Abstract:
This paper presents the implementation of a digital twin for the vibro-replacement ground improvement
process, as part of a digital construction site. The goal is to implement an evidence-based quality
assurance for each produced element. The system uses a combination of planning data and real-time
machine data collected automatically from construction equipment. In addition to the quality assurance,
feedback is provided in a number of areas, e.g., condition monitoring of the equipment. The digital twin
builds upon the real-time sensor and machine data, fused with metadata, to implement a digital model
for the process. A hierarchical data evaluation, using rule-based KPI, permits viewing of the spatial
variation of data over the construction site. This permits the modelling of systematic variations in
subsurface properties across the site, enabling the detection of anomalous individual elements, the
diagnosis of their cause and the prediction of properties as construction proceeds over the site. All timeseries data and KPIs are represented by standard classes of objects, leading to a generic coding of the
data analysis; this minimizes the effort required to add additional sites and/or new KPIs. Examples are
presented for a number of sites, where the system was used to identify anomalous columns.