Prediction Of 28 Days Compressive Strength of High Slag Concrete by Establishing Accelerated Oven Curing Regimes for Rapid Quality Control

Authors

  • Sudin Mohan
  • Mohammad Ajma
  • Michal P. Drewniok

Keywords:

Calcium Silicate Hydrate Gel (C-S-H). Calcium Hydroxide (Ca (OH)2). Calcium Alumina (Aft). Monosulphur Calcium Sulphoaluminate Hydrate (AFm). Delayed Ettringite Formation (DEF)

Abstract

High Slag Concrete (HSC) offers substantial benefits in terms of durability and reduced carbon footprint, but its late strength gain delays accurate 28-day strength prediction from early strength. This study aims to develop accelerated oven curing regimes to predict 28-day compressive strength of HSC accurately. The research focuses on the fundamental question of whether the application of accelerated curing at specific temperatures would help estimate High Slag Concrete's long-term strength. To achieve this, a series of concrete specimens were subjected to accelerated oven curing at 50°C and 70°C. The compressive strength development was observed and correlated with standard curing conditions. Additionally, the hydration kinetics of the cementitious paste under these elevated temperatures were examined by using the Isothermal calorimetry method. This research will produce a predictive model correlating accelerated curing data with 28-day strength. The findings of this study will provide a reliable method for estimating the strength of High Slag Concrete at an early age, enabling more efficient construction planning.

Author Biographies

Sudin Mohan

Qatar Beton LLC, Doha, Qatar

Mohammad Ajma

Doha Technical Laboratories, Mesaieed, Qatar

Michal P. Drewniok

School of Civil Engineering, University of Leeds, Leeds, UK

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Published

2025-04-22

How to Cite

Mohan, S., Ajma, M., & Drewniok, M. P. (2025). Prediction Of 28 Days Compressive Strength of High Slag Concrete by Establishing Accelerated Oven Curing Regimes for Rapid Quality Control. Artificial Intelligence and Sustainable Materials, 1(1). Retrieved from http://34.218.147.63/index.php/Artificial-Intelligence-Sustaina/article/view/90