F. Tscheikner-Gratl
7 records found
1
Authored
Small utilities often lack the required amount of data to train machine learning-based models to predict pipe failures, and hence are unable to harness the possibilities and predictive power of machine learning. This study evaluates the generalizability and transferability of ...
The calibration of models for urban drainage systems has become more and more important as especially the usage of detailed models has increased considerably over the last years as the basis for planning and design. Still the effects originating from the choice of data used fo ...
Pipe failure modelling is an important tool for strategic rehabilitation planning of urban water distribution infrastructure. Rehabilitation predictions are mostly based on existing network data and historical failure records, both of varying quality. This paper presents a fra ...