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.

This study focuses on translating the underground spatial impact of redevelopments in public space, related to heat transition, electric grid upgrading, and climate resilience.

Utility lines can be extracted from point clouds to support further 3D modelling and mapping. Doing this manually is time consuming, Jorn Kruiper investigated how to use machine learning for this task.

Therefore, Dutch law (WIBON) mandates practitioners to reduce the uncertainty in the information about the subsurface situation through the process of utility surveying. One of the most promising non-disruptive methods is the Ground Penetrating Radar (GPR). GPR is a geophysical method that uses electromagnetic waves to investigate the subsurface. This study aims to explore, explain and support the decision making between trial trenches and GPR as a utility surveying method by developing, implementing and evaluating a Decision Support Tool.

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

Smart Utility Registration aims to develop a method for scanning trench data in more automated ways. It mobilizes off-the-shelf (preferably open sources) technologies to register deployed cables/pipelines. Currently, this process is laborious and involves many different workers, while new technology might enable direct registration by the jobsite crew.

This project ran from 2019-2021 and resulted in a prototype Decision Support System, which provides decision-makers with insight in the optical fiber deployment process as well as the results of operational changes they may wish to make

This project is aims to validate the developed CityGML Utility ADE Operations and Maintenance data model. To this end Federico encoded the UML diagram (concept model) and implemented this as relational database tables in a PostGreSQL and PostGIS environment.

This study explores the state-of-practice in 3D modelling at a mid-sized Utility Contractor.