Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Monitoring Building Settlements Using Space–Time Cubes and Geospatial AI

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月4日

查看全文
  • Luigi Barazzetti,
  • Mattia Previtali,
  • Fabio Roncoroni

摘要

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.