Experimental and Numerical Study on Complex Multi-planar Welded Tubular Joints in Umbrella-Type Space Trusses with Long Overhangs

Jiao, Jinfeng; Ma, Xiaohua; Lei, Honggang; Chen, Y. Frank

doi:10.1007/s13296-018-0064-4

Cited by 2 publications

(3 citation statements)

References 20 publications

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“…Which is not an accurate method. On the other hand, the recent proposed design methods [7][8][9][10][11][12][13][14][15] considered the multi-planar joint as a 3D structure; however, they considered a certain loading condition, which limits the applicability of their developed design formulas.…”

Section: Objectivementioning

confidence: 99%

“…The authors also developed a set of strength reduction factors for cracked multi-planar tubular DT-joints based on the results of their study. Jiao et al (2018) [9] conducted an experimental and numerical investigation of complex multi-planar welded tubular joints in umbrella-type space trusses with long overhangs. The authors conducted a series of experimental tests on four full-scale multi-planar welded tubular joints under static loading.…”

Section: Introductionmentioning

confidence: 99%

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Unified AI-Based Predictive Models for the Ultimate Capacity of Multi-Planar Gapped KK Steel Pipe Joints

Kadry,

El-Ganzoury,

Mokhtar

et al. 2024

Civ Eng J

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The multi-planar steel pipe joints are widely used in communication towers, industrial structures, and offshore platforms. The current design formulas consider this joint as a uniplanar joint and account for the multi-planar effect using empirical correction factors. Recent studies deal with this multi-planar joint as a 3D joint but considering certain loading conditions. Hence, the aim of this research is to develop more general AI-based predictive models for the ultimate capacity of multi-planar gapped KK steel pipe joints, considering both symmetric and asymmetric loading conditions. Three AI techniques were applied to a database of previously published works. These techniques are “Genetic Programming” (GP), “Artificial Neural Network” (ANN), and “Evolutionary Polynomial Regression” (EPR). The prediction accuracies of the developed AI models were compared against two previously published formulas. The results indicated that the developed AI models are much more accurate than the previously published formulas. Also, the results showed that both the ANN and EPR models have almost the same level of accuracy (about 92%), but the EPR model has the advantage of presenting a closed-form equation that could be implemented either manually or using software. Doi: 10.28991/CEJ-SP2024-010-07 Full Text: PDF

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Section: Objectivementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unified AI-Based Predictive Models for the Ultimate Capacity of Multi-Planar Gapped KK Steel Pipe Joints

Kadry,

El-Ganzoury,

Mokhtar

et al. 2024

Civ Eng J

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show abstract

“…Case 1: Welded Q235B joint The welded joints of Q235B cast steel have good low temperature impact toughness and are commonly utilized material for the welding frame of vehicle bogies. 42 The fatigue tests were carried out under three-level stress loading with increasing loading sequence; details about loading stress levels and sequences can be found in Jiao et al 42 The life fractions corresponding to the last loading are chosen to verify prediction performance of different fatigue models. The predicted residual life fractions of the third-level loading are listed in Table 3.…”

Section: Multiloading Conditionmentioning

confidence: 99%

Nonlinear time‐varying fatigue reliability analysis based on the improved toughness exhaustion model

Gao

Liu

2021

Fatigue Fract Eng Mat Struct

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The state function based on fatigue accumulation model has a great influence on accuracy of fatigue reliability analysis of components. A nonlinear fatigue accumulation model considering the interaction of loads is proposed in this paper. The square ratio of front and back loads is presented as a new load interaction factor and embedded into the conventional toughness exhaustion model; thus, this improved model can reflect the load sequence and interaction effect simultaneously. Moreover, the fatigue state function using the improved model is constructed and analyzed by the probability density evolution method. Then experimental data of four materials are used to verify the proposed model, and results show that the residual life fraction predicted by the proposed model is accurate. Furthermore, accuracy and efficiency of the timevarying fatigue reliability analysis using the proposed model is validated using experimental data from a carbon steel test and a helical compression spring test.

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Experimental and Numerical Study on Complex Multi-planar Welded Tubular Joints in Umbrella-Type Space Trusses with Long Overhangs

Cited by 2 publications

References 20 publications

Unified AI-Based Predictive Models for the Ultimate Capacity of Multi-Planar Gapped KK Steel Pipe Joints

Unified AI-Based Predictive Models for the Ultimate Capacity of Multi-Planar Gapped KK Steel Pipe Joints

Nonlinear time‐varying fatigue reliability analysis based on the improved toughness exhaustion model

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