Preliminary estimation of rock slope stability using rock mass classification systems

1993 ◽  
Vol 30 (3) ◽  
pp. A164
Keyword(s):  
Rock Mass ◽  
Slope Stability ◽  
Rock Slope ◽  
Rock Mass Classification ◽  
Classification Systems ◽  
Rock Slope Stability ◽  
Preliminary Estimation ◽  
Mass Classification

scholarly journals Assessment of a Modified Rock Mass Classification System for Rock Slope Stability Analysis in the Q-system

2015 ◽  
Vol 19 (2) ◽  
pp. 147-152 ◽  
Author(s):  
Davood Fereidooni ◽  
Gholam Reza Khanlari ◽  
Mojtaba Heidari
Keyword(s):  
Rock Mass ◽  
Slope Stability ◽  
Classification System ◽  
Rock Slope ◽  
Rock Mass Classification ◽  
Classification Systems ◽  
Rock Slope Stability ◽  
Rock Slopes ◽  
El Sistema ◽  
Mass Classification

<p>This paper explores the applicability of a modified Q classification system and its component parameters for analysis and conclusion of site investigation data to estimate rock slope stability. Based on the literature, Q classification system has high applicable potential for evaluation of rock mass quality. Therefore, in this study, it was used with RMR and SMR rock mass classification systems to assess stability or instability of different rock slopes along the Hamedan-Ganjnameh-Tuyserkan road, Hamedan province west of Iran. Furthermore, a modified rock mass classification system namely Slope Quality Rating (SQR) was proposed based on the correction of the Q classification parameters and calculating some new parameters such as dip and strike of discontinuities and the method of rock excavation or blasting. For this purpose, the SMR and RMR rock mass classifications were also needed. By measuring SQR for different rock slopes, it will be possible to measure Slope Mass Rating (SMR).</p><p> </p><p><strong>Evaluación del sistema Q modificado de clasificación del macizo rocoso para el análisis de estabilidad de pendiente de roca</strong></p><p> </p><p><strong>Resumen</strong></p>Este artículo explora la aplicabilidad del sistema de clasificación Q modificado y sus parámetros para analizar y determinar la información estimada de estabilidad de pendiente de roca en el sitio determinado de estudio. Según la literatura, el sistema de clasificación Q tiene un alto potencial de aplicabilidad paral a evaluación de la calidad del macizo rocoso. En este estudio además se utilizó el sistema Q junto con los sistemas Índice de Masa de Pendiente (SMR) y Clasificación Geomecánica de Bienawski (RMR) para evaluar la estabilidad e inestabilidad de diferentes pendientes rocosas en la carretera Hamedan-Ganjnameh-Tuyserkan, de la provincia de Hamedan, en el Oeste de Irán. Además, se propone el Índice de Calidad de Pendiente (SQR), un sistema de clasificación de macizo rocoso modificado, a partir de la corrección de los parámetros de clasificación Q y el cálculo de nuevos parámetros como pendiente y caída de las discontinuidades y el método de excavación o explosión de la roca. Para esta propuesta también se utilizaron las clasificaciones SMR y RMR. La medición SQR en diferentes pendientes hizo posible el cálculo del sistema SMR.</p>

scholarly journals Assessment of Slope Instability and Risk Analysis of Road Cut Slopes in Lashotor Pass, Iran

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Mohammad Hossein Taherynia ◽  
Mojtaba Mohammadi ◽  
Rasoul Ajalloeian
Keyword(s):  
Rock Mass ◽  
Risk Analysis ◽  
Rock Slope ◽  
Classification Systems ◽  
Slope Instability ◽  
Rock Slope Stability ◽  
Rock Slopes ◽  
The Stability ◽  
Cut Slopes ◽  
Road Cut Slopes

Assessment of the stability of natural and artificial rock slopes is an important topic in the rock mechanics sciences. One of the most widely used methods for this purpose is the classification of the slope rock mass. In the recent decades, several rock slope classification systems are presented by many researchers. Each one of these rock mass classification systems uses different parameters and rating systems. These differences are due to the diversity of affecting parameters and the degree of influence on the rock slope stability. Another important point in rock slope stability is appraisal hazard and risk analysis. In the risk analysis, the degree of danger of rock slope instability is determined. The Lashotor pass is located in the Shiraz-Isfahan highway in Iran. Field surveys indicate that there are high potentialities of instability in the road cut slopes of the Lashotor pass. In the current paper, the stability of the rock slopes in the Lashotor pass is studied comprehensively with different classification methods. For risk analyses, we estimated dangerous area by use of the RocFall software. Furthermore, the dangers of falling rocks for the vehicles passing the Lashotor pass are estimated according to rockfall hazard rating system.

Why the future of rock mass classification systems requires revisiting their empirical past

2021 ◽  
pp. qjegh2021-039
Author(s):  
Beverly Yang ◽  
Amichai Mitelman ◽  
Davide Elmo ◽  
Doug Stead
Keyword(s):  
Rock Mass ◽  
Rock Mass Classification ◽  
Classification Systems ◽  
Empirical Knowledge ◽  
Rock Engineering ◽  
Geotechnical Data ◽  
Use Of Technology ◽  
Data Collection Process ◽  
Engineering Problems ◽  
Mass Classification

Despite recent efforts, digitisation in rock engineering still suffers from the difficulty in standardising and statistically analysing databases that are created by a process of quantification of qualitative assessments. Indeed, neither digitisation nor digitalisation have to date been used to drive changes to the principles upon which, for example, the geotechnical data collection process is founded, some of which have not changed in several decades. There is an empirical knowledge gap which cannot be bridged by the use of technology alone. In this context, this paper presents the results of what the authors call a rediscovery of rock mass classification systems, and a critical review of their definitions and limitations in helping engineers to integrate these methods and digital acquisition systems. This discussion has significant implications for the use of technology as a tool to directly determine rock mass classification ratings and for the application of machine learning to address rock engineering problems.

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