Anwendung von maschinellen Lernverfahren zur Prognose der Vortriebsgeschwindigkeit von Tunnelvortriebsmaschinen
Betreuer: Scheffer, M.
In mechanized tunneling, as in many engineering endeavors, determining the duration of a project reliably plays a prominent role during the planning stage. Particularly for logistics planning and for the creation of delivery and storage workloads, accurate forecasts of the expected advancement speed of the TBM are of significant importance. In this thesis it has been shown how the use of data analysis and data mining approaches in combination with machine learning techniques, specifically artificial neural networks, can provide results to adequately predict the behaviour of a TBM (the advancement rate, for example) as required in a simulation software system.
Quelle und Weiterlesen: Ruhr-Uni-Bochum