. The experimental SCF results have been compared with the predicted SCF in empty T-joints. The relationship between the maximum SCF in relation to parameter β, with fixed other geometrical parameters, has been investigated for the basic load conditions. The experimental maximum SCF under axial tension has been compared with the predicted maximum SCF from parametric equations for CHS-to-CFCHS T-joints previously developed by the authors. The results show that the concrete has a significant effect in reducing the SCF, mostly under axial tension and the parametric equations for predicting SCFs in empty T-joints are not suitable for CHS-to-CFCHS T-joints. The effect of parameter β on the maximum SCF in CHS-to-CFCHS T-joints is significant under axial tension and out-of-plane bending moment.

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“…[1][2][3][4] Meanwhile, FRP is used in the field of civil engineering for the replacement of rebar and enhancement. the T-shaped structure involved in this study is widely used in Bridges and other structures in civil engineering, 5,6 which can provide references for the promotion and application of materials and structures in civil engineering. As manufacture-process constraints, the generated structures require connecting pieces.…”

Section: Introductionmentioning

confidence: 99%

An experimental study on the failure mechanism of local-strengthening glass fiber reinforced polymer T-joints under/after the low-velocity impact loading

Wu,

You,

et al. 2023

Journal of Composite Materials

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In this study, an experimental methodology, consisting of low-velocity impact (LVI) and quasi-static compressive test, is conducted to investigate the performance and failure mechanism of GFRP T-joints, fabricated by vacuum-assisted resin infusion process (VARI), when subjected to out-of-plane LVI loading at the corner. Further, the effect of LVI loading on the reduction of strength is analyzed by the tensile test after LVI. In order to improve the strength of the GFRP T-joints, one layer of stainless-steel wire net (SSWN) is inserted into the corner of the T-joints structure. The experimental results indicate that the addition of SSWN decreases the damaged area of delamination in the web, meanwhile, connecting the nearby GFRP layers to avoid the structure failure suddenly, which improves its performance of tensile test after LVI.

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Parametric study and equations of the maximum SCF for concrete filled steel tubular T-joints under in-plane and out-of-plane bending

Cited by 14 publications

References 5 publications

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Experimental Stress Concentration Factor in Concrete-Filled Steel Tubular T-Joints

An experimental study on the failure mechanism of local-strengthening glass fiber reinforced polymer T-joints under/after the low-velocity impact loading

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