The EU-Horizon-2020-funded project BIM2TWIN aims to build a Digital Building Twin (DBT) platform for construction management that implements machine learning techniques to reduce operational waste of all kinds, shortening schedules, reducing costs, enhancing quality and safety, and reducing carbon footprint. In the context of the project, the chair of computing in civil engineering develops various methods and prototypes for the optimal planning and control of construction machinery on construction sites in cooperation with practical partners.
The efficient control of equipment on the construction site is a major challenge. In contrast to assembly line production in mechanical engineering, the use of equipment on the construction site varies from project to project and must be adapted continuously. The construction equipment interacts with manual activities and it must be possible to consider disturbances. If changes on the construction site occur or work is not carried out as planned, adjustments must be made as required. However, ad hoc decisions must be avoided, and adjustments should be made in a targeted and controlled manner based on production planning. Ultimately, for an optimal and quality-assured construction, these very complex and dynamic boundary conditions in the daily use of the construction equipment must be evaluated continuously. The focus is on short- and medium-term operational planning and control of the equipment. In order to control the equipment optimally, the current position, the current activity and the current condition must be captured and analysed. This analysis is then used as the basis for controlling the equipment in a targeted manner. Within the scope of the work package, methods and prototypes are developed for the optimal planning and control of equipment on complex construction sites. The following objectives are targeted:
BIM2TWIN – Equipment optimization
EUROPEAN COMMISSION Directorate-General for Research and Innovation
European Union’s Horizon 2020 research and innovation programme
02.2021 – 04.2024