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Energy Harvesting from Train-Induced Response in Bridges

2014· article· en· W2095003778 on OpenAlex

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

VenueJournal of Bridge Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsTrinity College
Fundersnot available
KeywordsEnergy harvestingTrainBridge (graph theory)Structural health monitoringEnergy (signal processing)VibrationWork (physics)EngineeringComputer scienceStructural engineeringMechanical engineeringAcoustics

Abstract

fetched live from OpenAlex

The integration of large infrastructure with energy-harvesting systems is a growing field with potentially new and important applications. The possibility of energy harvesting from ambient vibration of bridges is a new field in this regard. This paper investigates the feasibility of energy harvesting for a number of trains considering their passage over a bridge. The power that can be derived from an energy-harvesting device due to a train crossing a bridge at different speeds is compared against typical demands of small wireless devices and is found to be adequate for powering such devices. These estimates of harvested energy also relate to the individual signatures of trains. In this work, the modeled dynamic responses of a bridge traversed by trains are compared against full-scale experimental analysis of train–bridge interactions. A potential application in structural health monitoring (SHM) using energy harvesting has also been demonstrated and compared with laboratory experimental data. Consistent and monotonic damage calibration curves have been constructed using estimated harvested energy.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.210
Teacher spread0.194 · 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