Wind-load fragility analysis of monopole towers by Layered Stochastic-Approximation-Monte-Carlo method. (1st November 2018)

• Record Type: Journal Article
• Authors: Giaccu, Gian Felice; Caracoglia, Luca
• Is Part Of: Engineering structures. Volume 174(2018)
• Page Start: 462
• Page End: 477
• Publication Date: 2018-11-01
• Subjects: Slender structures -- Wind loading uncertainties -- Along-wind dynamic response -- Stochastic approximation -- Monte Carlo method -- First-order reliability method; 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.2018.07.081
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Wind load analysis of slender monopole towers is studied. Uncertainties related to wind loads and structural properties are considered. A Layered Stochastic-Approximation-Monte-Carlo is used to estimate fragility curves. Monte Carlo simulations are employed to verify the LSAMC results. The proposed algorithm is a viable alternative to standard Monte Carlo sampling. Abstract: This paper describes a novel numerical algorithm for the simulation of the along-wind dynamic response of a prototype of slender towers under turbulent winds, using a Layered Stochastic Approximation Monte Carlo algorithm (LSAMC). The proposed algorithm is applied to derive the statistics of the dynamic response in the presence of uncertainties in the structural properties and in the wind loading. Standard "brute force" Monte-Carlo methods are also used for validating the LSAMC results. The proposed methodology efficiently estimates structural fragility curves under extreme wind loads. The methodology enables a significant speedup in the computing time compared to standard Monte Carlo sampling. Furthermore, it is demonstrated that accuracy in the estimation of structural fragility curves is superior to ordinary reliability methods (e.g. "First-order reliability methods" or FORM).