Seismic Response of Wind Turbine Foundations on Sabkha Soils Improved by Deep Soil Mixing
Bibliographic record
Abstract
Wind turbines provide renewable electricity which reduces carbon dioxide emissions and promotes sustainable development globally.Nevertheless, when built on Sabkha soil, their foundations face serious geotechnical challenges.Sabkha soil, with its low shear strength and high compressibility, is highly susceptible to seismic forces.It can cause foundations displacement or overturning.Ground improvement processes are needed to provide a solution to these problems.Among these, deep soil mixing (DSM) has shown capabilities to improve soil stiffness and shear strength thus it can be an option for wind turbine foundations stabilization against seismic forces.The aim of this study is to investigate the seismic response of raft foundations on DSM-treated Sabkha soils using 3D numerical analysis based on site-specific seismic accelerograms.The results indicate that DSM reduces horizontal displacement by over 95%, with maximum displacement decreasing from 0.02321 m to 1.00 10 m.Additionally, peak acceleration at the top of the wind turbine is reduced by 50%, from 0.16 g to 0.08 g.These findings confirm that DSM significantly enhances foundation stability, mitigating seismic risks and improving dynamic performance.The study provides practical recommendations for optimizing DSM applications, supporting the safe and sustainable development of wind energy infrastructure in Sabkha active regions.
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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 | 0.000 | 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 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".