This project aimed to support the detection of risky excavation operations based on historical datasets of damages in the Netherlands. Jiarong Li developed, using XGBoost, a data-driven model that predicted the likelihood of damage occurring in an excavation polygon. The Dutch agency Kadaster uses such polygons to exchange data on utility locations between network owners and excavator operators.
The machine learning model had a satisfactory performance with an AUC-ROC score of 0.821 and a balanced accuracy of 0.743.
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To scale up city district heating construction projects, processes need to be developed and reliable. Currently teams largely rely on experience and improvisation, making them skilled in troubleshooting, but less efficient in planning and anticipating disruptions. This thesis has explored anticipation and containment through the lens of HRO
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Traditionally, sewer inspections are conducted by direct access to the pipes, and where possible by visual inspections. In addition to being a tedious and cumbersome job, visual inspections do not always deliver the desired results. In the TISCALI project, research is being done to more efficient and less error prone inspection methods.
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In the project IMKL 2.0, four showcases have been developed to show how the data that is gathered according to the new IMKL2.1 protocol can be used for visualising uncertainties and risks.
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