journal article·2026-03Open access

Influences of Surface Properties on the Reflection Intensity - Towards in Situ Monitoring During Early Age Hydration of CEM I

Eric Schönsee, Götz Hüsken, Amarteja Kocherla, Christoph Strangfeld

Abstract

Abstract Interlayer bonding in 3D concrete printing is influenced by the hydration progress and surface moisture of the previously printed layer. For effective quality control, continuous in situ monitoring of interlayer surface properties is required. This study investigated reflection intensity as a method for in situ measurements during the hydration of CEM I mixtures with varying retarder contents. Additional factors influencing the reflection intensity are also examined. Two laser line scanners with different wavelengths were used to track hydration over 72 h. Vicat tests and isothermal calorimetry served as reference methods. Across all the mixtures, the reflection intensity exhibited a repeatable pattern with five different stages. A sharp increase in intensity during the third stage was consistent with the acceleration period of hydration. These findings suggest that reflection intensity measurements could serve as a promising tool for evaluating interlayer bonding in 3D concrete printing. Graphical abstract The material used in this study is a cement lime, based on CEM I 42.5 N, with varying retarder content. From each batch of material, two samples were prepared for Vicat testing, two samples were prepared for isothermal calorimetry measurements, and one sample was cast for monitoring the reflection intensity. Two laser profile scanners were used, operating at 405 nm and 658 nm, respectively. Data were acquired for 72 h. The results show a strong increase in reflection intensity during the acceleration period.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Innovations in Concrete and Construction Materials · Innovative concrete reinforcement materials · Additive Manufacturing and 3D Printing Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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