Estimating the frequency of traffic overloading on road bridges

IF 7.4 2区 工程技术 Q1 ENGINEERING, CIVIL Journal of Traffic and Transportation Engineering-English Edition Pub Date : 2024-08-01 DOI:10.1016/j.jtte.2023.11.005
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Abstract

Load limits, which appear to be routinely exceeded by trucks, occasionally result in road bridge failures. Therefore, predicting failures is crucial for safeguarding road safety. Past studies have largely focused on forecasting bridge failure event probability using the reliability analysis method, whilst occasionally accounting for vehicular overloading effects. Only recently, a study has investigated design traffic overloading event frequency using generalised linear regression models (GLRMs), including a power component and negative binomial regressions (NBRs). However, as far as the authors know, artificial neural network models (ANNMs) have never been applied to this field. This paper is an attempt to fill in these gaps. First a frequency-based metric of traffic overloading was adopted as a driver of failure probability. Second, two alternative ‘frequency’ models were specified, calibrated, and validated. The former was based on a GLRM, the latter on ANNMs. Then, these models were compared using regression plots (RPs), measures of errors (MoEs) and the ratio between the number of observed vs predicted design load overcoming events to evaluate their performance. The models analysed more than 2 million weigh-in-motion (WIM) data records from a pilot station on a bridge on a heavily used ring road in Brescia (Italy). Results showed that ANNMs outperformed GLRMs. ANNMs have a higher correlation coefficient (between predicted and target frequencies), lower MoEs, and a closer-to-unity ratio (between predicted and target frequencies). These findings may increase prediction accuracy of design traffic overloading events and give road authorities more effective traffic management to protect bridges from load hazards.

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公路桥梁交通超载频率估算
载荷限制似乎经常被卡车超过,偶尔会导致公路桥梁垮塌。因此,预测故障对于保障道路安全至关重要。过去的研究主要集中在使用可靠性分析方法预测桥梁故障事件的概率,偶尔也会考虑车辆超载的影响。直到最近,才有一项研究使用广义线性回归模型(GLRMs)(包括幂成分和负二项回归(NBRs))对设计交通超载事件频率进行了调查。然而,据作者所知,人工神经网络模型(ANNM)从未应用于这一领域。本文试图填补这些空白。首先,采用基于频率的交通超载度量作为故障概率的驱动因素。其次,确定、校准和验证了两个可供选择的 "频率 "模型。前者基于 GLRM,后者基于 ANNM。然后,使用回归图 (RP)、误差度量 (MoE) 以及观察到的设计荷载超限事件数量与预测的设计荷载超限事件数量之间的比率对这些模型进行比较,以评估它们的性能。这些模型分析了 200 多万条称重运动(WIM)数据记录,这些数据来自意大利布雷西亚使用率很高的环形公路上一座桥梁上的试验站。结果表明,ANNMs 的性能优于 GLRMs。ANNM 具有更高的相关系数(预测频率与目标频率之间)、更低的 MoE 和更接近的统一比(预测频率与目标频率之间)。这些发现可能会提高设计交通超载事件的预测准确性,为道路管理部门提供更有效的交通管理,保护桥梁免受荷载危害。
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来源期刊
CiteScore
13.60
自引率
6.30%
发文量
402
审稿时长
15 weeks
期刊介绍: The Journal of Traffic and Transportation Engineering (English Edition) serves as a renowned academic platform facilitating the exchange and exploration of innovative ideas in the realm of transportation. Our journal aims to foster theoretical and experimental research in transportation and welcomes the submission of exceptional peer-reviewed papers on engineering, planning, management, and information technology. We are dedicated to expediting the peer review process and ensuring timely publication of top-notch research in this field.
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