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

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 along-wind and across-wind 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. (C) 2022 American Society of Civil Engineers.

Automated summary

This paper proposes a probabilistic collapse assessment framework for steel structures, which integrates high-fidelity nonlinear structural modeling and wind-tunnel-informed stochastic wind load model to perform nonlinear time history analysis. The framework efficiently estimates reliabilities associated with rare events and provides critical discussions on collapse mechanisms, nonlinear behavior, and fragility functions of residual and peak story drifts.