Evidence map›Paper›PMID 31387488›Full record

ArticleJournal of the Royal Society, Interface2019

Robust component: a robustness measure that incorporates access to critical facilities under disruptions.

Shangjia Dong, Haizhong Wang, Ali Mostafavi, Jianxi Gao

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Modeling Culvert Failure and Community Accessibility Impacts During Hazards.Risk analysis : an official publication of the Society for Risk Analysis · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Shangjia DongSchool of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331, USA.
Haizhong WangSchool of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331, USA.
Ali MostafaviZachry Department of Civil Engineering, Texas A&M University, College Station, TX 77840, USA.
Jianxi GaoDepartment of Computer Science, Rensselaer Polytechnic Institute, Lally Hall 207, Troy, NY 12180, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The objective of this paper is to integrate the post-disaster network access to critical facilities into the network robustness assessment, considering the geographical exposure of infrastructure to natural hazards. Conventional percolation modelling that uses generating function to measure network robustness fails to characterize spatial networks due to the degree correlation. In addition, the giant component alone is not sufficient to represent the performance of transportation networks in the post-disaster setting, especially in terms of the access to critical facilities (i.e. emergency services). Furthermore, the failure probability of various links in the face of different hazards needs to be encapsulated in simulation. To bridge this gap, this paper proposed the metric robust component and a probabilistic link-removal strategy to assess network robustness through a percolation-based simulation framework. A case study has been conducted on the Portland Metro road network during an M9.0 earthquake scenario. The results revealed how the number of critical facilities severely impacts network robustness. Besides, earthquake-induced failures led to a two-phase percolation transition in robustness performance. The proposed robust component metric and simulation scheme can be generalized into a wide range of scenarios, thus enabling engineers to pinpoint the impact of disastrous disruption on network robustness. This research can also be generalized to identify critical facilities and sites for future development.

Indexed as

Computer SimulationDisaster PlanningModels, TheoreticalEarthquakesHumansTransportationnetwork access to critical facilitiesnetwork robustnesspercolation modellingprobabilistic infrastructure failure

Identifiers

PMID31387488
PMCPMC6731514

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.