Performance evaluation of extreme value prediction methods for bridge traffic load effects
Abstract 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 saf...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-08-01
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| Series: | Advances in Bridge Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43251-025-00175-3 |
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| _version_ | 1849342343639990272 |
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| author | Miaomiao Xu Xiao-Yi Zhou Jie Shen Deliang Ding Sugong Cao C. S. Cai |
| author_facet | Miaomiao Xu Xiao-Yi Zhou Jie Shen Deliang Ding Sugong Cao C. S. Cai |
| author_sort | Miaomiao Xu |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-03dfe68ee5384b37860045932ad3d8f4 |
| institution | Kabale University |
| issn | 2662-5407 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Advances in Bridge Engineering |
| spelling | doaj-art-03dfe68ee5384b37860045932ad3d8f42025-08-20T03:43:25ZengSpringerOpenAdvances in Bridge Engineering2662-54072025-08-016112010.1186/s43251-025-00175-3Performance evaluation of extreme value prediction methods for bridge traffic load effectsMiaomiao Xu0Xiao-Yi Zhou1Jie Shen2Deliang Ding3Sugong Cao4C. S. Cai5Department of Architectural Engineering, Changzhou Vocational Institute of EngineeringDepartment of Bridge Engineering, School of Transportation, Southeast UniversityChangzhou Architectural Research Institute Group Co., Ltd.China Construction Seventh Engineering Division Corp.. Ltd.Zhejiang Scientific Research Institute of TransportDepartment of Bridge Engineering, School of Transportation, Southeast UniversityAbstract 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.https://doi.org/10.1186/s43251-025-00175-3Extreme value theoryBridge traffic load effectPrediction methodCharacteristic valueDistribution parameter |
| spellingShingle | Miaomiao Xu Xiao-Yi Zhou Jie Shen Deliang Ding Sugong Cao C. S. Cai Performance evaluation of extreme value prediction methods for bridge traffic load effects Advances in Bridge Engineering Extreme value theory Bridge traffic load effect Prediction method Characteristic value Distribution parameter |
| title | Performance evaluation of extreme value prediction methods for bridge traffic load effects |
| title_full | Performance evaluation of extreme value prediction methods for bridge traffic load effects |
| title_fullStr | Performance evaluation of extreme value prediction methods for bridge traffic load effects |
| title_full_unstemmed | Performance evaluation of extreme value prediction methods for bridge traffic load effects |
| title_short | Performance evaluation of extreme value prediction methods for bridge traffic load effects |
| title_sort | performance evaluation of extreme value prediction methods for bridge traffic load effects |
| topic | Extreme value theory Bridge traffic load effect Prediction method Characteristic value Distribution parameter |
| url | https://doi.org/10.1186/s43251-025-00175-3 |
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