A GPS-Integrated IoT Framework for Real-Time Monitoring of Prefabricated Building Modules During Transportation
Bibliographic record
Abstract
The transportation phase in off-site construction subjects prefabricated modules to road-induced vibrations, shocks, and handling loads that can degrade structural integrity. Existing monitoring approaches often rely on local data loggers, which not only lack real-time visibility but also fail to link structural responses to their precise spatial and temporal context. To address this gap, this study proposes a GPS-integrated Internet of Things (IoT) framework for real-time monitoring of prefabricated modules during transit. The system comprises distributed inertial sensing nodes wirelessly connected to a central gateway, which aggregates and transmits synchronized sensor and GPS data to a cloud platform for analysis and visualization. Field validation demonstrated stable multi-node data acquisition with sufficient battery life to support extended monitoring under LTE connectivity. The framework supports dual-stream analytics: (i) time- and frequency-domain assessment of structural exposure using peak acceleration, RMS, and FFT metrics, and (ii) causal inference of road events (e.g., potholes, bumps, sharp turns). Vertical acceleration emerged as the most responsive diagnostic channel for capturing road-induced excitations, while gyroscope-derived motion profiles distinguish between driver maneuvers and road irregularities. Through seamless integration of structural and geospatial data in a scalable, low-cost system, this framework enables actionable insights for route planning, condition-based inspection, and improved logistics management in modular construction.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".