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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Rathnarajan, Sundar
in Cooperation with on an Cooperation-Score of 37%
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Publications (6/6 displayed)
- 2025Use of seawater as an accelerator in 3D printed concrete (3DPC)
- 2024Report of RILEM TC 281-CCC: insights into factors affecting the carbonation rate of concrete with SCMs revealed from data mining and machine learning approachescitations
- 2024Report of RILEM TC 281-CCC: insights into factors affecting the carbonation rate of concrete with SCMs revealed from data mining and machine learning approachescitations
- 2024Report of RILEM TC 281-CCC: Insights into factors affecting the carbonation rate of concrete with SCMs revealed from data mining and machine learning approaches
- 2023Seawater-Mixed Lightweight Aggregate Concretes with Dune Sand, Waste Glass and Nanosilica: Experimental and Life Cycle Analysis
- 2023Seawater-Mixed Lightweight Aggregate Concretes with Dune Sand, Waste Glass and Nanosilica: Experimental and Life Cycle Analysiscitations
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document
Report of RILEM TC 281-CCC: Insights into factors affecting the carbonation rate of concrete with SCMs revealed from data mining and machine learning approaches
Abstract
<title>Abstract</title><p>The RILEM TC 281–CCC ‘‘Carbonation of concrete with supplementary cementitious materials’’ conducted a study on the effects of supplementary cementitious materials (SCMs) on the carbonation rate of blended cement concretes and mortars. In this context, a comprehensive database has been established, consisting of 1044 concrete and mortar mixes with their associated carbonation depth data over time. The dataset comprises mix designs with a large variety of binders with up to 94% SCMs, collected from the literature as well as unpublished testing reports. The data includes chemical composition and physical properties of the raw materials, mix-designs, compressive strengths, curing and carbonation testing conditions. Natural carbonation was recorded for several years in many cases with both indoor and outdoor results. The database has been analysed to investigate the effects of binder composition and mix design, curing and preconditioning, and relative humidity on the carbonation rate. Furthermore, the accuracy of accelerated carbonation testing as well as possible correlations between compressive strength and carbonation resistance were evaluated. The analysis revealed that the <italic>w</italic>/CaO<sub>reactive</sub> ratio is a decisive factor for carbonation resistance, while curing and exposure conditions also influence carbonation. Under natural exposure conditions, the carbonation data exhibit significant variations. Nevertheless, probabilistic inference suggests that both accelerated and natural carbonation processes follow a square-root-of-time behavior, though accelerated and natural carbonation cannot be converted into each other without corrections. Additionally, a machine learning technique was employed to assess the influence of parameters governing the carbonation progress in concretes.</p>