In the quest for sustainable construction materials, researchers have turned to an unlikely ally: waste foundry sand (WFS). A byproduct of the metal casting process, WFS is often discarded, but a recent study suggests it could be a viable alternative to natural aggregates in mortar. The research, led by Sahar Mahdinia from the Department of Civil Engineering at Ferdowsi University of Mashhad, explores the combined influence of WFS and cement strength class (CSC) on the compressive strength of mortar, using a cutting-edge machine learning approach called Gene Expression Programming (GEP).
The study, published in the journal Scientific Reports (translated to English as “Scientific Reports”), is a significant step forward in the field of sustainable construction. “The growing interest in sustainable construction materials has prompted the investigation of alternative resources and sophisticated predictive techniques to enhance material performance,” Mahdinia explains. The research team created a comprehensive experimental dataset by varying WFS percentages (0%, 10%, 20%, 30%, 40%, and 50%) and CSCs (32.5, 42.5, 52.5 MPa). They then used GEP to predict the compressive strength of the mortar mixes.
GEP is a type of machine learning that offers enhanced transparency and robust predictive accuracy compared to other models like Artificial Neural Networks (ANN). This makes it particularly suitable for data-driven decision-making in sustainable construction. The study found that incorporating CSC as an input variable significantly improved predictive accuracy, achieving a high correlation coefficient (R=0.99) and a low root mean square error (RMSE=2.3).
The implications of this research are far-reaching, particularly for the energy sector. As the demand for sustainable and energy-efficient buildings grows, so does the need for high-performance, eco-friendly construction materials. By optimizing mortar mix designs with WFS and advanced modeling methodologies, engineers and researchers can contribute to resource conservation and the creation of sustainable infrastructure.
“This research aids in resource conservation and the creation of high-performance, eco-friendly construction materials,” Mahdinia notes. The study provides a solid framework for engineers and researchers to advance material design and sustainability within the construction sector. As the industry continues to evolve, the integration of sustainable materials and advanced modeling techniques will be crucial in shaping the future of construction.
The research also highlights the importance of considering both WFS and CSC in tandem within predictive models. This holistic approach can lead to more accurate predictions and better optimization of mortar mix designs. As the construction industry strives for sustainability, the insights gained from this study can guide the development of innovative, eco-friendly materials that meet the demands of modern construction.
In conclusion, the study by Mahdinia and her team represents a significant advancement in the field of sustainable construction. By leveraging the power of machine learning and exploring the potential of waste foundry sand, the research paves the way for more efficient, sustainable, and high-performance construction materials. As the industry continues to evolve, the integration of these innovative approaches will be crucial in shaping the future of construction and contributing to a more sustainable built environment.