Structural stability analysis of single-layer reticulated shells with stochastic imperfections. (1st October 2016)

• Record Type: Journal Article
• Authors: Liu, Huijuan; Zhang, Wei; Yuan, Hao
• Is Part Of: Engineering structures. Volume 124(2016:Oct. 01)
• Page Start: 473
• Page End: 479
• Publication Date: 2016-10-01
• Subjects: Single layer reticulated shell -- Stochastic imperfection modal superposition method -- Least favorable distribution of imperfections -- Critical buckling load -- Timoshenko beam; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2016.06.046
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Stochastic imperfections of the structure are included. Stochastic imperfection mode superposition method is proposed. Mode combination factor is not sensitive to its distribution types. Abstract: Buckling is one of the typical failure modes for single-layer reticulated shells (SLRSs). To prevent buckling, it is necessary to predict the critical buckling load and post-buckling path of the structure, and hence, structural stability analysis should be carried out. Unlike ideal structures, SLRSs with stochastic imperfections could experience different failure modes with varied post buckling paths. In the present paper, a stochastic imperfection modal superposition method is proposed for SLRS with stochastic imperfections. The SLRS structure is modeled using Timoshenko beams. Considering several possible buckling modes and random variables, Monte Carlo simulations are performed to analyze the superposition of buckling modes of the structure with stochastic imperfections. The modal combination factor is treated as a random variable and different distributions types are used for comparisons, such as uniform distribution, Gaussian distribution, T-Gaussian distribution, and triangular distribution. Based on parametric study results and comparisons with other traditional stability analysis method, the proposed method is found to provide good accuracy at considerably less computation cost.