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Article

Integrating Cluster Analysis into Multi-Criteria Decision Making for Maintenance Management of Aging Culverts

Section of Infrastructure Management, Federal Waterways Engineering and Research Institute, 76152 Karlsruhe, Germany
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Academic Editors: James Liou and Artūras Kaklauskas
Mathematics 2021, 9(20), 2549; https://doi.org/10.3390/math9202549
Received: 28 June 2021 / Revised: 16 August 2021 / Accepted: 20 August 2021 / Published: 12 October 2021
(This article belongs to the Special Issue Multi-Criteria Decision Making and Data Mining)
Negligence in relation to aging infrastructure systems could have unintended consequences and is therefore associated with a risk. The assessment of the risk of neglecting maintenance provides valuable information for decision making in maintenance management. However, infrastructure systems are interdependent and interconnected systems of systems characterized by hierarchical levels and a multiplicity of failure scenarios. Assessment methodologies are needed that can capture the multidimensional aspect of risk and simplify the risk assessment, while also improving the understanding and interpretation of the results. This paper proposes to integrate the multi-criteria decision analysis with data mining techniques to perform the risk assessment of aging infrastructures. The analysis is characterized by two phases. First, an intra failure scenario risk assessment is performed. Then, the results are aggregated to carry out an inter failure scenario risk assessment. A cluster analysis based on the k-medoids algorithm is applied to reduce the number of alternatives and identify those which dominate the decision problem. The proposed approach is applied to a system of aging culverts of the German waterways network. Results show that the procedure allows to simplify the analysis and improve communication with infrastructure stakeholders. View Full-Text
Keywords: data mining; k-medoids algorithm; maintenance backlog; multi-criteria decision analysis; risk-based maintenance; simple multi-attribute rating technique; swing weights; weighted sum model data mining; k-medoids algorithm; maintenance backlog; multi-criteria decision analysis; risk-based maintenance; simple multi-attribute rating technique; swing weights; weighted sum model
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MDPI and ACS Style

Marsili, F.; Bödefeld, J. Integrating Cluster Analysis into Multi-Criteria Decision Making for Maintenance Management of Aging Culverts. Mathematics 2021, 9, 2549. https://doi.org/10.3390/math9202549

AMA Style

Marsili F, Bödefeld J. Integrating Cluster Analysis into Multi-Criteria Decision Making for Maintenance Management of Aging Culverts. Mathematics. 2021; 9(20):2549. https://doi.org/10.3390/math9202549

Chicago/Turabian Style

Marsili, Francesca, and Jörg Bödefeld. 2021. "Integrating Cluster Analysis into Multi-Criteria Decision Making for Maintenance Management of Aging Culverts" Mathematics 9, no. 20: 2549. https://doi.org/10.3390/math9202549

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