Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Daily Overview |
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SES 1-4-3: Asset Management Location: HSB2 Session Chair: Franz Tscheikner-Gratl Session Chair: Emma Madeleine Girot | |
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5:30pm - 5:45pm
Graph Neural Networks and Random Forests for Report-Based Failure Prediction in Sewage Pipes 1: Department of Mathematics, Technion - Israel Institute of Technology, Israel; 2: Department of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Israel; 3: KWB Kompetenzzentrum Wasser Berlin gemeinnützige GmbH, Berlin, Germany; 4: Berliner Wasserbetriebe, Neue Jüdenstraße 1, 10179 Berlin, Germany The increasing costs of maintaining sewer networks are driven by population growth and the aging of pipes. Predictive maintenance offers a solution by optimizing resource allocation and focusing repairs on the most vulnerable sewer pipes. This study employs Machine Learning (ML) models, particularly Random Forests (RF), to forecast hydraulic failures using citizens’ reporting data and GIS-based pipe parameters. This approach is advantageous for municipalities with accessible data, especially in areas lacking CCTV inspections for structural conditions. While initially utilizing RF models, the study aims to incorporate Graph Neural Networks (GNNs) to exploit spatial connections within the network. | |