A Digital Twin for Deep Vibro Ground Improvement-Paper

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The paper presents a digital-twin framework for quality assurance in vibro-replacement ground improvement. It combines construction plans and site metadata with real-time data automatically collected from equipment, creating a digital record for every ground-improvement point or column.<br /><br />The process is divided into penetration and compaction phases using rule-based segmentation. The system calculates key performance indicators (KPIs) for each phase and the complete element, then links them to the column’s location. Site managers can review KPI distributions on spatial maps and heat maps, identify unusual points, and inspect the underlying machine time series. These views help reveal both individual anomalies and systematic changes in subsurface conditions across a site.<br /><br />The framework uses standardized data objects and HDF5 files, with a shared index for time-series data, metadata, and KPIs. This data-centric design supports consistent access across visualizations and makes it easier to add sites, analyses, and KPIs. It also reduces the manual effort required to review large numbers of element reports.<br /><br />A test-site case study identified an area where penetration took longer and the vibrator travelled farther than usual. Detailed records confirmed that these anomalies were associated with stiffer, denser soil. About 10% of the executed points required abnormal additional penetration time, and the system automatically flagged them for follow-up.<br /><br />The authors conclude that combining machine data, spatial context, and multiple KPIs enables more effective evidence-based quality control and can expose soil variations that conventional checks may miss. Future work will investigate automatic recognition of operator actions and machine-learning methods, including deep learning, to better distinguish operational effects from ground conditions and reduce false alarms.

Keywords

digital twin

quality assurance

vibro-replacement

ground improvement

key performance indicators

real-time data

spatial maps

heat maps

HDF5

soil variability