Knowledge-guided data-driven design of ultra-high-performance geopolymer (UHPG)

Geopolymer has been identified as a promising family of sustainable construction materials alternative to cement-based materials. However, designing geopolymer utilizing solid wastes is a challenging task given the large variations of solid wastes in their physical and chemical properties. To overcome this challenge, this paper proposes a knowledge graph-guided data-driven approach to design geopolymer utilizing solid wastes, aimed at achieving high mechanical properties, low material cost, and low carbon emission, while largely improving material discovery efficiency. The proposed approach seamlessly integrates knowledge graph, machine learning, and multi-objective optimization, and has been utilized to design ultra-high performance geopolymer (UHPG). This approach has two main novelties: (1) The incorporation of knowledge graph imparts geopolymer domain knowledge, making the machine learning model interpretable and compliant with domain knowledge. (2) The consideration of physical and chemical properties of raw materials enables the utilization of various solid wastes. The results show that the proposed approach can reasonably predict geopolymer properties, interpret prediction results, and optimize UHPG design.