Open Access

ARTICLE

Fast Mixture Distribution Optimization for Rain-Flow Matrix of a Steel Arch Bridge by REBMIX Algorithm

Yuliang He¹, Weihong Lou¹, Da Hang², Youhua Su^3,*

1 School of Civil Engineering, Shaoxing University, Shaoxing, 312000, China
2 Department of Civil Engineering, Zhejiang University, Hangzhou, 310058, China
3 Department of Civil Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

* Corresponding Author: Youhua Su. Email:

(This article belongs to the Special Issue: Advanced Data Mining in Bridge Structural Health Monitoring)

Structural Durability & Health Monitoring 2025, 19(4), 887-902. https://doi.org/10.32604/sdhm.2025.062070

Received 09 December 2024; Accepted 23 January 2025; Issue published 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. Additionally, mixed distributions, including Normal-Normal, Lognormal-Normal, Weibull-Normal, and Gamma-Normal, are utilized to model the joint distribution of stress range and mean stress. The REBMIX algorithm estimates the number of components, component weights, and component parameters for each candidate finite mixture distribution. The results demonstrate that the REBMIX algorithm-based mixture parameter estimation approach effectively identifies the optimal distribution based on AIC values. Furthermore, the algorithm exhibits superior computational efficiency compared to traditional methods, making it highly suitable for practical applications.

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Keywords

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