Predictive Maintenance of Road Networks via Spatial Clustering and Scheduling: a Data-Driven Approach
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| Format: | Recurso digital |
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2026
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| _version_ | 1866901543100350464 |
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| author | Papageorgiou, Dimitrios |
| author_facet | Papageorgiou, Dimitrios |
| contents | <p>This presentation outlines the results on prediction maintenance of road network infrastructure, as part of the research pursued within the <a href="https://evoroads-project.eu/" target="_blank" rel="noopener">EvoRoads project</a> (Horizon Europe, Grant Agreement ID: <a href="https://cordis.europa.eu/project/id/101147850" target="_blank" rel="noopener">101147850</a>). The prognostic/predictive solution comprises a data-driven framework that integrates predictive analytics, spatial clustering, and optimization techniques to support more effective maintenance strategies.</p> <p>The presentation, delivered at the <strong>6th International Conference on Control and Fault-Tolerant Systems</strong> (SysTol 2025), held in Ayia Napa, Cyprus (6-8 October 2025), outlines:</p> <ul> <li>Tools for evaluating individual pavement condition metrics that incorporate threshold-based maintenance triggers.</li> <li>A density-based clustering algorithm that groups identified maintenance needs into geographically coherent clusters.</li> <li>A cluster-level optimisation algorithm that prioritise interventions within budget constraints.</li> <li>A cascaded architecture of two modules (prediction-optimisation) that enhances resource allocation, minimises risk in high-priority zones and enables more proactive and spatially-informed infrastructure management.</li> <li>Experimental results that highlight the effectiveness of the proposed approach.</li> </ul> <p>Conference official website: [<a href="https://www.kios.ucy.ac.cy/systol25/" target="_blank" rel="noopener">click here</a>]<br>Speaker: <a href="https://www.linkedin.com/in/dimitrios-papageorgiou-32701865/" target="_blank" rel="noopener">Dimitrios Papageorgiou</a> from <a href="https://electro.dtu.dk/" target="_blank" rel="noopener">DTU</a></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18134782 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Predictive Maintenance of Road Networks via Spatial Clustering and Scheduling: a Data-Driven Approach Papageorgiou, Dimitrios Predictive maintenance Road network infrastructure Predictive analytics Spatial clustering Pavement condition evaluation Asset management Prognostic analysis <p>This presentation outlines the results on prediction maintenance of road network infrastructure, as part of the research pursued within the <a href="https://evoroads-project.eu/" target="_blank" rel="noopener">EvoRoads project</a> (Horizon Europe, Grant Agreement ID: <a href="https://cordis.europa.eu/project/id/101147850" target="_blank" rel="noopener">101147850</a>). The prognostic/predictive solution comprises a data-driven framework that integrates predictive analytics, spatial clustering, and optimization techniques to support more effective maintenance strategies.</p> <p>The presentation, delivered at the <strong>6th International Conference on Control and Fault-Tolerant Systems</strong> (SysTol 2025), held in Ayia Napa, Cyprus (6-8 October 2025), outlines:</p> <ul> <li>Tools for evaluating individual pavement condition metrics that incorporate threshold-based maintenance triggers.</li> <li>A density-based clustering algorithm that groups identified maintenance needs into geographically coherent clusters.</li> <li>A cluster-level optimisation algorithm that prioritise interventions within budget constraints.</li> <li>A cascaded architecture of two modules (prediction-optimisation) that enhances resource allocation, minimises risk in high-priority zones and enables more proactive and spatially-informed infrastructure management.</li> <li>Experimental results that highlight the effectiveness of the proposed approach.</li> </ul> <p>Conference official website: [<a href="https://www.kios.ucy.ac.cy/systol25/" target="_blank" rel="noopener">click here</a>]<br>Speaker: <a href="https://www.linkedin.com/in/dimitrios-papageorgiou-32701865/" target="_blank" rel="noopener">Dimitrios Papageorgiou</a> from <a href="https://electro.dtu.dk/" target="_blank" rel="noopener">DTU</a></p> |
| title | Predictive Maintenance of Road Networks via Spatial Clustering and Scheduling: a Data-Driven Approach |
| topic | Predictive maintenance Road network infrastructure Predictive analytics Spatial clustering Pavement condition evaluation Asset management Prognostic analysis |
| url | https://doi.org/10.5281/zenodo.18134782 |