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Record W4393931968 · doi:10.54021/seesv5n1-049

Assessing road vulnerability in seismic conditions: a comprehensive study

2024· article· en· W4393931968 on OpenAlex
Sonia Adafer, Mohamed Badaoui, Mahmoud Bensaibi, Saïd Mokhbi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSTUDIES IN ENGINEERING AND EXACT SCIENCES · 2024
Typearticle
Languageen
FieldComputer Science
TopicSeismology and Earthquake Studies
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsVulnerability (computing)SeismologyGeologyComputer scienceComputer security

Abstract

fetched live from OpenAlex

Road infrastructure is indispensable for societal functionality, yet it is highly susceptible to the devastating impacts of seismic events. This paper focuses on enhancing the resilience of road systems by systematically identifying, quantifying, and assessing factors contributing to their vulnerability during earthquakes. The core objective of this study is to develop and validate a seismic vulnerability index for road sections, which aims to evaluate and classify the susceptibility of road infrastructure to earthquake-induced damage comprehensively. This systematic approach is pivotal for guiding effective mitigation strategies and prioritization efforts. To fulfill this objective, the study employs the Analytic Hierarchy Process (AHP) to introduce a novel methodology for calculating the seismic vulnerability index, incorporating various factors that influence road vulnerability. This method allows for the accurate classification of road sections into distinct levels of susceptibility, providing a solid foundation for implementing targeted interventions and enhancing road resilience. Further, the paper validates the theoretical model through several case studies, demonstrating the practical applicability and effectiveness of the seismic vulnerability index in real-world scenarios. Additionally, the use of Geographic Information System (GIS) technology to simulate earthquake scenarios within an urban road network offers valuable insights into the potential seismic behavior of roads. These simulations are crucial for identifying critical areas that require intervention and for planning resilience-building efforts more effectively. By proposing a comprehensive framework that combines rigorous analysis, empirical validation, and advanced simulation techniques, this paper seeks to make a significant contribution to the field of infrastructure resilience. It aims to advance the understanding of road vulnerability in seismic conditions and supports the development of strategic approaches for enhancing the resilience of road infrastructure against earthquakes. Through the development and validation of a seismic vulnerability index, this study meets its primary objective, providing a valuable resource for researchers, policymakers, and practitioners in disaster management and infrastructure planning.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.070
GPT teacher head0.372
Teacher spread0.302 · 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