Network level bridges maintenance planning using Multi-Attribute Utility Theory

• Published in: Structure and infrastructure engineering Vol. 15; no. 7; pp. 872 - 885
• Main Authors: Allah Bukhsh, Zaharah, Stipanovic, Irina, Klanker, Giel, O' Connor, Alan, Doree, Andre G.
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 03.07.2019
• Subjects: multi criteria decision-making; Multi-Attribute Utility Theory; multi-objective optimisation; network analysis; performance aspects; Performance indicators; road bridges
• ISSN: 1573-2479; 1744-8980
• DOI: 10.1080/15732479.2017.1414858

Bridge infrastructure managers are facing multiple challenges to improve the availability and serviceability of ageing infrastructure, while the maintenance planning is constrained by budget restrictions. Many research efforts are ongoing, for the last few decades, ranging from development of bridge management system, decision support tools, optimisation models, life cycle cost analysis, etc. Since transport infrastructures are deeply embedded in society, they are not only subject to technical requirements, but are required to meet the requirements of societal and economic developments. Therefore, bridge maintenance planning should accommodate multiple performance goals which need to be quantified by various performance indicators. In this paper, an application of Multi-Attribute Utility Theory (MAUT) for bridge maintenance planning is illustrated with a case study of bridges from the Netherlands road network. MAUT seeks to optimise multiple objectives by suggesting a trade-off among them and finally assigns a ranking to the considered bridges. Moreover, utility functions of MAUT appropriately account for the involved uncertainty and risk attitude of infrastructure managers. The main contribution of this study is in presenting a proof-of-concept on how MAUT provides a systematic approach to improve the decision-making of maintenance planning by making use of available data, accommodating multiple performance goals, their uncertainty, and preferences of infrastructure managers.