Nonlinear Aerodynamic Response and an Equivalent Static Wind-resistant Design for Anticlastic Conical Tensile Membranes

TL;DR

This study investigates the complex nonlinear aerodynamic behavior of anticlastic conical tensile membrane structures under wind loads, proposing an equivalent static design method using regression models and probabilistic analysis for practical application.

Contribution

It introduces a comprehensive nonlinear aerodynamic response analysis and a novel static wind-resistant design approach for conical tensile membranes, incorporating key parameters and probabilistic modeling.

Findings

Increasing prestress and rise-span ratio improve stiffness.

Higher roughness height increases peak responses due to turbulence.

The proposed static design method effectively simplifies nonlinear dynamic analysis.

Abstract

Conical Tensile Membrane Structure (TMS) is commonly used for aesthetics, economic design, high rain and snow loading. Such TMS shows complex aerodynamic behavior in presence of geometric nonlinearity, not adequately studied in the past. The aerodynamic responses of anticlastic conical TMS under random wind loading is presented herein along with an equivalent static wind resistant design approach. The stochastic wind loading on the TMS in the atmospheric boundary layer (ABL) is simulated via the Large Eddy simulation (LES); which is detailed in a previous study by the authors and hence not repeated here. The aerodynamic loading is then employed as input in conducting the nonlinear time history analyses considering open (i.e. without facade) and closed (with facade) TMS, supported by peripheral/radial cables. The influence of the key parameters (aerodynamic roughness height, the rise-span ratio of the TMS and the membrane prestress, notably) are demonstrated. Although increasing prestress and rise-to-span ratio enhances the stiffness of TMS, the former shows dominance. Increasing roughness height also lead to increased peak loading/responses by enhanced turbulence. An equivalent static wind-resistant design is presented via the Gust Response Factors (GRFs) and an additional Nonlinear Adjustment Factors (NAFs). These factors are presented systematically, encompassing alternative scenarios. Multi-linear regression models are presented for predictive modeling of these factors, along with a probabilistic analysis for their design values that can be employed in practice bypassing an involved nonlinear dynamic analysis.