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Record W2150048266 · doi:10.1061/9780784413609.099

Lifeline System Interdependencies—Key for Resilience in Practice

2014· article· en· W2150048266 on OpenAlexaff
Alex K. Tang, Jian Li, Leonardo Dueñas‐Osorio

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsAmgen (Canada)
FundersDivision of Civil, Mechanical and Manufacturing InnovationU.S. Department of DefenseNational Science Foundation
KeywordsInterdependenceResilience (materials science)Risk analysis (engineering)Computer scienceEmergency managementCritical infrastructureKey (lock)Computer securitySystems engineeringObservabilitySCADACommunity resilienceElectric power systemProcess managementEngineeringBusinessPower (physics)Redundancy (engineering)

Abstract

fetched live from OpenAlex

This paper explores the practical role of the interaction between critical lifeline systems that can impact their mutual performance as well as their ability to serve the community, especially in the aftermath of seismic events. In particular, the emergency and recovery planning of individual lifeline systems is discussed in relation to power and telecommunication system resilience as seen from different post-earthquake reconnaissance investigations and future evolution of these increasingly coupled systems. Seismic hardening of lifeline systems is improving either by means of enforcing standards, material advances, installation practice and quality control; but, these efforts are concentrated on individual components of individual systems. Hence, a new focus is needed that includes interconnections between components and coupling across systems, while distinguishing functional and management layers for enhanced tractability. Advances in telecommunication technologies make lifeline systems rely more on automated system control and monitoring during normal and abnormal operation regimens. Therefore, communication systems are critical for uninterrupted service and coordinated restoration across modern lifeline systems after a major disaster — all essential to community resilience. An example of the new methods to assess the observability and potential management of coupled networks is thus presented, while also highlighting research and implementation tasks for resilience based on practical operational and post-disaster observations on interdependencies in the last decade.

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)0.001
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: none
Teacher disagreement score0.963
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.004
GPT teacher head0.235
Teacher spread0.230 · 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
Published2014
Admission routes1
Has abstractyes

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