Improved interdigital sensors for structural health monitoring of composite retrofit systems
Bibliographic record
Abstract
In this article, an improved sensor performance based on detecting the dielectric variations in composite—concrete interface and linking these variations to bond deterioration is presented. The use of composites in retrofitting concrete structures is becoming a standard technique worldwide. The bond between the composite system and the concrete element surface is essential to ensure achieving the designed performance. However, there is little research on the development of an economic structural health monitoring technique for monitoring bond quality in large structures, such as buildings and bridges. The improved sensing technique utilizes an Interdigital Capacitance Sensor design to increase the signal-to-noise ratio and to detect shallow—depth bond deteriorations. An analytical modeling technique was used to analyze the influence of the different sensor parameters, especially the dependency of the capacitance signals on the sensor geometrical dimensions. Two-dimensional finite element (FE) simulations were also used to assess different related design parameters and to verify the experimental results. A concrete slab retrofitted with composites containing pre-induced air voids to simulate bond deteriorations defects were constructed and inspected in a laboratory setting. Good agreements were found between experimental capacitance signal response parameters and those predicated from the FE simulations of the slab specimen.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".