Stability analysis of road cut slopes in Sikkim Himalaya along national highway 10, India

Dutta, Kuldeep; Wanjari, Nishchal; Misra, Anil Kumar

doi:10.1080/24749508.2023.2182067

Cited by 4 publications

(1 citation statement)

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“…Rainfall data was sourced from the respective state meteorological department, while seismic data was taken from the seismic zonation map of India. Other sufficient relevant information and field data were collected from all 81 natural and man-made rock slopes during fieldwork Ghosh et al 32 , Siddique et al 33 , Sardana et al 34 Khanna and Dubey 35 , Verma et al 36 , Kumar et al 37 , Dutta et al 38 , Jaiswal et al 39 . Rock samples collected from the field were tested in the laboratory according to ASTM standard: D2938 to determine the uniaxial compressive strength (UCS).…”

Section: Methodology and Case Studiesmentioning

confidence: 99%

A novel proposed classification system for rock slope stability assessment

Jaiswal,

Verma,

Singh

2024

Sci Rep

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The present study introduces “rock slope instability score (RSIS)” a novel classification system for assessing rock slope stability. It takes into account geological and geotechnical parameters, as well as the impact of human activities and triggering parameters, which have become more frequent due to climate change and few of them have been ignored in existing classifications. The study focuses on rock slopes of various lithologies from the Indian Himalayas. The development of this new classification system is based on the examination of 81 different rock slopes from various states of the Indian Himalayas. Extensive field surveys, rock sampling, geotechnical laboratory tests, and ground measurements have been conducted at the various slope sites to establish a comprehensive scoring system for the stability assessment. The distributions of weightage to each parameter have been considered, corresponding to its degree of impact in causing slope instability. Sensitivity analysis of all defined parameters of RSIS system has revealed that the majority of the parameters exhibit a strong positive correlation, with Pearson correlation coefficients ranging from 0.74 to 0.61. However, two parameters, namely discontinuity dip and the relationship between slope & discontinuity direction, gives moderate relationship with correlation coefficient values of 0.48 and 0.41, respectively. To avoid any designer biasness in the system, several individuals gathered data set at different times. The proposed classification system has demonstrated a strong correlation with the actual slope condition, and it is quite promising. The outcome of RSIS classification for studied 81 slopes classified 2 slopes under stable condition, 21 slopes as partially stable, 44 as unstable, and 14 as completely unstable.

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