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A Machine Learning-Driven Geophysical-Geotechnical Approach for Improved Engineering Site Assessment[v1]

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By Mbuotidem David Dick, Andy Anderson Bery, Adedibu Sunny Akingboye, Mfoniso Aka
| Preprints.org
Submitted: 10 February 2025 You are already at the latest version Subsurface geological formations are vital for validating deep engineering design assumptions, particularly in weathered terrains where unstable ground conditions pose risks. Geophysical investigations often face challenges due to inverse problem uncertainties and inadequate subsurface data.
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