In the heart of Nigeria, researchers are tackling a global problem with a local solution, offering a glimpse into the future of sustainable construction. Hyginus Obinna Ozioko, a civil engineering professor at the Michael Okpara University of Agriculture, is leading the charge in exploring the use of demolished concrete aggregate (DA) as a viable alternative to natural aggregates in concrete production. His recent study, published in Discover Civil Engineering, delves into the impact of DA on concrete properties and uses predictive modeling to optimize its use, with significant implications for the energy sector and beyond.
The construction industry is a major consumer of natural resources, with concrete production alone accounting for a substantial portion of global aggregate demand. As resources dwindle and environmental concerns grow, the need for sustainable alternatives has never been more pressing. Ozioko’s research addresses this challenge head-on, investigating the feasibility of using DA, a major global waste product, as a sustainable aggregate alternative.
The study, conducted in a region with limited natural aggregates, evaluated the impact of DA on concrete workability, density, and compressive strength. Concrete mixes with 0–30% DA replacement were tested, revealing a decrease in workability as DA content increased. “The maximum slump reduction was observed at 30% DA replacement, with a significant drop of 72.7%,” Ozioko notes. Bulk density showed a slight decline, while compressive strength decreased notably, reaching a 56.7% reduction at 30% DA replacement.
To quantify these effects, the research employed ANOVA, confirming significant strength differences among the mixes. Post-hoc tests indicated that DA replacements of 2–7% had no significant effect on strength, but reductions became notable at ≥10% (p < 0.05). This finding underscores the importance of optimizing DA replacement levels to maintain concrete performance. But how can the industry predict and optimize DA usage in concrete mixes? Ozioko and his team turned to machine learning, developing three predictive models: linear regression, polynomial regression, and artificial neural networks. The linear regression model outperformed the others, with an R2 value of 0.801, demonstrating its potential for practical application in the field. The commercial impacts of this research are far-reaching, particularly for the energy sector. With the increasing demand for sustainable and resilient infrastructure, the use of DA in concrete production could significantly reduce the environmental footprint of energy projects. Moreover, as natural aggregate resources become scarcer and more expensive, the energy sector could benefit from a cost-effective and sustainable alternative. Ozioko's work is not just about finding a new use for waste material; it's about reshaping the future of construction. By optimizing DA usage and leveraging predictive modeling, the industry can move towards a more sustainable and efficient future. As Ozioko puts it, "The findings underscore DA’s potential as a sustainable aggregate alternative, emphasizing the importance of optimizing replacement levels to maintain concrete performance." The study, published in Discover Civil Engineering, which translates to "Explore Civil Engineering," is a testament to the power of local research in addressing global challenges. As the construction industry continues to evolve, Ozioko's work serves as a beacon, guiding the way towards a more sustainable and innovative future. The energy sector, in particular, stands to gain from this research, as it strives to build a more resilient and eco-friendly infrastructure. The question now is, how quickly can the industry adapt and integrate these findings into practice? The future of sustainable construction may well depend on it.