The term “digital twin” is becoming increasingly prevalent in both research and politics with regard to infrastructure structures. Industry is making significant advancements in the fields of automation and autonomy. While numerous definitions are in circulation, some of which are identical, many are similar in nature. This work examines the digital twin from its original industry perspective and considers its relevance to the constructional engineering sector. The objective of this study is to provide an overview of the current approaches in the Industry 4.0 and constructional engineering industry, examining the associated technical opportunities and challenges. The study compares the definitions, requirements and projects executed in this field. The technologies under examination are contrasted on the basis of their position within the data model. The preliminary findings suggest that some solutions for Industry 4.0 have already been developed for the construction engineering sector. However, it must be acknowledged that some solutions have yet to be fully validated.

In recent times, the value of data has grown. This tendency is also observeable in the construction industry, where research and digitalisation are increasingly oriented towards the collection, processing and analysis of different types of data. In addition to planning data, measurement data is a main focus. fibre optic measurements offer a highly precise and comprehensive approach to data collection. It is, however, important to note that this technology is still in research regarding concrete structures. This paper presents two methods of integrating filigree sensors into concrete structures. The first approach entails wrapping a fibre around a tendon duct and analysing the installation and associated measurements. The second method involves bonding polyimide and acrylate-coated fibres with 2K epoxy and cyanoacrylate in the grooves of rebars, exposing them to chemical environments. The resulting measurement data is evaluated qualitatively and quantitatively to ascertain its resilience to environmental factors. These developed criteria are consolidated in a decision matrix. Fibre-adhesive combinations necessitate protection from chemical and mechanical influences. The limitations of the solutions are pointed out, and alternative options are proposed.

Structural health monitoring is the process of implementing a continuous damage detection strategy to optimize the inspection and maintenance schedules of bridges, and extend their lifespans. One of the main challenges of automated damage detection is the lack of data on damaged states, which makes it difficult to validate new approaches in the research and development stage. To alleviate this problem, a monitoring campaign on a two‐span test bridge with defined defects is conducted and documented in this article. The bridge is a steel‐concrete composite structure with a length of 30 m, with two primary steel girders and a segmented concrete deck. The recorded data capture the long‐term ambient data from 18 test days and changing environmental conditions, as well as the short‐term ambient data and dynamic load tests from four damage scenarios with well‐defined damage extents. A mobile measurement system with numerous sensors is used for data acquisition. A shaker is placed on the bridge to excite white noise. The main goal of this article is to document the experimental procedure and perform preliminary plausibility checks on the measured data. First results demonstrate that system response data and environmental conditions are recorded reliably and that environmental effects significantly affect the long‐term measurements. Therefore, a suitable data set is provided as open‐source data for future studies on data normalization and automated damage detection.