Digital Ground Modeling: Standardization and Full Control of Geotechnical Data

In infrastructure projects, the quality of technical decisions relies directly on the integrity, coherence, and traceability of ground data. However, geotechnical data management remains one of the most fragmented processes in engineering practice: multiple formats, dispersed sources, unstructured information, and limited reusability.

In this context, the Digital Ground Model (DGM) emerges not merely as a technological advance, but as a strategic need — a way to transform raw geotechnical data into a structured, traceable, and actionable digital asset that supports design, construction, and performance monitoring across the entire project lifecycle.

This article explores how the standardization and digitalization of geotechnical information — through tools like DAARWIN — enable uncertainty reduction, interdisciplinary collaboration, and stronger technical decision-making in complex ground environments.

The Challenge: Dispersed Data, Limited Decisions

Geotechnical engineering relies on diverse data sources: boreholes, laboratory and in-situ tests, historical reports, field logs, and monitoring during construction. These data are often stored in disconnected formats (PDFs, spreadsheets, CAD drawings, proprietary software), making them difficult to centralize, interpret, or reuse.

• This fragmentation introduces multiple risks.

• Loss of valuable information across project phases.

• Uncertainty in defining geotechnical units.

• Poor traceability from design models to original data.

• Overdesign strategies as a precaution against unknowns.

Such limitations not only reduce technical efficiency, but also affect risk management, cost optimization, and design reliability.

The Digital Ground Model: A New Technical Standard

A Digital Ground Model (DGM) is a structured digital representation of the subsurface that integrates all available data — public and private — into an interoperable, visual, and traceable format. Its goal is to provide a consistent and quantifiable understanding of ground conditions across the full project timeline.

Key components of a robust DGM include:

• Data centralization: unifying boreholes, test data, historical and public information into a single structured database.

• Standardization and classification: automatic identification of layers and grouping into geotechnical units.

• Parameter characterization: assignment of mechanical and constitutive properties derived from reliable test data.

• Advanced visualization: generation of cross-sections, 3D models, and graphical interfaces for interpretation and design.

• Interoperability: seamless export of models into numerical analysis software or BIM environments.

This approach not only improves technical workflows but also strengthens documentation traceability, audit readiness, and the consistency of project assumptions and outcomes.

Practical Implementation with DAARWIN

DAARWIN brings this methodology into practical application through its Ground Investigation Data Management module, enabling users to:

• Import and structure borehole logs, lab, and in-situ test data.

• Integrate public datasets and historical reports.

• Automatically classify layers and create geotechnical units.

• Calculate characteristic parameters for selected constitutive models.

• Generate visual cross-sections and fully navigable ground models.

These models are directly linked with other DAARWIN modules — such as sensitivity analysis, backanalysis, and monitoring — enabling a continuous digital flow between investigation, design, and construction control.

Technical and Operational Benefits

Adopting a digital ground modeling approach brings tangible benefits to engineering teams, designers, and decision-makers:

• Reduced geotechnical uncertainty in early-stage design.

• Data-driven decisions, not assumptions.

• Improved efficiency in data handling, modeling, and reporting.

• Long-term knowledge reuse across projects.

• Interoperability with numerical tools and collaborative platforms.

At the organizational level, it supports the creation of a corporate geotechnical data repository, turning every project into a cumulative knowledge asset.

In an industry where technical accuracy, budget constraints, and long-term sustainability are more critical than ever, the digitalization of ground data is not optional — it is essential.

The Digital Ground Model is a foundational tool for any organization that values traceability, risk reduction, and long-term geotechnical knowledge. Tools like DAARWIN offer a scalable, interoperable, and technically robust solution to implement this shift.

Engineering the ground of the future begins by understanding it — structurally, holistically, and digitally.