Yuliang He1, Weihong Lou1, Da Hang2, Youhua Su3,*
Structural Durability & Health Monitoring, Vol.19, No.4, pp. 887-902, 2025, DOI:10.32604/sdhm.2025.062070
- 30 June 2025
Abstract The computational accuracy and efficiency of modeling the stress spectrum derived from bridge monitoring data significantly influence the fatigue life assessment of steel bridges. Therefore, determining the optimal stress spectrum model is crucial for further fatigue reliability analysis. This study investigates the performance of the REBMIX algorithm in modeling both univariate (stress range) and multivariate (stress range and mean stress) distributions of the rain-flow matrix for a steel arch bridge, using Akaike’s Information Criterion (AIC) as a performance metric. Four types of finite mixture distributions—Normal, Lognormal, Weibull, and Gamma—are employed to model the stress range. More >