The DiTwin research project focuses on the development of ground-breaking methods for recording the condition of road sections using digital twin technology. By integrating state-of-the-art sensor technology and advanced prediction models, DiTwin aims to usher in a new era of road and asset management. This innovation promises not only a more accurate condition assessment, but also an optimized use of resources, leading to more sustainable and cost-efficient infrastructure management strategies.
In this research project, innovative approaches are to be developed to record the condition of an existing road section (real-world laboratory) by integrating material and condition data. The generated data is to be analyzed automatically using the concept of a digital twin of the real-world laboratory in order to develop optimized forecasting approaches for road conditions with the aim of enabling more efficient use of resources in road and asset management. The material and condition data from the real-world laboratory will be recorded using stationary sensors and Weigh-inMotion stations. This involves using data from existing sensors as well as installing new sensors to record the structural condition.
In addition, load-bearing capacity measurements with the drop weight deflectometer and GMO-based laboratory tests on drill cores taken from the real laboratory are planned. The digitalization of the real laboratory in a digital twin is to be carried out using 3D photogrammetric methods, among other things. A corresponding data and system design based on application-specific requirements is planned for data exchange and communication between the sensor and measuring units of the real laboratory and the digital twin. The database for storing and managing different road graphs and the possibility of referencing all sensor and measurement data to it represents the central element of a digital twin in the field of road superstructures.
As part of the research project, customized data management solutions and adapted data entry and data retrieval procedures are to be designed and implemented based on existing database systems. The goal of scalability for the DACH region will be taken into account. For the visualization of the digital twin incl. of the sensor data and the results obtained, the advantages and disadvantages of 2.5D, 3D and dashboard views are analyzed and demonstrated, and the most suitable visualization technique is selected in consultation with the client.
In order to determine the significance and potential of a digital twin for the asset and pavement management of highway operators in the DACH region, systematic research, the analysis of mathematical models and statistical evaluations are to be carried out. Existing models are to be processed and compared with more recent developments in order to prepare the models for use in the real laboratory. Based on the developed data and system structure, extended condition models for relevant damage characteristics of the road superstructure are to be derived from the recorded real-world laboratory data by means of statistical analyses and their information gain evaluated. Simulations will be used to illustrate the effects of optimized measure planning on measure timing and life cycle costs. In a further step, the scaling of the results to the network level and the transferability of the developed approaches to the high-ranking road network in the DACH region will be investigated. As an important step in achieving the climate targets on freeways and expressways in the DACH region, potentials for reducing user costs and emissions in the choice of materials are to be identified as part of the optimization of measures.
Part of the project team at the 2023 kick-off, from left to right: Melina Rohne (RWTH Aachen University), Markus Hoffmann (Hoffmannn Consult), Serge Lamberty (RWTH Aachen University), Paul Heinzlreiter (RISC Software), Martin Peyer (TU Vienna), Stefanie Kritzinger-Griebler (RISC Software)
This project is funded by the FFG.
Senior Data Engineer
