A Hierarchical Modular Fuzzy Model for Instability Susceptibility Assessment and Stabilization Decision Support in Rock Slopes

Traditional rock mass evaluation methods have three main limitations: (1) their application depends largely on specialist judgment; (2) their discrete classification approach, such as RMR (Rock Mass Rating) and SMR (Slope Mass Rating), leads to abrupt transitions between categories; and (3) the interaction among geomechancial parameters is limited; these aspects reduce their ability to represent slope behavior in a gradual manner. The main objective of this research was to develop a model capable of representing gradual transitions between geomechanical conditions and the interaction among parameters related to susceptibility to instability. The model uses a hierarchical modular framework based on the Mamdani fuzzy inference mechanism, allowing the incorporation of expert knowledge through linguistic rules. It is implemented in a graphical environment that allows users to directly use geomechanical parameters obtained through conventional characterization or from three-dimensional digital models derived from UAV (Unmanned Aerial Vehicle) photogrammetry. Model consistency was evaluated through a sensitivity analysis, which verified the model’s response coherence across variations in input parameters. The graphical evaluation tool was then applied to three real case studies with different geomechanical configurations, and the results were compared with those from traditional methods (RMR and SMR). The results showed differences between traditional and fuzzy approaches, as our proposal links recommendations to specific geomechanical conditions across different evaluation levels, identifying conditions for potential intervention measures. In addition, the model enables the zonification of instability susceptibility, facilitating its use in future risk analyses. Our model is intended for application under normal slope conditions, without accounting for extreme events or external dynamic loads, such as seismic activity, groundwater level variations, infiltration, or high-mountain conditions.

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Publication Details

Journal
Applied Sciences
Published
2026-08-03
DOI
https://doi.org/10.3390/app16157706
Primary Topic
Landslides and related hazards
Type
article
Field-Weighted Citation Impact
0.00

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article

A Hierarchical Modular Fuzzy Model for Instability Susceptibility Assessment and Stabilization Decision Support in Rock Slopes

Marsella Gissel Rodríguez-Servín, Luis Alberto Morales-Rosales, Juan Carlos López-Pimentel, Mariana Lobato Báez et al.
Applied Sciences
Landslides and related hazards
article

A Hierarchical Modular Fuzzy Model for Instability Susceptibility Assessment and Stabilization Decision Support in Rock Slopes

Marsella Gissel Rodríguez-Servín, Luis Alberto Morales-Rosales, Juan Carlos López-Pimentel, Mariana Lobato Báez, José Eleazar Arreygue-Rocha, José Manuel Díaz-Barriga
article en

Abstract

Traditional rock mass evaluation methods have three main limitations: (1) their application depends largely on specialist judgment; (2) their discrete classification approach, such as RMR (Rock Mass Rating) and SMR (Slope Mass Rating), leads to abrupt transitions between categories; and (3) the interaction among geomechancial parameters is limited; these aspects reduce their ability to represent slope behavior in a gradual manner. The main objective of this research was to develop a model capable of representing gradual transitions between geomechanical conditions and the interaction among parameters related to susceptibility to instability. The model uses a hierarchical modular framework based on the Mamdani fuzzy inference mechanism, allowing the incorporation of expert knowledge through linguistic rules. It is implemented in a graphical environment that allows users to directly use geomechanical parameters obtained through conventional characterization or from three-dimensional digital models derived from UAV (Unmanned Aerial Vehicle) photogrammetry. Model consistency was evaluated through a sensitivity analysis, which verified the model’s response coherence across variations in input parameters. The graphical evaluation tool was then applied to three real case studies with different geomechanical configurations, and the results were compared with those from traditional methods (RMR and SMR). The results showed differences between traditional and fuzzy approaches, as our proposal links recommendations to specific geomechanical conditions across different evaluation levels, identifying conditions for potential intervention measures. In addition, the model enables the zonification of instability susceptibility, facilitating its use in future risk analyses. Our model is intended for application under normal slope conditions, without accounting for extreme events or external dynamic loads, such as seismic activity, groundwater level variations, infiltration, or high-mountain conditions.

Applied SciencesVol. 16(15)
Instituto Tecnológico de Puebla (MX), Universidad Michoacana de San Nicolás de Hidalgo (MX), Secretaría de Ciencia, Humanidades, Tecnología e Innovación (MX), Universidad Panamericana (MX)
Consejo Nacional de Ciencia y Tecnología
Openalex Percentile: Top 5%
Landslides and related hazards
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