Abstract This paper explores the application of space–time cubes (S–T cubes) and geospatial artificial intelligence (GeoAI) for monitoring vertical settlements in structures. Although S–T cubes are not commonly employed in this type of structural analysis, they enable the storage and visualization of multi-temporal monitoring data, offering an effective framework to represent differential settlement over time within a defined spatial domain, including at the scale of individual structural elements. The study further integrates GeoAI techniques for predictive analysis aimed at detecting discontinuities, leveraging the temporal datasets organized within S–T cubes. We employ an index that combines accuracy across multiple prediction steps with least-squares statistics of adjusted data. This provides users with a rapid diagnostic to verify the effectiveness of forecasts before deeper analysis. Results from three datasets—each representing a monitoring project with distinct characteristics—demonstrate that machine-learning-based forecasts remain reliable for at least three prediction steps ahead. This level of consistency is sufficient to support the detection of discontinuities in new monitoring campaigns, with a precision on the order of ± 0.2–0.3 mm.
Barazzetti et al. (Mon,) studied this question.
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