Fuzzy-Based Intelligent Model for Rapid Rock Slope Stability Analysis Using Qslope

Abstract

Artificial intelligence (AI) applications have introduced transformative possibilities within geohazard analysis, particularly concerning the assessment of rock slope instabilities. This study delves into the amalgamation of AI and empirical techniques to attain highly precise outcomes in the evaluation of slope stability. Specifically, our primary objective is to propose innovative and efficient methods by investigating the integration of AI within the well-regarded Qslope system, renowned for its efficacy in analyzing rock slope stability. Given the complexities inherent in rock characteristics, particularly in coastal regions, the Qslope system necessitates adjustments and harmonization with other geomechanical methodologies. Uncertainties prevalent in rock engineering, compounded by water-related factors, warrant meticulous consideration during all calculations. To address these complexities, we present a novel approach through the infusion of fuzzy set theory into the Qslope classification, leveraging fuzziness to effectively quantify and accommodate uncertainties. Our approach employs a sophisticated fuzzy algorithm encompassing six inputs, three outputs, and 756 fuzzy rules, thereby enabling a robust assessment of rock slope stability in coastal regions. The implementation of this method capitalizes on the high-level programming language Python, enhancing computational efficiency. To validate the potency of our AI-based approach, we conducted preliminary tests on slope instabilities within coastal zones, indicating a promising initial direction. The results underwent thorough evaluation, affirming the precision and dependability of the proposed method. However, it is crucial to emphasize that this work represents a first attempt to apply AI to the evaluation of rock slope stability. Our findings underscore a high degree of concurrence and expeditious stability assessment, vital for timely and effective hazard mitigation. Nonetheless, we acknowledge that the reliability of this innovative method must be established through broader applications across diverse scenarios. The proposed AI-based approach’s effectiveness is validated through a preliminary survey on a slope instability case within a coastal region, and its potential merits must be substantiated through broader validation efforts.

Verifying the Stability of the Working Fronts of Lignite Open Pits Developed in Hilly Areas—A Case Study of Jilț North Open Pit (Romania)

“…Over time, the literature has been enriched with numerous methods for stability analyses, developed by academics and researchers in the field, among which we mention: methods based on the limit equilibrium concept [1][2][3][4][5][7][8][9][10]; methods based on finite element/difference method [3,4,[7][8][9][10]12,13,49] (which is a modern and accurate one, able to be applied to complex structures); methods based on 3D analyses [29,40,41,[50][51][52][53][54] (for slopes with complex geometries, partially loaded, with variable structure, having a well-defined failure mechanism and assuming the addition of the third dimension in 2D models); probabilistic methods [55][56][57][58] (implies probabilistic analysis and processing of the physical-mechanical properties of the rocks and probabilistic computation by analytical or numerical methods); methods using Fuzzy logic [59][60][61][62][63] Considering the positive influence that lignite can exert on the stability reserve, due to its superior resistance characteristics compared to the surrounding (waste) rocks, Table 5 presents a situation regarding the position occupied by the lignite layers in relation to the slopes (existing, as of May 2023, and designed at the end of 2023). Next, in the model made with the help of the specialized software Slide (Figure 10), the values of the physical and mechanical characterist...…”

A Generalized Limit Equilibrium-Based Platform Incorporating Simplified Bishop, Janbu and Morgenstern–Price Methods for Soil Slope Stability Problems

“…Here the limiting condition for any slope is obtained by determining the best upper bound value (least) and the best lower bound value (highest) by examining different permissible states of stress [20,21]. A few of literatures [22,23,24,25] discussed the stability of unsupported rock slopes mostly based on Hoek-Brown model. In some cases, [26,27,28] machine learning techniques are involved in slope stability analysis.…”

An Infinite Slope Model Considering Unloading Joints for Spatial Evaluation of Coseismic Landslide Hazards Triggered by a Reverse Seismogenic Fault: A Case Study of the 2013 Lushan Earthquake

“…Landslide disasters usually cause significant loss of life and property damage [1,2]. Earthquakes are one of the primary triggers for landslides [3].…”