Vibration-based energy harvesting in large-scale civil infrastructure: a comprehensive outlook of current technologies and future prospects
Bibliographic record
Abstract
Vibration-based energy harvesting (VEH) is a highly sought-after technology that attracts tremendous attention in various engineering applications to meet energy demands. Researchers have published numerous papers discussing various VEH devices utilizing different scavenging methodologies. However, a detailed and comprehensive insight into how this technology could be used in diverse large-scale smart civil infrastructure is lacking. To meet this need, this paper comprehensively reviews VEH techniques in the last decade within civil infrastructure, a critical domain for generating clean, sustainable energy. The review evaluates the deployment of VEH across a diverse range of large-scale infrastructure that consistently encounters vibrations, such as bridges, buildings, wind turbines, railways, tunnels, roads, and pavements. The literature is systematically classified into thematic case studies, employing diverse VEH methodologies, including piezoelectric (PE), electromagnetic (EM), and triboelectric (TE) transduction approaches. This review focuses explicitly on single energy harvesting (EH), where only one energy source and one transduction mechanism are used and does not include hybrid EH mechanisms. This review distinguishes itself by examining a broad spectrum of civil infrastructure, thus highlighting its novelty and expanding beyond previous reviews that focused on a single infrastructure type. Moreover, the paper identifies gaps and limitations in the current landscape over the last 10 years and articulates future research directions, culminating in key conclusions that advance the field of VEH in smart civil engineering applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".