Geophysical-machine learning tool for continuous subsurface geomaterials characterization
Peer-Reviewed Publication
Updates every hour. Last Updated: 23-Jan-2026 22:11 ET (24-Jan-2026 03:11 GMT/UTC)
Traditional geotechnical investigations provide data only at discrete borehole locations, leaving vast areas uncharacterized. This spatial gap often leads to unforeseen ground conditions during construction, causing costly delays, design modifications, and occasionally catastrophic failures. Now, a novel integrated geophysical-machine learning approach, using k-means clustering technique, by a team of researchers from Shibaura Institute of Technology provides continuous subsurface characterization, enabling evidence-based decision-making throughout project lifecycles.
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