Robustness of the Multi-Attribute Utility Model for Bridge Maintenance Planning

Authors

  • Zaharah Allah Bukhsh Dept of Construction Management and Engineering, Faculty of Engineering Technology, University of Twente, Enschede, The Netherlands
  • Irina Stipanovic Dept of Construction Management and Engineering, Faculty of Engineering Technology, University of Twente, Enschede, The Netherlands Infra Plan Consulting Ltd., Zagreb, Croatia
  • Sandra Skaric Palic Infra Plan Consulting Ltd., Zagreb, Croatia
  • Giel Klanker Rijkswaterstaat Ministry of Infrastructure and the Environment, Utrecht, Netherlands

DOI:

https://doi.org/10.7250/bjrbe.2018-13.425

Keywords:

bridge ranking, multi-criteria decision making, performance goals, risk tolerance value, sensitivity analysis

Abstract

Optimisation of maintenance planning is an essential part of bridge management. With the purpose to support maintenance planning, a multi- objective decision-making model is introduced in this paper. The model is based on multi-attribute utility theory, which is used for the optimisation process when multiple performance goals have to be taken into account. In the model, there are several parameters, which are freely chosen by the decision maker. The model is applied to the inventory of 22 bridges, where four Key Performance Indicators were determined for four performance aspects:  reliability, availability, costs and environment. A sensitivity analysis is performed by changing risk tolerance parameter and attribute weights to determine the robustness of the model. The Multi-Attribute Utility model and sensitivity analysis presented in this paper will help decision-makers to examine the robustness of the optimal solution by dynamically changing the critical parameters.

References

Allah Bukhsh, Z., Oslakovic, I. S., Klanker, G., Hoj, N. P., Imam, B., & Xenidis, Y. (2017). Multi-criteria decision making: AHP method applied for network bridge prioritization. In Joint COST TU1402–COST TU1406–IABSE WC1 Workshop: The value of Structural Health Monitoring for the reliable Bridge Management http://www.tu1406.eu/wpcontent/uploads/2017/05/TU1406_ ZAGREB_EBOOK. pdf.

Allah Bukhsh, Z., Saeed, A., & Stipanovic I. (2018). A machine learning approach for maintenance prediction of railway assets, In Proc of 7th Transport Research Arena TRA 2018, April 16-19, 2018, Vienna, Austria

Allah Bukhsh, Z., Stipanovic, I., Klanker, G., O’Connor, A., & Doree, A. G. (2018). Network level bridges maintenance planning using Multi-Attribute Utility Theory. Structure and infrastructure engineering, 1-14. https://doi.org/10.1080/15732479.2017.1414858

Borgonovo, E., & Cillo, A. (2017). Deciding with thresholds: Importance measures and value of information. Risk Analysis, 37(10), 1828-1848. https://doi.org/10.1111/risa.12732

Das, P., Micic, T., & Chryssanthopoulos, M. (1999). Reliability-based Assessment of Highway Bridges.

Egger, M. (2012). Benchmarking of Expenditures and Practices of Maintenance and Operation (BEXPRAC). Procedia-Social and Behavioral Sciences, 48, 1733-1742. https://doi.org/10.1016/j.sbspro.2012.06.1148

ERA-NET ROAD (2012, November). Inventory Bridge Management practices, Asset Service Condition Assessment Methodology (ASCAM), Deliverable No. 3. ASCAM-R3, Final report.

Ishizaka, A., & Nemery, P. (2013). Multi-criteria decision analysis: methods and software. John Wiley & Sons.

Keeney, R. L., & Raiffa, H. (1993). Decisions with multiple objectives: preferences and value trade-offs. Cambridge university press.

Núñez, A., Hendriks, J., Li, Z., De Schutter, B., & Dollevoet, R. (2014, October). Facilitating maintenance decisions on the Dutch railways using big data: The ABA case study. In Big Data (Big Data), 2014 IEEE International Conference on (pp. 48-53). IEEE. https://doi.org/10.1109/BigData.2014.7004431

Strauss, A., & Mandić Ivanković, A. (2016). Performance indicators for roadway bridges of COST Action TU1406. WG1 Technical report). ISBN 978-3-900932-41-1.

Thevenot, H. J., Steva, E. D., Okudan, G. E., & Simpson, T. W. (2006, January). A multi-attribute utility theory-based approach to product line consolidation and selection. In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. 441-450). American Society of Mechanical Engineers. https://doi.org/10.1115/DETC2006-99506

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Published

21.12.2018

How to Cite

Bukhsh, Z. A., Stipanovic, I., Palic, S. S., & Klanker, G. (2018). Robustness of the Multi-Attribute Utility Model for Bridge Maintenance Planning. The Baltic Journal of Road and Bridge Engineering, 13(4), 404-415. https://doi.org/10.7250/bjrbe.2018-13.425