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

Author:

Vollpracht Anya1ORCID,Gluth Gregor J. G.2,Rogiers Bart3,Uwanuakwa Ikenna D.4,Phung Quoc Tri3,Zaccardi Yury Villagran5,Thiel Charlotte6,Vanoutrive Hanne7,Etcheverry Juan Manuel8,Gruyaert Elke9,Kamali-Bernard Siham10,Kanellopoulos Antonios11,Zhao Zengfeng12,Martins Isabel Milagre13,Rathnarajan Sundar14,De Belie Nele8

Affiliation:

1. RWTH: Rheinisch-Westfalische Technische Hochschule Aachen

2. Bundesanstalt fur Materialforschung und -prufung

3. Belgian Nuclear Research Centre: Studiecentrum voor Kernenergie

4. Near East University: Yakin Dogu Universitesi

5. Flemish Institute for Technological Research: VITO NV

6. OTH-Technical University of Applied Sciences: Ostbayerische Technische Hochschule Amberg-Weiden

7. KU Leuven: Katholieke Universiteit Leuven

8. Ghent University: Universiteit Gent

9. KUL: Katholieke Universiteit Leuven

10. University of Rennes: Universite de Rennes

11. University of Hertfordshire

12. Tongji University

13. Laboratorio Nacional de Engenharia Civil

14. Indian Institute of Technology Madras

Abstract

Abstract

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comprises mix designs with a large variety of binders with up to 94% SCMs, collected from the literature as well as unpublished testing reports.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 w/CaOreactive 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.

Publisher

Research Square Platform LLC

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