In the quest for sustainable construction materials, researchers have turned to an unlikely ally: marble powder, an industrial by-product that often ends up as waste. A recent study published in *Frontiers in Built Environment* (which translates to *Frontiers in the Built Environment*) demonstrates how this powder can be repurposed as a supplementary cementitious material (SCM), reducing the environmental impact of cement manufacturing. But what sets this research apart is its innovative use of artificial neural networks (ANNs) and Blockchain-Rock technology to optimize concrete mix designs and ensure data integrity.
Lead author Moutaman M. Abbas, whose affiliation is not specified, and his team trained an ANN model on a substantial dataset of 629 entries to predict the mechanical properties of marble powder concrete. The results were impressive: Model I achieved an R² value of 0.99 and an RMSE of 1.63, while Model II hit a perfect R² of 1.00 with an RMSE of 0.21. These figures outperform other machine learning models, such as a feedforward ANN and a general regression neural network (GRNN), underscoring the effectiveness of the proposed ANN architecture.
“The ANN model’s ability to predict compressive and tensile strength with such high accuracy is a game-changer,” Abbas remarked. “It drastically reduces the need for standard long-duration tests, saving time and resources.”
But the innovation doesn’t stop at predictive modeling. The study also introduces Blockchain-Rock technology to ensure secure and tamper-free tracking of material origin and concrete mixes. This transparency in the supply chain can enhance efficiency and trust among stakeholders.
“The combination of AI and blockchain technology offers a robust solution for sustainable construction,” Abbas explained. “It not only optimizes the mix design but also ensures that the data is reliable and traceable.”
The commercial implications for the energy sector are significant. Construction materials that are both sustainable and high-performing can reduce the carbon footprint of buildings and infrastructure, aligning with global sustainability goals. The ability to predict mechanical properties accurately can also lead to more efficient use of materials, reducing waste and costs.
“This research opens up new possibilities for the construction industry,” said Abbas. “By leveraging AI and blockchain, we can create smarter, more sustainable materials that meet the demands of modern construction.”
Future work could explore real-time IoT integration and larger datasets to further improve predictive accuracy and industrial applicability. As the construction industry continues to evolve, the integration of advanced technologies like ANNs and blockchain could pave the way for more efficient, sustainable, and transparent practices.
In a field where innovation is key, this research offers a compelling vision of how technology can drive sustainability and efficiency in construction.