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Record W4405317594 · doi:10.3390/geosciences14120342

Case Study on the Evaluation of Rock Cut Stability for Highways in Egypt: Implications for Transportation Infrastructure and Safety

2024· article· en· W4405317594 on OpenAlexaff
Wael Rashad Elrawy Abdellah, Stephen Butt, Ahmed Rushdy Towfeek, Mahmoud M. Abozaied, Faisal A. Ali, Mahrous A. M. Ali, Abdullah Omar Mohammad Bamousa

Bibliographic record

VenueGeosciences · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsTransport engineeringStability (learning theory)Transportation infrastructureBusinessForensic engineeringCivil engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

This study addresses critical stability concerns along a key segment of the Egyptian highway linking Aswan and Cairo, focusing on a one-kilometer rock-cut section that is vital for transportation and commerce. Recent evaluations have highlighted significant rockfall and slope instability risks in this area, posing serious safety challenges. The primary objective is to identify and analyze the factors contributing to slope instability, assess potential rockfall hazards, and recommend effective mitigation strategies. To achieve this, this study employs a comprehensive, multi-faceted methodology. Key variables influencing slope stability are first identified, followed by a detailed analysis of discontinuity data using stereographic projection based on joint surveys. Rockfall propagation distances are then modeled through specialized software, while the Plaxis 2D tool 2023.2(V23.2.0.1059) is applied for advanced numerical modeling of slope behavior. The results indicate a pressing need for mitigation measures to address ongoing instability issues, including planar and wedge failures and raveling rockfalls, which pose considerable safety risks to road users. This study highlights the necessity of a robust and comprehensive mitigation strategy to ensure road safety and support uninterrupted commercial activity along this essential highway.

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)0.001
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 score0.075
Threshold uncertainty score0.141

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.040
GPT teacher head0.295
Teacher spread0.255 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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