Hybrid Nonlinear and Machine Learning Methods for Analyzing Factors Influencing the Performance of Large-Scale Transport Infrastructure
Hybrid Nonlinear and Machine Learning Methods for Analyzing Factors Influencing the Performance of Large-Scale Transport Infrastructure
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DOI：
10.1109/tits.2021.3112458
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发表时间：
2021-09-24
影响因子：
8.5
通讯作者：
Karunaratne, Lalinda
中科院分区：
文献类型：
--
作者：
Song, Yongze;Wu, Peng;Karunaratne, Lalinda
战略性维护对道路基础设施的可持续发展至关重要。对道路养护效果的准确估计可以为养护战略的评估、预算和资源的合理分配提供支持。道路劣化受复杂因素的影响，但对综合劣化因素的准确调查是有限的。该研究开发了一种动态权衡模型(DTOM)，这是一种非线性和机器学习的混合方法，用于量化因素在时间上的变化影响，并在网络级别上检查维护效果。路面老化的因素分为三类：(I)路面平整度的历史观测值；(Ii)路面老化程度；及(Iii)交通、气候及环境因素。分别用非线性最小二乘回归、连接点回归和随机森林模型估计了它们对人行道的影响。车载激光扫描仪监测的高分辨率恶化数据是从2007年至2018年收集的西澳大利亚州大规模空间公路网的数据。结果表明，表面处理和修复是战略减少劣化的关键。12年的维修活动使整个公路网的路面平整度降低了7.5%，路面使用性能(平整度低于2.085 IRI的道路百分比)提高了14.5%。DTOM在准确评估基础设施维护影响和预测恶化情况方面具有巨大潜力。
Strategic maintenance is essential for sustainable road infrastructure development. Accurate estimation of road maintenance effects can support the assessment of maintenance strategies and reasonable allocation of budgets and resources. Road deterioration is affected by sophisticated factors, but accurate investigation of the integrated deterioration factors is limited. This study developed a dynamic trade-off model (DTOM), a hybrid nonlinear and machine learning method, for quantifying temporally varied impacts of factors and examining maintenance effects at the network level. Pavement deterioration factors are classified into three categories: (i) historical observations of roughness, (ii) pavement age, and (iii) traffic, climate and environment factors. Their respective impacts on pavements are estimated using a non-linear least square regression, a joinpoint regression and a random forest model, respectively. Vehicle-based laser scanner monitored high-resolution deterioration data was collected for a large spatial scale road network in Western Australia from 2007 to 2018. Results show that the resurfacing and rehabilitation are essential for strategic reduction of deterioration. Twelve-year maintenance activities reduced the distress of roughness by 7.5% and increased road performance (the percentage of roads with roughness lower than 2.085 IRI) by 14.5% for the whole road network. The DTOM has great potentials in accurately assessing infrastructure maintenance effects and predicting deterioration scenarios.