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A platform for research: civil engineering, architecture and urbanism
Extreme Value Prediction of Traffic Loads Using the Average Conditional Exceedance Rate Method
https://orcid.org/https://orcid.org/0000-0002-2947-8660
https://orcid.org/https://orcid.org/0000-0003-0032-2512
https://orcid.org/https://orcid.org/0000-0002-7700-8794
https://orcid.org/http://orcid.org/0000-0003-1633-3718
https://orcid.org/https://orcid.org/0000-0001-6663-1022
https://orcid.org/http://orcid.org/0000-0003-4847-4902
An efficient prediction of the extreme value of traffic loads is crucial for the structural design, reliability evaluation, maintenance planning, and further life-cycle cost analysis of bridges. In this work, a novel method is proposed for predicting the appropriate extreme traffic load distribution. Specifically, the average conditional exceedance rate (ACER) statistical model is estimated from the historical traffic loads which was collected through a weigh-in-motion system installed in toll stations. The basic idea of the ACER approach lies in the introduction of a cascade of conditioning approximations and the average exceedance rate to capture the dependence effects and obtain the data tail, the trend features of which are fitted with a similar Gumbel distribution function and extrapolated to the concerned level. An illustration case dealing with traffic loads using the ACER strategy is presented, the extreme value and confidence interval (CI) in any return period can be predicted by application of this approach. Furthermore, the peaks-over-threshold (POT) method based on the asymptotic extreme theory is also applied to illustrate the advantages of the ACER method. The ACER method has advantages in analyzing extreme traffic loads, with good robustness and the ability to handle extreme value prediction for different sampling strategies, it also can produce more accurate confidence intervals and predicts consistent extreme values. The study results are expected to help accurately determine traffic loads and ensure safety in bridge engineering.
Extreme Value Prediction of Traffic Loads Using the Average Conditional Exceedance Rate Method
https://orcid.org/https://orcid.org/0000-0002-2947-8660
https://orcid.org/https://orcid.org/0000-0003-0032-2512
https://orcid.org/https://orcid.org/0000-0002-7700-8794
https://orcid.org/http://orcid.org/0000-0003-1633-3718
https://orcid.org/https://orcid.org/0000-0001-6663-1022
https://orcid.org/http://orcid.org/0000-0003-4847-4902
An efficient prediction of the extreme value of traffic loads is crucial for the structural design, reliability evaluation, maintenance planning, and further life-cycle cost analysis of bridges. In this work, a novel method is proposed for predicting the appropriate extreme traffic load distribution. Specifically, the average conditional exceedance rate (ACER) statistical model is estimated from the historical traffic loads which was collected through a weigh-in-motion system installed in toll stations. The basic idea of the ACER approach lies in the introduction of a cascade of conditioning approximations and the average exceedance rate to capture the dependence effects and obtain the data tail, the trend features of which are fitted with a similar Gumbel distribution function and extrapolated to the concerned level. An illustration case dealing with traffic loads using the ACER strategy is presented, the extreme value and confidence interval (CI) in any return period can be predicted by application of this approach. Furthermore, the peaks-over-threshold (POT) method based on the asymptotic extreme theory is also applied to illustrate the advantages of the ACER method. The ACER method has advantages in analyzing extreme traffic loads, with good robustness and the ability to handle extreme value prediction for different sampling strategies, it also can produce more accurate confidence intervals and predicts consistent extreme values. The study results are expected to help accurately determine traffic loads and ensure safety in bridge engineering.
Extreme Value Prediction of Traffic Loads Using the Average Conditional Exceedance Rate Method
https://orcid.org/https://orcid.org/0000-0002-2947-8660
https://orcid.org/https://orcid.org/0000-0003-0032-2512
https://orcid.org/https://orcid.org/0000-0002-7700-8794
https://orcid.org/http://orcid.org/0000-0003-1633-3718
https://orcid.org/https://orcid.org/0000-0001-6663-1022
https://orcid.org/http://orcid.org/0000-0003-4847-4902
An efficient prediction of the extreme value of traffic loads is crucial for the structural design, reliability evaluation, maintenance planning, and further life-cycle cost analysis of bridges. In this work, a novel method is proposed for predicting the appropriate extreme traffic load distribution. Specifically, the average conditional exceedance rate (ACER) statistical model is estimated from the historical traffic loads which was collected through a weigh-in-motion system installed in toll stations. The basic idea of the ACER approach lies in the introduction of a cascade of conditioning approximations and the average exceedance rate to capture the dependence effects and obtain the data tail, the trend features of which are fitted with a similar Gumbel distribution function and extrapolated to the concerned level. An illustration case dealing with traffic loads using the ACER strategy is presented, the extreme value and confidence interval (CI) in any return period can be predicted by application of this approach. Furthermore, the peaks-over-threshold (POT) method based on the asymptotic extreme theory is also applied to illustrate the advantages of the ACER method. The ACER method has advantages in analyzing extreme traffic loads, with good robustness and the ability to handle extreme value prediction for different sampling strategies, it also can produce more accurate confidence intervals and predicts consistent extreme values. The study results are expected to help accurately determine traffic loads and ensure safety in bridge engineering.
Extreme Value Prediction of Traffic Loads Using the Average Conditional Exceedance Rate Method
KSCE J Civ Eng
Zhang, Liping (author) / Bu, Jianqing (author) / Zhou, Liming (author) / Cao, Wenlong (author) / Zhao, Cunbao (author) / Chai, Wei (author)
KSCE Journal of Civil Engineering ; 27 ; 5256-5267
2023-12-01
12 pages
Article (Journal)
Electronic Resource
English
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