In the quest for sustainable construction materials, a novel approach leveraging artificial intelligence (AI) is making waves. Researchers, led by Abba Mas’ud Alfanda from the Department of Civil Engineering and Institute for Disaster Management and Reconstruction at Sichuan University in China, have developed an AI model to predict the compressive strength of rice husk ash (RHA) sandcrete blocks. This innovation, published in *Academia Engineering* (which translates to *Academic Engineering*), could revolutionize the construction industry’s approach to eco-friendly materials.
Rice husk ash, a byproduct of rice milling, has emerged as a promising alternative to traditional construction materials. Its use in sandcrete blocks—commonly used in building construction—reduces environmental impact by repurposing agricultural waste. However, predicting the engineering properties of RHA sandcrete blocks has been a persistent challenge, hindering its widespread adoption.
Alfanda and his team tackled this issue by training various AI models on a dataset of 182 samples, each characterized by four key variables. The models included multiple linear regression, support vector machine, and artificial neural network (ANN). The results were impressive, with the ANN model achieving a coefficient of determination (R2) of 0.94, indicating a high level of predictive accuracy.
“This study demonstrates the potential of AI in enhancing the reliability and efficiency of material property predictions,” Alfanda explained. “By integrating AI technology into the assessment process, we can advance sustainable construction practices and optimize mix design and production strategies.”
The implications for the construction industry are significant. Accurate predictions of compressive strength can lead to more consistent product quality, reduced material waste, and lower production costs. For the energy sector, this research could pave the way for more sustainable building practices, aligning with global efforts to mitigate climate change and reduce resource depletion.
As the world grapples with rapid population growth and increasing resource limitations, innovations like this are crucial. Alfanda’s research not only highlights the potential of AI in construction but also underscores the importance of interdisciplinary collaboration in driving sustainable development.
“This research is a step forward in making eco-friendly construction materials more viable and reliable,” Alfanda added. “It’s about creating a future where sustainability and efficiency go hand in hand.”
With the construction industry increasingly focused on sustainability, this AI-driven approach could shape future developments in material science and engineering. As Alfanda’s work gains traction, it may inspire further research and practical applications, ultimately contributing to a greener and more resilient built environment.