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- Alkali-activated building materials (1)
- Building and Construction (1)
- Data-driven material design (1)
- Data-driven materials design (1)
- General Materials Science (1)
- Inverse design techniques (1)
- Machine learning optimization (1)
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- Sequential learning (1)
- Sustainable building materials (1)
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This paper presents a novel approach for developing sustainable building materials through Sequential Learning. Data sets with a total of 1367 formulations of different types of alkali-activated building materials, including fly ash and blast furnace slag-based concrete and their respective compressive strength and CO2-footprint, were compiled from the literature to develop and evaluate this approach. Utilizing this data, a comprehensive computational study was undertaken to evaluate the efficacy of the proposed material design methodologies, simulating laboratory conditions reflective of real-world scenarios. The results indicate a significant reduction in development time and lower research costs enabled through predictions with machine learning. This work challenges common practices in data-driven materials development for building materials. Our results show, training data required for data-driven design may be much less than commonly suggested. Further, it is more important to establish a practical design framework than to choose more accurate models. This approach can be immediately implemented into practical applications and can be translated into significant advances in sustainable building materials development.
Fused cement clinker can be produced from molten basic oxygen furnace slag (BOFS) by way of a reductive thermochemical treatment. During the thermochemical treatment, oxidic iron is reduced to metallic iron and separated. The resulting low-iron slag has a chemical and mineralogical composition similar to ordinary Portland cement (OPC) clinker. In this study, the hydraulic reactivity of the fused clinker from BOFS with and without gypsum was investigated using isothermal calorimetry, differential scanning calorimetry, in situ X-ray diffraction and powder X-ray diffraction. Furthermore, a synthetic fused clinker without foreign ions and fused clinker produced by a mixture of both materials was studied. The hydraulic reaction of the fused clinker from BOFS was considerably slower than that of OPC. However, the reaction can be accelerated by adding gypsum as a sulfate carrier. Furthermore, the results showed an increased reaction rate with decreasing content of foreign ions such as Fe, P or Mn.
In recent decades, the number of components in concrete has grown, particularly in formulations aimed at reducing carbon footprints. Innovations include diverse binders, supplementary cementitious materials, activators, concrete admixtures, and recycled aggregates. These developments target not only the enhancement of material properties but also the mitigation of the ecological and economic impacts of concrete — the most extensively used material by humankind. However, these advancements also introduce a greater variability in the composition of raw materials. The material’s behavior is significantly influenced by its nanoscale properties, which can pose challenges in accurate characterization. Consequently, there’s an increasing need for experimental tuning of formulations. This is accompanied by a more inconsistent composition of raw materials, which makes an experimental tuning of formulations more and more necessary. However, the increased complexity in composition presents a challenge in finding the ideal formulation through trial and error. Inverse design (ID) techniques offer a solution to this challenge by allowing for a comprehensive search of the entire design space to create new and improved concrete formulations. In this publication, we introduce the concept of ID and demonstrate how our open-source app “SLAMD” provides all necessary steps of the workflow to adapt it in the laboratory, lowering the application barriers. The intelligent screening process, guided by a predictive model, leads to a more efficient and effective data-driven material design process resulting in reduced carbon footprint and improved material quality while considering socio-economic factors in the materials design.