Comparative bibliometric and AI-Enhanced analysis of dredged sediments, plant residues, and plastic fibers as eco-friendly additives for sustainable concrete, toward an integrated AI framework

Current research in the field of sustainable concrete is part of an evolutionary transition toward a circular economic model aimed at reducing the environmental footprint of construction materials. This study proposes a bibliometric and comparative analysis of three families of eco-sustainable additives: dredged sediments, plastic fibers, and plant residues incorporated into the concrete matrix. From a database of 245 formulations extracted from the literature (52 sediments, 147 plastic fibers, 46 plant residues), optimal performance ranges were identified: 10–20% sediment substitution, 0.3–1% plastic fiber incorporation, and 1–8% plant residue addition. Research trends were examined using VOSviewer, followed by a semantic analysis assisted by artificial intelligence to refine the interpretation of bibliometric clusters and uncover significant conceptual relationships. The AI-enhanced analysis highlights pretreatment processes (thermal activation, washing, alkali/mineralization treatments) and mixing protocols as key variables controlling mechanical, microstructural, and durability outcomes across studies. The results emphasize the specific strengths and limitations of each additive family and reveal a clearly structured scientific landscape. The integration of artificial intelligence into the bibliometric workflow provides an integrated and reproducible methodological framework, supporting future AI-driven modeling and predictive optimization of eco-sustainable concrete formulations.