Published Paper Details:

The advent of data-driven methodologies has revolutionized the field of materials science, particularly in the analysis and optimization of geopolymer materials. Geopolymers, known for their superior mechanical properties and environmental sustainability, have garnered significant attention in various industrial applications. This comprehensive review delves into the multifaceted role of data science in geopolymer research, emphasizing its contribution to the advancement of material properties, performance, and application domains. We systematically explore the literature, focusing on the integration of data analytics, machine learning, and computational modeling in understanding and predicting the behavior of geopolymer composites. A detailed synthesis of findings from selected studies highlights the efficiency of data-driven techniques in enhancing the structural integrity and durability of geopolymers. The review identifies prevailing trends, pinpoints gaps in current research, and discusses the challenges encountered in data acquisition and analysis. Furthermore, we propose future research directions that promise to foster innovation and optimization in geopolymer technology. By encapsulating the state-of-the-art in data-driven geopolymer analysis, this review aims to serve as a pivotal resource for researchers and industry practitioners alike, aspiring to harness the power of data science in material development and application.