A review on the application of geophysical methods in civil engineering studies
Bibliographic record
Abstract
This paper reviewed the application of geophysical methods in civil engineering projects by way of subsurface characterization by examining more than 75 publications in peer–reviewed journals. The paper highlighted various geological conditions considered in engineering site characterization and the appropriate geophysical methods such as electrical resistivity tomography, seismic refraction tomography, self-potential, induced polarization, electromagnetic, multichannel analysis of surface waves and magnetic methods used in subsurface characterization. Case studies drawn from 26 publications were presented to show the successful application of geophysical methods in subsurface characterization in relation to civil engineering projects. The paper also highlighted the challenges of geophysical data in civil engineering projects involving ambiguities in data interpretation, complexity in data processing and high noise to signal ratio in culturally noisy environments. Resolutions in the limitations and challenges of geophysical methods in civil engineering characterization were also offered in the paper, chief among them is integrated use of geophysical methods which has gained traction in recent years. Further solutions are incorporating appropriate band pass filters in the design of geophysical equipment’s to enhance signal to noise ratio in culturally noisy environments. Future researches in the use of geophysical methods in subsurface characterization in relation to civil engineering projects should involve joint inversion and modelling of integrated geophysical methods to achieve optimum results for subsurface imaging. Future researches should also incorporate the integration of machine learning and deep learning techniques, which enhance automated interpretation, facilitate anomaly detection, and enable real-time geophysical monitoring in civil infrastructure applications.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".