Preliminary Insights on Moisture Content Measurement in Square Timbers Using GPR Signals and 1D-CNN Models
Why this work is in the frame
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Bibliographic record
Abstract
Accurately measuring the moisture content (MC) of square timber is crucial for ensuring the quality and performance of wood products in wood processing. Traditional MC detection methods have certain limitations. Therefore, this study developed a one-dimensional convolutional neural network (1D-CNN) model based on the first 8 nanoseconds of ground-penetrating radar (GPR) signals to predict the MC of square timber. The study found that the mixed-species model exhibited effective predictive performance (R2 = 0.9864, RMSE = 0.0393) across the tree species red spruce, Dahurian larch, European white birch, and Manchurian ash (MC range 0%–133.1%), while single-species models showed even higher accuracy (R2 ≥ 0.9876, RMSE ≤ 0.0358). Additionally, the 1D-CNN model outperformed other algorithms in automatically capturing complex patterns in GPR full-waveform amplitude data. Moreover, the algorithms based on full-waveform amplitude data demonstrated significant advantages in detecting wood MC compared to those based on a traditional time–frequency feature parameter. These results indicate that the 1D-CNN model can be used to optimize the drying process and detect the MC of load-bearing timber in construction and bridge engineering. Future work will focus on expanding the dataset, further optimizing the algorithm, and validating the models in industrial applications to enhance their reliability and applicability.
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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 it