Wind–Temperature Load Combination Coefficients for Long-Span Hybrid Cable-Stayed Suspension Bridge with Considerations of Load Correlation and Geometry Nonlinearity

This study focuses on quantifying wind–temperature load combination coefficients for long-span hybrid cable-stayed suspension bridges (HCSSBs) to overcome limitations of traditional methods in ignoring load correlation and geometry nonlinearity. A probabilistic framework is proposed to use site-specific load data to determine load combination coefficients, focusing on load correlation and geometric nonlinearity while assuming that stress reflects load effects and that 100-year samples are statistically representative. Long-sequence meteorological data, including wind and temperature measurements, were used to construct marginal and bivariate joint distributions, which characterize the randomness and correlation of wind and temperature loads. Load samples covering the design reference period were generated and validated via convergence tests. Four load scenarios (individual temperature, individual wind, linear superposition, and nonlinear coupling) were designed, and key control points are screened using indicators reflecting the comprehensive load effect EII¯, combined load proportion ζ, and nonlinear influence η. Based on stress responses of key control points, load combination coefficients were derived with probability modeling. A case study for a bridge with span length of 2300 m shows that the load combination coefficients for the main girder are 0.60 (east wind) and 0.59 (west wind), while they are 0.51 (east wind) and 0.58 (west wind) for the main tower. These results demonstrate that the proposed method enables the provision of rational load combination coefficients.