The basis of intelligent maintenance management is in addition to the development of prognosis models the collection of data. Every structure, every building material and every building has its own requirements. Our own investigations have shown that prognosis models for maintenance management are very sensitive to the sensor technology used. If data patterns are generated with unsupervised learning, their quality and informative value also depends on the sensors used for data acquisition. The fundamentals for an optimal sensor set for data acquisition on reinforced concrete structures are investigated on a test beam made of reinforced concrete. The following physical parameters are measured on a 4-point bending test of a reinforced concrete beam under laboratory conditions: Strains, temperature (outside and inside the concrete), accelerations, inclinations, and deformations. The measurement is done with different systems like fiber optic or piezoelectric sensors. In addition to the measurement of the physical quantities, data storage plays an essential role for subsequent evaluations using machine learning techniques. For this purpose, an asset administration shell was developed at the Institute of Structural Engineering at UniBw in accordance with Industry 4.0. This is where all data from measurement and planning, such as sensor data, BIM models or laser scans, are consolidated. The article shows the experimental setup and the sensor set for optimal data acquisition on reinforced concrete structures. Furthermore, the asset administration shell for intelligent data management is presented.