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Analysing the Conditions of Road Assets with a Network Thinking

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Chen, Weiwei 
Brilakis, Ioannis 

Abstract

Road transport is indispensable and requires improvements. The current road asset maintenance practice often treats defects as isolated entities and provides guidance on follow-up actions in fragmented documentation. Most of the previous research tended to focus on limited types of road assets, which did not cover defects across different types or consider holistically causes and repair strategies. This research explores relationships between five classes of information, summarizing various road objects into 66 assets, 48 defects, 28 repairs, 27 causes and 39 preventative treatments. Relationships in the network of road asset conditions are built by breaking paragraphs and descriptions of maintenance guidance in the United Kingdom into class-to-class relationships, checked and supplemented by standards from 8 overseas jurisdictions in 4 countries/regions. The network merges segregated road asset failures into a comprehensive network, which contributes to laying the ground rules in automating road maintenance and acts as a precursor to risk and reliability analyses for asset management.

Description

Keywords

Defect Relationships, Network Thinking, Road Assets, Road Maintenance

Journal Title

Proceedings of the 2023 European Conference on Computing in Construction and the 40th International CIB W78 Conference

Conference Name

2023 European Conference on Computing in Construction

Journal ISSN

2684-1150

Volume Title

Publisher

Publisher DOI

Sponsorship
Engineering and Physical Sciences Research Council (EP/S02302X/1)
The author (PH Lam) is funded by the UK Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training in Future Infrastructure and Built Environment: Resilience in a Changing World (FIBE2) [grant number EP/S02302X/1] and sponsored by the National Highways, Costain and Trimble Solutions. This work relates to Digital Roads, a project supported by the UK EPSRC [grant number EP/V056441/1].