Stochastic analysis of steel frames considering the material, geometrical and loading uncertainties. (May 2023)

• Record Type: Journal Article
• Authors: Dang, Huy-Khanh; Thai, Duc-Kien; Kim, Seung-Eock
• Is Part Of: Advances in engineering software. Volume 179(2023)
• Publication Date: 2023-05
• Subjects: Stochastic analysis -- Statistic approach -- Steel structure -- Bayesian model averaging -- Monte carlo sampling -- Latin hypercube sampling -- Reliability -- Sensitivity; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2023.103434
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: An effective model of SPAAP predicts the accurate steel frame responses. Influence of uncertain parameters is significantly on the structural steel design. The yield stress is the most sensitive comparing with the other uncertainties. Dual mNR and GDC algorithms find the system deformation-based reliability index. The BMA technique is considerable in predicting response of structural components. Abstract: This paper develops a Stochastic Practical Advanced Analysis Program for stochastic analysis of structural steel frames. The second-order refined plastic-hinge analysis method combined with the technical simulation of Latin Hypercube Sampling is developed to predict the actual ultimate load-carrying capacity of steel frames and investigate the sensitivity of the uncertain input parameters. The input parameters of material properties, geometrical characteristics, and load combinations are considered as independent random variables that may occur in simultaneous randomness. A proposed parallel analytical technique integrates the modified Newton-Raphson and Generalized Displacement Control algorithms to solve the nonlinear inelastic problems to estimate the critical displacement-based system reliability index. The results of the statistical analysis in terms of coefficients of variation and Pearson correlation index show that the yield strength of material is the most sensitive with respect to the behavior of steel frames. The Bayesian Model Averaging is employed to find the most influential structural components on the ultimate structural resistance. The useful results of this research may be used in steel structure design and maintenance in practice.