Machine-Learning-Driven Performance Optimization of Nano-Modified Construction Materials: A Bibliometric Analysis and Systematic Review

Nanomaterials improve the fresh-state behavior, mechanical performance, durability, and multifunctionality of construction materials through packing and filling, nucleation, interfacial regulation, crack bridging, transport-barrier effects, and functional responses. These benefits depend on coupled material, processing, matrix, and service variables, making conventional trial-and-error development inefficient. Machine learning (ML) offers a data-driven route for resolving nonlinear composition–processing–structure–property relationships. This review combines a CiteSpace-based bibliometric analysis of Web of Science Core Collection (WoSCC) records with a structured qualitative synthesis of ML-assisted studies on zero-dimensional nanoparticles, one-dimensional high-aspect-ratio nanomaterials, two-dimensional layered nanomaterials, functional nanomaterials, carbon dots, and emerging hybrid systems. Annual output increased from four publications in 2012 to 268 in 2025, and ML showed the strongest recent keyword burst (strength = 22.57). Across representative studies, reported test-set coefficients of determination commonly ranged from 0.858 to 0.995, although validation designs and data independence varied markedly. Tree-based ensembles were generally effective for small-to-medium tabular datasets, whereas deep learning was most defensible for images, spectra, and time-series data. Prediction estimates properties for a specified formulation; optimization uses a validated surrogate model to search the formulation or processing space and therefore requires independent experimental verification. Nano-SiO2, carbon nanotubes, and graphene oxide dominate the evidence base, while durability, microstructure, uncertainty quantification, and multifunctional co-optimization remain underrepresented. Matrix-aware standardized databases, physically constrained models, grouped or external validation, and closed-loop experiments are required for reliable intelligent design. The bibliometric component covers 1324 Web of Science Core Collection records published during 2012–2026, whereas the qualitative component applies explicit criteria for construction relevance, definition of the ML task, and sufficiency of performance and validation reporting. Bibliometric patterns and qualitative evidence are therefore reported and interpreted separately.