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Record W3005451847 · doi:10.5267/j.esm.2019.12.002

A creative rapid assembly modular free form pavement for post-disaster temporary roads and sidewalks

2020· article· en· W3005451847 on OpenAlexvenueno aff
Yahya Aliabadizadeh, Farzaneh Tahmoorian, Saeed Nemati, Bijan Samali, P. Sharafi

Bibliographic record

VenueEngineering Solid Mechanics · 2020
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsnot available
Fundersnot available
KeywordsModular designTransport engineeringCivil engineeringStructural engineeringEngineeringForensic engineeringConstruction engineeringArchitectural engineeringComputer science

Abstract

fetched live from OpenAlex

Immediate aid to survivors of a natural disaster is the keynote to crisis management. Providing temporary access is one of the most important principles of immediate relief. However, in the postdisaster conditions, it is not possible to use road construction machinery, especially in rural areas. Therefore, in this study, the feasibility of using a Rapid Assembly Building (RAB) system for the temporary pavement with the possibility of rapid construction, which follows the natural topography of the place, is investigated. The introduced system consists of a high-density polyurethane (PUR) foam core as well as two continuous layers of high-density polyethylene (HDPE) facings. For this purpose, the mechanical properties of the materials and composite pavement were determined by a series of laboratory tests. Then, the mechanical performance and bearing behaviour of an element of the presented pavement system was numerically modelled under AASHTO loading. Since in the post-disaster situation, it is not possible to establish the subgrade, an un-compacted subgrade is used for modelling. The results show that this system can be used well in post-disaster situations to provide a rapid, safe, yet robust road without any permanent deformation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.792
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.209
Teacher spread0.197 · 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.

Study designBench or experimental
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
Published2020
Admission routes1
Has abstractyes

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