A New Quantitative Procedure to Determine the Location and Embedment Depth of a Void Using Surface Waves
Bibliographic record
Abstract
Abstract Detecting underground cavities beneath construction sites and urban areas is a crucial task for many engineering projects. Each year, subsidence and surface soil failure due to underground voids cause substantial damage around the world. Most of the seismic methods currently used for cavity detection can successfully locate a void but not its embedment depth. In spite of successful case histories, void detection is still a challenging problem because of the lack of a standard, quantitative void-detection technique. In addition, existing non-destructive techniques do not consider the effect of lateral inhomogeneities, i.e., cavities, in the wave propagation. Thus, the detection of underground cavities needs further study. This paper presents the results of numerical simulations of the multi-channel analysis of surface waves (MASW) in a laterally non-homogeneous medium. First, the Lamb solution is used to calibrate a homogeneous model, subsequently, voids with different dimensions and embedment depths are included in the medium. Analysis of the resulting surface responses shows that time and frequency domain parameters are sensitive to the location, embedment depth, and size of voids; which interact with the incoming wave front causing reflection of Rayleigh waves and strong attenuation of transmitted waves. The power-spectral-density functions clearly show patterns of attenuation and amplification. The authors propose a new analysis procedure to determine not only the location but also embedment depth of a void; this procedure is based on the attenuation analysis of Rayleigh waves (AARW). The new method uses the frequency spectra of recorded signals to compute a spectral-energy parameter and a modified logarithmic-decrement parameter. Numerical results of the AARW method applied to different conditions, including noisy signals, show that these parameters indicate successfully the location and embedment depth of underground voids.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".