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CONCRETE TECHNOLOGY38 CPI %u2013 Concrete Plant International %u2013 4 | 2025 www.cpi-worldwide.comApplying Digitalization in Concrete ConstructionOne recent example for the implementation of IoT concepts in the construction industry comes from the Danish precast concrete plant (Contiga Tinglev), which deployed wireless IoT sensors to monitor real-time concrete strength. The company reported a 35% reduction in material waste and a 15% increase in output, primarily due to faster, safer demolding enabled by accurate in-place strength data, within six weeks of implementation [6, 7]. These figures are reported in industry sources and communicate the benefits of digitalization in precast manufacturing and interest of the industry in such solutions. While the specific numbers cannot be backed by scientific and independent research, the usefulness of the underlying technology is supported by peer-reviewed studies and academic research. For instance, Miller et al. (2023) confirmed that IoT-enabled systems can closely match laboratory-tested strength values [8] while Lo et al. (2021) stated that IoT-based curing systems can even outperform traditional methods in quality and efficiency [9]. These examples showcase how data-driven oversight can improve efficiency, reduce waste, and enhance scheduling in concrete production. Another area of digital innovation lies in the delivery phase of concrete production. Automated truck-mounted sensor systems enable real-time monitoring and adjustment of concrete properties in transit. These systems can continuously track parameters like slump, temperature, mixing energy, humidity, rotation of the drum and even detect water additions. The tracking of such parameters can help ensuring the mix remains within specification from the plant to the construction site. Such systems help detect out-of-spec conditions early, reduce rejected loads and, where permitted, automatically adjust the mix using pre-authorized water or admixtures within regulatory limits to achieve target consistency before discharge [10]. This can help to reduce the reliance on manual interventions on-site and lead to more consistent placement quality. In a related study, implementation at European sites demonstrated reduced variability in slump, minimized material waste and improved traceability of mix adjustments [11].At the job site, sensor-equipped concrete pumps and placement machines can be used to enable continuous monitoring of parameters such as hydraulic pressure, piston movement or flow rate. The tracking of such data allows for an early detection of anomalies during the casting process, such as pressure spikes that signal an imminent pump blockage. Blockages during the pumping process are a major cause of downtime and cost overruns in concrete construction. Rather than reacting after a blockage halts operations, these systems can alert operators or even automatically adjust the pumping process to avoid disruptions. Recent research projects conducted by the TU Dresden and Leibniz University Hannover have established a scientific basis for such monitoring. These studies developed validated methods to assess pumpability and stability of fresh concrete and further introduced predictive models for blockage risk, based on sensor data from real pump operations [12, 13].Building on this foundation, the Huckepack system, which is currently in pilot implementation and protected under patent DE 10 2022 116 603.1, represents a major step forward in retrofitting conventional construction equipment with edge intelligence. By piggybacking on existing machine interfaces, it transforms \without interfering with core operations. Its plug-and-play architecture enables flexible, use-case-specific data collection and analysis, unlocking digital capabilities in machines that might not even have been designed for connectivity in the first place. These advancements highlight how data-driven oversight is transforming even the most physical aspects of the construction process. From mixing to placement, integrating digital systems like Huckepack into everyday operations paves the way for safer, smarter, and more sustainable concrete workflows. These digital systems also enable full data logging, supporting compliance documentation, accountability and optimization of fleet logistics.Results from First Testing PhaseIn an initial pilot phase of the Huckepack system, sensor modules were installed on both truck-mounted concrete pumps and combined mixer-pump vehicles. To enable inline monitoring of key process parameters during operation, sensors were installed at locations identified as critical for capturing data relevant to material properties. A common challenge in the digitalization of construction processes lies in the lack of deep domain knowledge throughout several key domains. Systems are often developed without sufficient understanding of the physical processes involved or coordination between the specialists in their fields. In the case of Huckepack, sensor placement was guided by expertise in concrete technology, ensuring that the collected data is relevant to the rheological behavior of fresh concrete and the functional specifics of the equipment involved. The pilot implementation was conducted in collaboration with the company BFU (Betonf%u00f6rderunion), contributing operational expertise in concrete transport and placement, and Perinet GmbH, providing competence in sensor topology, edge computing, and secure data transmission. The collaboration ensured that both the practical requirements of on-site concrete handling and the technical demands of data integration were systematically addressed.The hydraulic pressure signal was continuously captured by embedded sensors and transmitted to an edge device mounted directly on the machine. There, a dedicated script processed the data to calculate dynamic coefficients, derived from previous piston strokes, that characterize the pumping behavior. Based on these calculated values, the system is able to assess whether a critical threshold is being approached or exceeded, enabling early detection of flow anomalies that may indicate an impending blockage. The processed data is subsequently transmitted via a built-in communication router to a remote server for logging, diagnostics, and visualization. The information contained in these time-dependent series can be analyzed either on edge or on cloud. An example of a pressure measurement time series from a concrete pump is shown in Figure 1.