Evaluating Subgrade Compaction for Different Soils Using Nondestructive Lightweight Deflectometer
Bibliographic record
Abstract
Insufficient compaction of the subgrade can result in nonuniform deformation, leading to severe subgrade distress. To address this issue and find a new method for rapid detection and evaluation of subgrade compaction, we used the lightweight deflectometer (LWD) to analyze the dynamic deformation modulus (Evd) of subgrades filled with four types of soils—silt (ML), well-graded gravel (GW), lean clay (CL), and poorly graded sand (SP)—in different regions of Gansu province, China. Concurrently, we measured the degree of compaction (Doc) using the sand replacement method (SRM) to establish its correlation with dynamic deformation modulus (Evd). A strong correlation between the degree of compaction and dynamic deformation modulus was established for soils ML, GW, CL, and SP, and suitable formulas were selected based on curve variations. The developed formulas enabled back-calculation of the dynamic deformation modulus requirements corresponding to different degrees of compaction ranging from 90 to 100, facilitating direct queries and quick field checks. Results demonstrated that the LWD, as a reliable rapid detection method, effectively controlled subgrade compaction quality in field construction. Moreover, it extended the testing area and increased measurement frequency, thus providing a practical means for quickly evaluating the qualification rate and uniformity of subgrade compaction.
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 | 0.001 | 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".