{"title":"公路桥梁网络极端交通负荷的分层贝叶斯建模","authors":"","doi":"10.1016/j.strusafe.2024.102503","DOIUrl":null,"url":null,"abstract":"<div><p>The road network consists of bridges of various lengths and configurations, all of which require accurate prediction of traffic load within their lifetime. However, current prediction methods are limited to modeling and predicting traffic load for a handful of individual bridges only; no method can simultaneously model and predict the traffic load of all bridges within an entire road network. Further, conventional models neglect the information that exists in the traffic load effect data established for different bridges, leading to large estimation uncertainties for each bridge and load effect examined. This study proposes a hierarchical Bayesian model that can estimate the traffic load effect of multiple bridges simultaneously, and subsequently create predictions for the remaining (unexamined) bridges within the road network. The proposed model is demonstrated using the traffic load data and influence lines used in the background study for the Eurocode 1 Load Model 1. The results show significant reductions in prediction uncertainties, better fits as measured by leave-one-out statistics, more robust fits against extremes, and the emergence of intuitive correlation structures between different bridges’ traffic loads that are absent in conventional models. This paper also presents a potential new strategy to reduce estimation uncertainty, and a method to predict parameters and return levels for bridges across an entire network made possible by the proposed hierarchical Bayesian model.</p></div>","PeriodicalId":21978,"journal":{"name":"Structural Safety","volume":null,"pages":null},"PeriodicalIF":5.7000,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167473024000742/pdfft?md5=f14dc58ccd8879f52aeaf0ae6661d703&pid=1-s2.0-S0167473024000742-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Hierarchical Bayesian modeling of highway bridge network extreme traffic loading\",\"authors\":\"\",\"doi\":\"10.1016/j.strusafe.2024.102503\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The road network consists of bridges of various lengths and configurations, all of which require accurate prediction of traffic load within their lifetime. 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The results show significant reductions in prediction uncertainties, better fits as measured by leave-one-out statistics, more robust fits against extremes, and the emergence of intuitive correlation structures between different bridges’ traffic loads that are absent in conventional models. 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引用次数: 0
摘要
公路网由不同长度和结构的桥梁组成,所有这些桥梁都需要对其使用寿命内的交通负荷进行准确预测。然而,目前的预测方法仅限于对少数单座桥梁的交通荷载进行建模和预测,没有一种方法能同时对整个道路网络中所有桥梁的交通荷载进行建模和预测。此外,传统模型忽略了为不同桥梁建立的交通荷载效应数据中存在的信息,导致对每座桥梁和荷载效应的估算存在较大的不确定性。本研究提出了一种分层贝叶斯模型,可以同时估算多座桥梁的交通荷载效应,并随后对道路网络中剩余(未考察)的桥梁进行预测。建议的模型使用 Eurocode 1 Load Model 1 背景研究中使用的交通荷载数据和影响线进行了演示。结果表明,预测的不确定性明显降低,通过留一统计量测量的拟合效果更好,对极端情况的拟合更稳健,不同桥梁的交通荷载之间出现了传统模型所没有的直观相关结构。本文还提出了一种降低估计不确定性的潜在新策略,以及一种预测整个网络中桥梁参数和回报水平的方法,该方法通过所提出的分层贝叶斯模型得以实现。
Hierarchical Bayesian modeling of highway bridge network extreme traffic loading
The road network consists of bridges of various lengths and configurations, all of which require accurate prediction of traffic load within their lifetime. However, current prediction methods are limited to modeling and predicting traffic load for a handful of individual bridges only; no method can simultaneously model and predict the traffic load of all bridges within an entire road network. Further, conventional models neglect the information that exists in the traffic load effect data established for different bridges, leading to large estimation uncertainties for each bridge and load effect examined. This study proposes a hierarchical Bayesian model that can estimate the traffic load effect of multiple bridges simultaneously, and subsequently create predictions for the remaining (unexamined) bridges within the road network. The proposed model is demonstrated using the traffic load data and influence lines used in the background study for the Eurocode 1 Load Model 1. The results show significant reductions in prediction uncertainties, better fits as measured by leave-one-out statistics, more robust fits against extremes, and the emergence of intuitive correlation structures between different bridges’ traffic loads that are absent in conventional models. This paper also presents a potential new strategy to reduce estimation uncertainty, and a method to predict parameters and return levels for bridges across an entire network made possible by the proposed hierarchical Bayesian model.
期刊介绍:
Structural Safety is an international journal devoted to integrated risk assessment for a wide range of constructed facilities such as buildings, bridges, earth structures, offshore facilities, dams, lifelines and nuclear structural systems. Its purpose is to foster communication about risk and reliability among technical disciplines involved in design and construction, and to enhance the use of risk management in the constructed environment