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Record W4400243311 · doi:10.11159/ijci.2024.006

Stability Analysis of Surface and Subsurface Geological Hazards Using Numerical Approach in Hydropower Project of India- a Case Study

2024· article· en· W4400243311 on OpenAlexvenueno aff
M. Roy, Nirmal Singh, Sajan Moideen

Bibliographic record

VenueInternational Journal of Civil Infrastructure · 2024
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsHydropowerGeologic hazardsGeologyStability (learning theory)Subsurface flowEnvironmental scienceGeotechnical engineeringMining engineeringCivil engineeringLandslideEngineeringComputer scienceGroundwater

Abstract

fetched live from OpenAlex

The paper reviews major geological problems encountered during construction of a mega hydropower project in the Lesser Himalayan region of Himachal Pradesh, India.Major hindrances encountered during construction of the project were backslope failure during construction of surface powerhouse in metavolcanics with slumped rockmass and rock bursting during tunnelling in hard and brittle quartzite.Two different modes of failures occurred during excavation of 180m long backslope of surface Powehousecircular failure along highly weathered slumped rockmass and planar failure along crushed rock filled valley dipping joints.Stability assessment of powerhouse backslope was carried out through Finite element based software and Limit equilibrium based software for analysis of planar failure in jointed rockmass and determine critical slip circle of failure and Factor of Safety of the slope through strength reduction technique.Optimum support analysis for stabilization of cutslope was carried out through numerical simulation.Deformation meshes, accumulated principal strain and total displacement are the other parameters obtained from the results computed and analysed using FEM.The paper also back analyzes magnitude of in-situ stresses in rock bursting zones encountered during tunnelling using finite element based software to determine role of depth of overburden and field stress ratio in causing rock bursting.The result indicates that magnitude of principal stresses and yielding zones increase with depth of overburden and decrease in field stress ratios.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.284
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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