Abstract
Fuzzy-based methods have become increasingly relevant in rock engineering because of their ability to represent uncertainty, imprecision, and gradual transitions inherent in geological and geotechnical information. This study presents a comprehensive bibliometric and science mapping analysis of fuzzy logic applications in rock engineering from 2000 to 2025, based on 1538 articles. The analysis examines the evolution, conceptual and intellectual structure, thematic development, and geographical distribution of the field. The findings reveal a marked expansion of research activity, with artificial neural networks, blasting, fuzzy logic, ANFIS, ground vibration, machine learning, and rock-property estimation forming major components of the research landscape. Predictive applications are particularly concentrated on rock-property estimation and blasting-related responses, while fuzzy multi-criteria approaches constitute an important direction in risk assessment and engineering decision-making. Temporal and thematic patterns indicate an increasing integration of fuzzy reasoning with machine learning, optimization, and other data-driven techniques, reflecting a transition toward hybrid computational frameworks. The intellectual structure further demonstrates strong connections between rock mechanics, engineering geology, and computational intelligence. Despite this methodological diversification, comparatively limited attention to several engineering-critical outcomes, including flyrock, backbreak, and blast damage, indicates opportunities for further investigation. By identifying the principal applications, methodological transitions, knowledge foundations, and research gaps of the field, this study provides rock engineering researchers and practitioners with a structured basis for directing future research and developing uncertainty-aware decision-support approaches.