Abstract
Recycled aggregate concrete (RAC) contributes to mitigating the depletion of natural aggregates, alleviating the carbon footprint of concrete construction, and averting the landfilling of colossal amounts of construction and demolition waste. However, complexities in the mixture optimization of RAC due to the variability of recycled aggregates and lack of accuracy in estimating its compressive strength require novel and sophisticated techniques. This paper aims at developing state-of-the-art machine learning models to predict the RAC compressive strength and optimize its mixture design. Results show that the developed models including Gaussian processes, deep learning, and gradient boosting regression achieved robust predictive performance, with the gradient boosting regression trees yielding highest prediction accuracy. Furthermore, a particle swarm optimization coupled with gradient boosting regression trees model was developed to optimize the mixture design of RAC for various compressive strength classes. The hybrid model achieved cost-saving RAC mixture designs with lower environmental footprint for different target compressive strength classes. The model could be further harvested to achieve sustainable concrete with optimal recycled aggregate content, least cost, and least environmental footprint.
Highlights
Portland cement concrete is the most widely used construction material and the most consumed industrial product in the world
The present study explores deploying state-of-the-art machine learning models to predict the compressive strength of Recycled aggregate concrete (RAC) and optimize its mixture design
To guarantee that the developed models were able to generalize the compressive strength of RAC, K-fold cross-validation was used during the tuning process
Summary
Portland cement concrete is the most widely used construction material and the most consumed industrial product in the world. There have been multiple studies on the mechanical properties of RAC, more research needs to be devoted to investigating the effects of various parameters on RAC compressive strength, including the variability of RA, the effect of old cement mortar attached to the RA surface, and the crushing process of RA [7,13]. The present study aims at creating a large and comprehensive experimental dataset from the available studies in the open literature to develop powerful state-of-the-art ML models to predict the compressive strength of RAC. For this purpose, a dataset consisting of 1134 experimental examples of RAC mixture designs with 10 attributes was developed. The superior accuracy of the proposed models should assist various stakeholders in optimal use of recycled concrete in diverse construction applications
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