In contrast to planning, construction has hardly changed in the last decade concerning digital progress. The execution on the construction site is still intransparent and complex due to the high number of different processes. However, the need for optimisation is immense: Operations can be accelerated, disruptions avoided, and damage prevented. To this end, there is a lack of support from digital methods within executed construction processes. In the context of this master's thesis, a developed data framework for investing existing processes of construction execution follows. The classification for the organisation and evaluation of the individual methods plays a central role. In consideration of this, the implemented data pipeline represents the "as-performed construction process" methodology. Eventually, individual processes are schematically examined based on the system created. With the help of designing a prototype, the potential for optimisation is analysed. The use of sensors (e.g. BLE chips) applied on the construction site provides a suitable solution. All introduced prototypes are illustrated and verified within diverse case studies. The data obtained is then validated, evaluated and integrated into the Data Mining process. In summary, this thesis provides the foundation for further developing existing construction processes and collecting data for future analyses.     «

In contrast to planning, construction has hardly changed in the last decade concerning digital progress. The execution on the construction site is still intransparent and complex due to the high number of different processes. However, the need for optimisation is immense: Operations can be accelerated, disruptions avoided, and damage prevented. To this end, there is a lack of support from digital methods within executed construction processes. In the context of this master's thesis, a develope...     »