Although rock-mass rating (RMR) and tunneling quality index (Q) systems are used in different rock engineering projects as empirical design tools, their application in tunnel design is widely accepted as these systems were developed and updated for this purpose specifically. This paper reviews the work conducted by various researchers since the development of these two systems with respect to tunneling only. Compared to other empirical classification systems, these systems received international acceptance and are still used as empirical design tools in tunneling due to their continuous updates in the form of characterization and support. As the primary output of these systems is the initial support design for tunnel, however, their use in the calculation for rock-mass properties is an essential contribution of these systems in rock engineering design. Essential for the tunnel design, these rock-mass properties include the modulus of deformation, strength, Poisson’s ratio, Mohr-Coulomb parameters and Hoek-Brown constants. Other application for tunneling include the stand-up time and rock load. The uses and limitations of these systems as empirical tunnel design tools are also included in this review article for better results. Research to date indicates that if the ground behavior is also taken into account, the application of these empirical systems will be more beneficial to the preliminary design of tunnels.

In the primary and final designs of projects related to rock mechanics and engineering geology, one of the key parameters that needs to be taken into account is the intact rock elastic modulus (E). To measure this parameter in a laboratory setting, core samples with high-quality and costly tools are required, which also makes for a time-consuming process. The aim of this study is to assess the effectiveness of two meta-heuristic-driven approaches to predicting E. The models proposed in this paper, which are based on integrated expert systems, hybridize the adaptive neuro-fuzzy inference system (ANFIS) with two optimization algorithms, i.e., the differential evolution (DE) and the firefly algorithm (FA). The performance quality of both ANFIS-DE and ANFIS-FA models was then evaluated by comparing them with ANFIS and neural network (NN) models. The ANFIS-DE and ANFIS-FA models were formed on the basis of the data collected from the Azad and Bakhtiari dam sites in Iran. After applying several statistical criteria, such as root mean square error (RMSE), the ANFIS-FA model was found superior to the ANFIS-DE, ANFIS, and NN models in terms of predicting the E value. Additionally, the sensitivity analysis results showed that the P-wave velocity further influenced E compared with the other independent variables.

Using explosive material to fragment rock masses is a common and economical method in surface mines. Nevertheless, this method can lead to some environmental problems in the surrounding regions. Flyrock is one of the most dangerous effects induced by blasting which needs to be estimated to reduce the potential risk of damage. In other words, the minimization of flyrock can lead to sustainability of surroundings environment in blasting sites. To this aim, the present study develops several new hybrid models for predicting flyrock. The proposed models were based on a cascaded forward neural network (CFNN) trained by the Levenberg–Marquardt algorithm (LMA), and also the combination of least squares support vector machine (LSSVM) and three optimization algorithms, i.e., gravitational search algorithm (GSA), whale optimization algorithm (WOA), and artificial bee colony (ABC). To construct the models, a database collected from three granite quarry sites, located in Malaysia, was applied. The prediction values were then checked and evaluated using some statistical criteria. The results revealed that all proposed models were acceptable in predicting the flyrock. Among them, the LSSVM-WOA was a more robust model than the others and predicted the flyrock values with a high degree of accuracy.

Rainfall-triggered landslides pose significant risks in hilly and mountainous areas, necessitating advanced susceptibility and hazard modeling in GIS environments. This review examines research trends, influential authors, and emerging themes in this field through bibliometric analysis and content review, covering publications from 2007 to 2025 with a focus on the last five years. Recent research trends reveal a shift towards complex, physically based models incorporating multiple rainfall variables and their interactions with local conditions. Emerging trends include machine learning applications, increased spatiotemporal analysis, and rising interest in climate change impacts on landslide occurrence. However, there is a lack of attention given to how topography and other factors might explain spatial differences in rainfall. Research output has increased since 2020, driven by initiatives like the Kyoto Landslide Commitment 2020. While progress has been made in landslide susceptibility modeling, several challenges persist, such as developing standardized protocols for case study research to enhance comparability; exploring methods to integrate multiple case studies and improve generalizability; investigating emerging technologies like big data analytics to address sample limitations; improving translation of research findings for practitioners and policymakers; and enhancing methods to incorporate topographic effects on rainfall variability in susceptibility models. Many studies rely on single-site case studies, limiting result generalizability. Better integration of topographic influences on rainfall spatial variability is needed, particularly in mountainous regions. Addressing these challenges will be crucial for advancing landslide risk assessment. Future research should focus on developing robust, transferable models that account for complex environmental interactions and provide practical decision-support tools.