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 will be developed to monitor the condition of an existing road section (real laboratory) by integrating material and condition monitoring data. The generated data will be analyzed automatically using the concept of a digital twin of the real laboratory to develop optimized prediction approaches for the road condition, with the goal of enabling a more efficient use of resources in road and asset management.
The material and condition monitoring data of the existing real laboratory will be collected by existing stationary sensors or Weigh-in-Motion stations, as well as new sensors to determine the structural state of the pavement. In addition, performance-based laboratory tests on drilling cores taken from the real laboratory and load-bearing capacity measurements with the Falling Weight deflectometer are planned.
The digitalization of the real laboratory in a digital twin is done by using 3D photogrammetric methods. A corresponding data and system design based on application-specific requirements is planned for the data exchange and communication between the sensor and measurement 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 or measurement data to it represents the central element of a digital twin in the field of road pavements. Within the framework of this research project, customized data storage solutions and adapted data input and data request procedures are to be designed and implemented based on existing database systems. The goal of scalability for the DACH region will be considered. The advantages and disadvantages of 2.5D, 3D and dashboard views will be analyzed and demonstrated for the visualization of the digital twin, including the sensor data and the results obtained, and the most suitable framework will be selected in consultation with the project stakeholders.
To assess the importance 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 will be done. Existing models will be processed and compared with more recent developments 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 surface will be derived from the collected real-lab data by using statistical analyses to assess their information gain. Simulations will be used to describe the effects of optimized measure planning on measure timing and life cycle costs. In a further step, an investigation of the scaling of the results to the system level and the transferability of the developed approaches to the high-ranking road network in the DACH region will be examined. As an important step in the realization of the climate goals on highways and motorways in the DACH region, potentials for the reduction of user costs and emissions in the choice of materials within the framework of the optimization of measures will be identified.
The project team at the 2023 kick-off, from left to right:
This project is funded by the FFG.
Senior Data Engineer
