Assessing road vulnerability in seismic conditions: a comprehensive study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".