No abstract

The similarity of the fine fraction of dredged freshwater sediments to natural clays stimulates the valorization in a product similar to expanded clay aggregates, obtained by bloating at high temperature. The impact of the ratios between different flux elements (Fe 2 O 3 + CaO + MgO + K 2 O + Na 2 O) on the melting behavior is quantified using thermodynamic modeling and validated with experiments. The total content of alkalis is found to be key in controlling melting and bloating. Increased alkali content triggers the formation of alkali feldspars that start to melt at low temperature and give rise to a gradual increase of the amount of melt with temperature. The ratio (CaO + MgO + K 2 O + Na 2 O)/Al 2 O 3 should preferably be smaller than or close to 1 to avoid excessive pyroxene formation, which causes complete meltdown of the granules around 1150 °C. The chemical composition has a complex influence on the melting behavior, even when on the same location on the SiO 2 -Al 2 O 3 -flux diagram. The conventional approach of trying to modify the composition towards a certain range in the ternary diagram does not work for all materials, and therefore, the understanding of the melting behavior using the phase composition at high temperature (= the metallurgical approach) provided in this work is shown to be crucial to produce lightweight aggregates from materials with a higher chemical variability.

In this paper, we present a methodology for measuring the reaction degree of ground granulated blast furnace slag (GGBFS) in alkali‐activated cements using neural network based image analysis. The new methodology consists of an image analysis routine in which the segmentation of the back scattered electron (BSE) (SEM) images is based on a deep learning U‐net. This methodology was applied to and developed for NaOH‐activated slag cements and validated against independently measured XRD results. In a next step the developed method was applied to NaOH‐Na2SO4‐activated systems, to check the broader applicability. The neural networks based image analysis results were shown to correlate well with the XRD results. Once the model was trained, it segmented images fast and accurately. Furthermore, the model trained on the NaOH‐activated systems was readily applicable on NaOH‐Na2SO4‐activated system indicating that the model generalises well. As such, the developed methodology and models can be more performant and robust than conventional threshold‐based image segmentation. The method's accuracy, replicability and transferability make it a promising tool for material analysis and characterisation.

This research investigated the possibility of using metallurgical slags from the copper and lead industries as partial replacement for cement. The studied slags were fayalitic, having a mainly ferro-silicate composition with minor contents of Al2O3 and CaO. The slags were treated at 1200–1300 °C (to reduce the heavy metal content) and then granulated in water to promote the formation of reactive phases. A full hydration study was carried out to assess the kinetics of reactions, the phases formed during hydration, the reactivity of the slags and their strength activity as supplementary cementitious material (SCM). The batch-leaching behaviour of cementitious mixtures incorporating treated slags was also investigated. The results showed that all three slags have satisfactory leaching behaviour and similar performance in terms of reactivity and contribution to the strength development. All slags were found to have mediocre reactivity and contribution to strength, especially at early ages. Nonetheless, they passed the minimum mechanical performance requirements and were found to qualify for use in cement.