Buckling behavior of 420 MPa HSSY columns: Test investigation and design approach

Deng, Hongda; Zhang, Donghong; Cui, Lei

doi:10.1016/j.engstruct.2017.07.016

Cited by 5 publications

(11 citation statements)

References 10 publications

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“…Lateral deflection of the column Δr (in %) was defined using the following equation: Δr=rL×100, where r is the maximum lateral deflection in m over the centerline of the column. For such a column under axial compression, typical buckling behavior was reported by many researchers in the literature [2,5,6,8]. A schematization of the nonlinear relationship between load and lateral deflection of the column is presented in Figure 1b.…”

Section: Methodsmentioning

confidence: 70%

“…Various parameters of structural elements are uncertain in nature, for instance, the length of structural components [1]; geometry of the cross-section [2]; boundary conditions [3]; loads [4]; and especially mechanical properties of materials [5,6,7,8,9,10,11,12]. Indeed, uncertainty in Young’s modulus, Poisson’s ratio and yield strength of materials constituting structural elements has been experimentally explored in the literature, such as by Shi et al [5] for hollow circular steel tubes, Ma et al [1] for I-section steel columns, Cao et al [7] for welded T-section structural elements, Jamaluddin et al [13] for elliptical concrete filled columns and Vu et al [14] for circular concrete filled steel tubes.…”

Section: Introductionmentioning

confidence: 99%

“…Despite all of these efforts, it is not always possible to investigate the highly nonlinear relationship between load–lateral deflection of columns under axial loading, as reported in the literature [1,2,5,6,11,37,38,39]. Such nonlinear phenomenon (large displacement when the load increment is small) made the instability analysis of structural components under compressive stress more complicated [40,41].…”

Section: Introductionmentioning

confidence: 99%

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Quantification of Uncertainties on the Critical Buckling Load of Columns under Axial Compression with Uncertain Random Materials

Desceliers

et al. 2019

Materials

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This study is devoted to the modeling and simulation of uncertainties in the constitutive elastic properties of material constituting a circular column under axial compression. To this aim, a probabilistic model dedicated to the construction of positive-definite random elasticity matrices was first used, involving two stochastic parameters: the mean value and a dispersion parameter. In order to compute the nonlinear effects between load and lateral deflection for the buckling problem of the column, a finite element framework combining a Newton-Raphson solver was developed. The finite element tool was validated by comparing the as-obtained critical buckling loads with those from Euler’s formula at zero-fluctuation of the elasticity matrix. Three levels of fluctuations of material uncertainties were then propagated through the validated finite element tool using the probabilistic method as a stochastic solver. Results showed that uncertain material properties considerably influenced the buckling behavior of columns under axial loading. The coefficient of variation of a critical buckling load over 500 realizations were 15.477%, 26.713% and 41.555% when applying dispersion parameters of 0.3, 0.5 and 0.7, respectively. The 95% confidence intervals of column buckling response were finally given. The methodology of modeling presented in this paper is a potential candidate for accounting material uncertainties with some instabilities of structural elements under compression.

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Section: Methodsmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantification of Uncertainties on the Critical Buckling Load of Columns under Axial Compression with Uncertain Random Materials

Desceliers

et al. 2019

Materials

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“…In this study, 57 experimental results on buckling behavior of Y-section steel column were extracted from the literature and summarized in Table 1. The dataset, conducted by Yu et al [52], was achieved from measures of steel columns with pinned-pinned boundary conditions (Figure 5a). This form of cross-section has been recently used in transmission tower to increase the performance under various usage conditions.…”

Section: Data Used and Input Selectionmentioning

confidence: 99%

“…Moreover, Monte Carlo simulations were applied to evaluate the robustness of both ANFIS-GA and ANFIS-PSO models, taking into account input variability. With this aim, 57 experimental results in the work of Yu et al [52] with input variables (length of columns, geometry of cross-section, initial geometrical deviation in both x and y directions) and one output variable (the buckling load of 420 MPa high strength steel Y-section columns with slenderness ratio varying from 30 to 80) was used to generate training and testing datasets. Several statistical quality assessments such as the coefficient of determination (R 2 ), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were used to validate the performance of the proposed models.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression

Pham

et al. 2019

Materials

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This study aims to investigate the prediction of critical buckling load of steel columns using two hybrid Artificial Intelligence (AI) models such as Adaptive Neuro-Fuzzy Inference System optimized by Genetic Algorithm (ANFIS-GA) and Adaptive Neuro-Fuzzy Inference System optimized by Particle Swarm Optimization (ANFIS-PSO). For this purpose, a total number of 57 experimental buckling tests of novel high strength steel Y-section columns were collected from the available literature to generate the dataset for training and validating the two proposed AI models. Quality assessment criteria such as coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to validate and evaluate the performance of the prediction models. Results showed that both ANFIS-GA and ANFIS-PSO had a strong ability in predicting the buckling load of steel columns, but ANFIS-PSO (R2 = 0.929, RMSE = 60.522 and MAE = 44.044) was slightly better than ANFIS-GA (R2 = 0.916, RMSE = 65.371 and MAE = 48.588). The two models were also robust even with the presence of input variability, as investigated via Monte Carlo simulations. This study showed that the hybrid AI techniques could help constructing an efficient numerical tool for buckling analysis.

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Experimental and Numerical Investigations on Closely Spaced Built-Up Angle Members

et al. 2023

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Buckling behavior of 420 MPa HSSY columns: Test investigation and design approach

Cited by 5 publications

References 10 publications

Quantification of Uncertainties on the Critical Buckling Load of Columns under Axial Compression with Uncertain Random Materials

Quantification of Uncertainties on the Critical Buckling Load of Columns under Axial Compression with Uncertain Random Materials

Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression

Experimental and Numerical Investigations on Closely Spaced Built-Up Angle Members

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