Bibliographic record
Abstract
The process of nondestructive evaluation (NDE) or structural health monitoring (SHM) uses a series of new and existing technologies that are designed to monitor various internal and external conditions of civil engineering infrastructure, such as bridges. This article discusses the technological aspects of SHM, describes some companies involved in providing SHM services, and reviews research efforts focused on monitoring and improving the structural health of the nation's bridges. Some of the technologies involved in SHM are fiber optics, wireless communications, ground-penetrating radar imaging, and information technology. Using technology, owners and engineers are able to predict more accurately the longevity of existing structures as well as design more durable, smarter structures. SHM systems can be engineered to monitor humidity, temperature, chloride ingress, corrosion potential, vibration and strains. There are also systems for monitoring structural steel on bridges, as well as the cable systems for suspension bridges. Since 1998, the Federal Highway Administration's NDE Validation Center has provided state highway agencies with independent evaluation and validation of NDE technologies, offered technical assistance and developed new NDE technologies.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".