LLM-empowered literature mining for material substitution studies in sustainable concrete

Abstract

Substituting constituents within concrete with lower impact materials is of utmost importance for the sustainable transition of the concrete industry. Systematic analyses of knowledge within the published literature can facilitate industrial practice and focus research inquiry. To address the prohibitive workload of manual review and the multifaceted linguistic complexity of communication within the domain, this study develops an automatic literature mining framework combining lightweight large language models (LLMs) (pythia-2.8B) with multiple-choice instructions. The current landscape, temporal trends, and future directions of concrete material substitution studies were analyzed using the extracted information. Although supplementary cementitious materials (SCMs) have remained a research hotspot, results revealed a systematic shift in recent studies from commercial SCMs to other materials. Geopolymer and fine aggregate studies have surged in the recent period, while clinker feedstock and filler studies have declined. Lime-pozzolan cement has been an underexplored application but emerges as a potentially promising future research direction.