An intelligent decision-making framework for asphalt pavement maintenance using the clustering-PageRank algorithm

IF 2.2 3区工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY

Engineering Optimization Pub Date : 2020-11-01 DOI:10.1080/0305215x.2019.1677636

Chengjia Han, Mingjing Fang, T. Ma, Hongyou Cao, Hao Peng

引用次数: 11

Abstract

With ever-increasing road mileages worldwide, more pavement deterioration and ageing present great challenges to the maintenance and rehabilitation (M&R) of road pavement. In this study, an intelligent decision-making framework is developed for pavement maintenance using the clustering-PageRank algorithm (CPRA) based on historical big data. The proposed model is applied to a 3.5 km pavement (500 road sections) and leads to recommendations for the optimal pavement maintenance plans with appropriate possibilities. The results indicate that seven plans are the same as those obtained by the experience-based maintenance approach, while the other three are similar. The framework is also verified by comparison with the experience-based maintenance activities and is found to have limited reliability when dealing with a small quantity of solutions. The method and results of this study are expected to serve as a reference for decision makers to make well-informed project decisions on the optimum M&R activities.

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基于聚类- pagerank算法的沥青路面维修智能决策框架

随着世界范围内道路里程的不断增加，越来越多的路面老化和老化给路面的维护和修复带来了巨大的挑战。本研究基于历史大数据，利用聚类- pagerank算法(CPRA)开发了路面养护智能决策框架。所提出的模型应用于3.5公里的路面(500个路段)，并给出了具有适当可能性的最佳路面维护计划的建议。结果表明，有7个方案与经验维护法得到的方案相同，其余3个方案相似。通过与基于经验的维护活动的比较，验证了该框架在处理少量解决方案时的可靠性有限。本研究的方法和结果有望为决策者提供参考，以便在最佳的M&R活动中做出明智的项目决策。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Engineering Optimization 管理科学-工程：综合

CiteScore

5.90

自引率

7.40%

发文量

审稿时长

3.5 months

期刊介绍： Engineering Optimization is an interdisciplinary engineering journal which serves the large technical community concerned with quantitative computational methods of optimization, and their application to engineering planning, design, manufacture and operational processes. The policy of the journal treats optimization as any formalized numerical process for improvement. Algorithms for numerical optimization are therefore mainstream for the journal, but equally welcome are papers which use the methods of operations research, decision support, statistical decision theory, systems theory, logical inference, knowledge-based systems, artificial intelligence, information theory and processing, and all methods which can be used in the quantitative modelling of the decision-making process. Innovation in optimization is an essential attribute of all papers but engineering applicability is equally vital. Engineering Optimization aims to cover all disciplines within the engineering community though its main focus is in the areas of environmental, civil, mechanical, aerospace and manufacturing engineering. Papers on both research aspects and practical industrial implementations are welcomed.