Seismic response prediction of FRC rectangular columns using intelligent fuzzy-based hybrid metaheuristic techniques

Mehrabi, Peyman; Honarbari, Soheil; Rafiei, Shervin; Jahandari, Soheil; Bidgoli, Mohsen Alizadeh

doi:10.1007/s12652-020-02776-4

Cited by 56 publications

(26 citation statements)

References 49 publications

(56 reference statements)

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“…As discussed comprehensively, ANFIS can be considered as a reliable predictive method to be integrated with other metaheuristics algorithms, like previous studies [28,43,44] which have used hybrid algorithms to solve non-linear relationships between input and output variables.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

“…Several AI-based models have been utilized to predict the hardened and fresh properties of concrete. Studies have revealed that using hybrid algorithms in many cases can lead to improving the performance of predictive models [43][44][45][46]. The main objective of this paper is to predict the SP demand of specific SCC by soft computing methods to avoid mathematical approaches with high nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

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Application of Artificial Intelligence to Evaluate the Fresh Properties of Self-Consolidating Concrete

et al. 2021

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This paper numerically investigates the required superplasticizer (SP) demand for self-consolidating concrete (SCC) as a valuable information source to obtain a durable SCC. In this regard, an adaptive neuro-fuzzy inference system (ANFIS) is integrated with three metaheuristic algorithms to evaluate a dataset from non-destructive tests. Hence, five different non-destructive testing methods, including J-ring test, V-funnel test, U-box test, 3 min slump value and 50 min slump (T50) value were performed. Then, three metaheuristic algorithms, namely particle swarm optimization (PSO), ant colony optimization (ACO) and differential evolution optimization (DEO), were considered to predict the SP demand of SCC mixtures. To compare the optimization algorithms, ANFIS parameters were kept constant (clusters = 10, train samples = 70% and test samples = 30%). The metaheuristic parameters were adjusted, and each algorithm was tuned to attain the best performance. In general, it was found that the ANFIS method is a good base to be combined with other optimization algorithms. The results indicated that hybrid algorithms (ANFIS-PSO, ANFIS-DEO and ANFIS-ACO) can be used as reliable prediction methods and considered as an alternative for experimental techniques. In order to perform a reliable analogy of the developed algorithms, three evaluation criteria were employed, including root mean square error (RMSE), Pearson correlation coefficient (r) and determination regression coefficient (R2). As a result, the ANFIS-PSO algorithm represented the most accurate prediction of SP demand with RMSE = 0.0633, r = 0.9387 and R2 = 0.9871 in the testing phase.

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Section: Discussion Of Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence to Evaluate the Fresh Properties of Self-Consolidating Concrete

et al. 2021

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“…Viana et al (2020) addressed a corotational Lagrangian formulation for nonlinear dynamic analysis of steel planar frames. Mehrabi et al (2021) studied the dynamic response and mechanical performance of fiber-reinforced concrete columns using hybrid numerical algorithms.…”

Section: Review Of the Literaturementioning

confidence: 99%

Experimental Comparison of the Known Hypotheses of the Lateral Buckling for Semi-Slender Pinned Columns

Murawski¹

2021

International Journal of Structural Glass and Advanced Material

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The paper presents the comparison of experimental results obtained from tests on semi-slender columns with pinned ends made of steel R35 to simplifications and hypotheses of loss of stability by lateral buckling in elastic-plastic states of columns axially compressed by force. The Tetmajer-Jasiński's and Johnson-Ostenfeld's simplifications, as well as the hypotheses given by Engesser and Kármán and Shanley, Ylinen, Březina, Pearson and Bleich and Vol'mir and the author's one approximated, are analyzed. The graphs of surface functions of critical compressive stress cr(A, L*t) depending on a cross-section area and length times thickness product are presented as the theoretical examples of thin-walled cylindrical and square columns made of steel R35. In order to compare the experimental results with other simplifications and hypotheses are shown in the adequate ranges for elastic-plastic states as the graphs of the functions of critical compressive stress depending on slenderness ratio cr().

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“…The experimental test results of the wing-box were also compared with the predictions made by a numerical study performed. Mehrabi et al (2021) dealt with a seismic response prediction of fiber-reinforced concrete columns rectangular columns using intelligent fuzzy-based hybrid metaheuristic techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Stability, Stress and Strain Analysis of Very Slender Pinned Thin-Walled Box Columns according to FEM, Euler and TSTh

Murawski¹,

Wahrhaftig²

2021

American Journal of Engineering and Applied Sciences

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In this study, a Finite Element Method (FEM) analysis is presented for the loss of stability in elastic states of very slender pinned without friction box-section thin-walled column axially compressed. From the FEM buckling linear stress analyses are determined the compressing critical forces for 36 cases, presented in tables and as the surface functions in dependence on the slenderness ratio and cross-section. Also are presented graphs obtained from the FEM post-buckling linear stress analysis for the elastic central line, slope, deflection and states of the stresses and strains of the box-section column 202812500 mm made of steel, by the assumption that a maximal deflection equals the half of a side dimension. The obtained from the FEM computing function and surface graphs are compared and then discussed with graphs corresponding to Euler's and Technical Stability Theory (TSTh) results. Finally are compared graphs of the stresses and strains of box-section thin-walled column 202812500 obtained from FEM and TSTh, but under compressing critical force determined according to TSTh.

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Seismic response prediction of FRC rectangular columns using intelligent fuzzy-based hybrid metaheuristic techniques

Cited by 56 publications

References 49 publications

Application of Artificial Intelligence to Evaluate the Fresh Properties of Self-Consolidating Concrete

Application of Artificial Intelligence to Evaluate the Fresh Properties of Self-Consolidating Concrete

Experimental Comparison of the Known Hypotheses of the Lateral Buckling for Semi-Slender Pinned Columns

Stability, Stress and Strain Analysis of Very Slender Pinned Thin-Walled Box Columns according to FEM, Euler and TSTh

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