Performance evaluation of extreme value prediction methods for bridge traffic load effects

• Published in: Advances in bridge engineering Vol. 6; no. 1; pp. 27 - 20
• Main Authors: Xu, Miaomiao, Zhou, Xiao-Yi, Shen, Jie, Ding, Deliang, Cao, Sugong, Cai, C. S.
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.12.2025; Springer Nature B.V; SpringerOpen
• Subjects: Accuracy; Bridge loads; Bridge traffic load effect; Characteristic value; Civil Engineering; Datasets; Disaster Prevention and Mitigation for Coastal Bridges; Distribution functions; Distribution parameter; Engineering; Extreme value theory; Extreme values; Level crossings; Methods; Normal distribution; Original Innovation; Parameter estimation; Performance evaluation; Prediction method; Random variables; Traffic; Extreme value theory; Prediction method; Distribution parameter; Bridge traffic load effect; Characteristic value
• ISSN: 2662-5407; 2662-5407
• DOI: 10.1186/s43251-025-00175-3

This study investigates six types of prediction methods for estimating extreme bridge traffic load effects, aiming to establish a correlation between prediction accuracy and data quality. Accurately determining the distribution functions of maximum values is crucial for assessing bridge safety under traffic loads. Methods including the Peaks Over Threshold, the block maxima approach, fitting to a Normal distribution, and the Rice formula based level crossing method, are investigated. Additionally, Bayesian Updating and Predictive Likelihood techniques, integrated with the block maxima approach, are explored. The performance of these methods is assessed using two distinct datasets. The first dataset is generated from a known distribution, allowing the estimated distribution parameters and extreme values derived from each method to be compared with the true values. The analysis is then extended to more realistic scenarios, where long-run simulations provide benchmark results for evaluating the accuracy of each method. Based on the findings, recommendations are provided for selecting the most suitable prediction method, considering factors such as sample size, time interval, and the type of load effect. This work offers practical insights for improving the reliability of extreme value prediction methods in bridge safety assessments.