English

Reliability-Based Collapse Assessment of Wind-Excited Steel Structures within Performance-Based Wind Engineering

Computational Engineering, Finance, and Science 2022-07-29 v1

Abstract

As inelastic design for wind is embraced by the engineering community, there is an increasing demand for computational tools that enable the investigation of the nonlinear behavior of wind-excited structures and subsequent development of performance criteria. To address this need, a probabilistic collapse assessment framework for steel structures is proposed in this paper. The framework is based on the integration of a high-fidelity fiber-based nonlinear structural modeling environment with a wind-tunnel-informed stochastic wind load model to perform nonlinear time history analysis. General uncertainty is propagated using a stratified sampling scheme enabling the efficient estimation of reliabilities associated with rare events. The adopted models for simulating high-fidelity nonlinear structural behavior were found, in general, to be adequate for capturing phenomena, including progressive yielding, buckling, and low-cycle fatigue, that are essential for wind induced collapse analysis. In particular, the adopted fatigue model was found to be capable of predicting damage and potential fiber/section fracture associated with non-fully reversing stress-strain cycles that are characteristic of wind loading. Through illustration on a 45-story archetype steel building, critical discussions on the types of observed collapse mechanisms, the difference between alongwind and acrosswind nonlinear behavior, reliabilities associated with first yield, and collapse are presented. A probabilistic description of the residual and peak story drifts is also provided through development of fragility functions.

Keywords

Cite

@article{arxiv.2207.14156,
  title  = {Reliability-Based Collapse Assessment of Wind-Excited Steel Structures within Performance-Based Wind Engineering},
  author = {Srinivasan Arunachalam and Seymour M. J. Spence},
  journal= {arXiv preprint arXiv:2207.14156},
  year   = {2022}
}

Comments

42 pages, 16 figures Forthcoming. ASCE Journal of Structural Engineering, 2022

R2 v1 2026-06-25T01:18:27.236Z