Background and motivation

Sewer infrastructure is of critical importance for ensuring reliable delivery of public services. However, sewer systems are ageing and require increasing maintenance. Currently, maintenance and operational procedures are primarily carried out in a manual and reactive manner, relying on heterogeneous and often inconsistent data, which prohibits pro-active prognoses and predictive maintenance, which are essential for enhancing the efficiency, dependability, and resilience of sewer infrastructure.

Project goals and scientific approach

The KaSyTwin project aims to address these challenges by developing a methodology for creating digital twins of sewer infrastructure, facilitating pro-active maintenance and operation as well as intelligent decision-making relevant to sewer infrastructure. The proposed methodology entails a generally valid digital twin concept that integrates laser scan data recorded with multi-sensor systems, as-built documentations, and sensor data provided by IT systems commonly used in sewer maintenance and operation. KaSyTwin will focus on three key research areas. The first research area involves defining the current state of the art and deriving essential requirements for digital twins. Furthermore, reference models and architectures will be developed. The second research area centers around devising a workflow for AI-based data analysis, along with establishing seamless data integration with the digital twin. In the third research area, a knowledge-based damage prediction system will be implemented, taking advantage of the results achieved in the previous research areas.

Expected results

As a result, it is expected that KaSyTwin, representing an interdisciplinary research project, will support sewer infrastructure management through cutting-edge technologies. By leveraging digital twins and advanced data analytics, the project seeks to enhance the efficiency, reliability, and resilience of sewer systems, improving public services and promoting a healthier environment.