Application of the modified Q-slope classification system for sedimentary rock slope stability assessment in Iran

Azarafza, Mohammad; Nanehkaran, Yaser Ahangari; Rajabion, Lila; Akgün, Haluk; Rahnamarad, Jafar; Derakhshani, Reza; Raoof, Amir

doi:10.1016/j.enggeo.2019.105349

Cited by 39 publications

(24 citation statements)

References 31 publications

(38 reference statements)

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“…The presented article describes the integrated aspect of rock mass rating (SMR) and Q slope systems (SMR-Q slope ) methods which are used for geomechanical classification and quantification of rock mass characteristics. It was used to both benefits, primarily as flexible empirical approaches to rock mass quantifications and investigate the various issues of the discontinuous to provide a suitable description in design applications [1] . Throughout the present investigation, the two geomechanical classifications, SMR and Q slope been applied to Isfahan Province, Iran, which prepared the appropriate database for a primary check on stability status for studied cases.…”

Section: Methods Detailsmentioning

confidence: 99%

“…It is derived from the Q-system which used globally for the characterisation of rock exposures, drill core and tunnels under construction for over 40 years [7] , [8] . The Q slope used Q system parameters to slope stability assessments which are modified by some scholar [1] which is calculated using the expression [9] , [10] : Q slope = (RQD/J n ) × (J r /J a ) × (J wice /SRF slope ) where RQD : rock quality designation, J n : joint set number, J r : joint roughness number, J a : joint alteration number, J wice and slope relevant strength reduction factors ( SRF ) are applied for long-term exposure to various conditions. The authors are present experimental tables to evaluate the value of each parameter on the field.…”

Section: Methods Detailsmentioning

confidence: 99%

“…3 . By using the results of the Q slope stability number [1 , 6] and SMR support suggestion can be provided the appropriate maintenance system for slopes. Table 3 presents the SMR and Q slope data for studied slopes.…”

Section: Methods Detailsmentioning

confidence: 99%

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An empirical method for slope mass rating-Qslope correlation for Isfahan province, Iran

et al. 2020

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The presented article provides an empirical method on rock slope classification, slope mass rating (SMR), Q slope , stability condition, failure type and stabilisation procedures for 35 road/railway discontinuous rock slopes after field surveys in Isfahan Province of Iran. Also, it presents the empirical correlation for SMR and Q slope classification system that prepares a link between the stability status (safety factor, reliability condition) and stabilisations (failure mechanism, support system) which performed on natural/trench slopes cases related sedimentary rocks cuts in the studied region. As results, the SMR-Q slope equation for Isfahan Province obtained as SMR = 11.89 ln(Q slope ) + 71.92 (R 2 = 0.756). • This method can be useful on a stability assessment and providing appropriate stabilisations for the discontinuous rock slope based on simple assumptions where used in different geotechnical projects such as road/railway slope, excavations, open-pit mining, trench boring, etc. • This method can be useful for quick calculation of stability conditions and suggestion of slope maintenance system in a short time as preliminary reactions. • This method can be used as an effective way to convert SMR and Q slope equations and used both benefits in geo-engineering application faced with discontinuous rock masses. • This method can be useful for future research on the empirical geomechanically classification and rock mass preliminary quantifications. • This method can be used as an appropriate database for SMR and Q slope classification.

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Section: Methods Detailsmentioning

confidence: 99%

Section: Methods Detailsmentioning

confidence: 99%

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An empirical method for slope mass rating-Qslope correlation for Isfahan province, Iran

et al. 2020

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“…Before any geotechnical investigation for the determination of structurally controlled instabilities within the tunnel sections, slope stability assessment studies are pre-requisites [2]. The Q-slope system has an empirical method to predict rock slope stability, and correlation relationship assessment between Q-slope and slope stability degree also possible [8]. Intense rainfall is considered as a prime factor for landslides, even though systematic integration of various surface features that trigger landslide hazard is a significant aspect in landslide hazard zonation study [31].…”

Section: Introductionmentioning

confidence: 99%

“…Density is the total length/unit area, and the generation of drainage density and the lineament density map was carried out in the ArcGIS line density tool. The geology and soil data used are from secondary sources (GSI and NBSS).Figures 5,6,7,8,9,10,11,and 12 show details of thematic layers generated for overlay analysis to develop an LSM for the study area.…”

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confidence: 99%

Landslides susceptibility assessment by analytical network process: a case study for Kuttiyadi river basin (Western Ghats, southern India)

Swetha

Gopinath

2020

SN Appl. Sci.

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In the case of Western Ghats of Kerala located in southern India, landslides become the most devastating hazard during the monsoon season is mainly because of intense rainfall. The recent landslides struck in the months of June and August 2018 were the worst in the history of the state as the damages to life and property happened were very dangerous. From this evidences, being a landslide-prone area a hazard zonation attempted using the terrain susceptibility concept for highlands of Kuttiyadi river basin is a part of Western Ghats in Kozhikode district with a maximum elevation of 1640 m above MSL. Through landslide susceptibility mapping, the assessment of areas likely to have or probable to landslides in the future identified using geospatial and multi-criteria decision-making (MCDM) tool mix. This work explores the usage of the MCDM tool-analytical network process (ANP)-based decision-making approach to determine the relationship between each landslide conditioning factors and to assign weight effectively. Factor maps of various landslide triggering parameters such as slope, soil, landuse, drainage density, stage of landform, lineament density, geology, and geomorphology were prepared and integrated through geospatial analyses in the GIS platform, and the study area (318 km 2) is categorized into four classes of unstable (121 km 2), moderately unstable (76 km 2), stable to moderately stable (124 km 2), high vulnerability (67 km 2), and very high vulnerability (74 km 2). The result of this work concluded that the application of ANP is an effective tool to weight the factors causing landslides and to generate landslide susceptibility zonation map with maximum accuracy using geospatial technology. The validation of output data with landslide inventory data is satisfying it.

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Empirical and Numerical Evaluation of a Cut Slope Near Rishikesh, India

Pandey

Kainthola

Singh

2022

Lecture Notes in Civil Engineering

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Application of the modified Q-slope classification system for sedimentary rock slope stability assessment in Iran

Cited by 39 publications

References 31 publications

An empirical method for slope mass rating-Qslope correlation for Isfahan province, Iran

An empirical method for slope mass rating-Qslope correlation for Isfahan province, Iran

Landslides susceptibility assessment by analytical network process: a case study for Kuttiyadi river basin (Western Ghats, southern India)

Empirical and Numerical Evaluation of a Cut Slope Near Rishikesh, India

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