Machine Learning: A Turning Point in Geotechnical Engineering

Ground investigation is the backbone of every geotechnical, tunneling, or infrastructure project. Yet despite decades of data collection—borehole logs, CPTs, lab tests, and monitoring records—most of this information remains siloed or underutilized. Traditional workflows often rely on manual interpretation, fixed assumptions, and time-consuming model calibration

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Machine learning is changing that. It provides a powerful way to extract patterns from existing data, uncover hidden relationships, and improve ground models with greater speed and accuracy.

Why is it critical to apply machine learning to ground investigation?

Every ground model is a simplification. Engineers are often forced to prioritize certain parameters, rely on limited datasets, or revisit calibration in later stages when problems arise. These challenges create inefficiencies and introduce risk.

Machine learning enables a more objective, comprehensive approach. By analyzing the full range of available data, it helps:

• Prioritize parameters based on influence, not guesswork

• Detect patterns across datasets and sites

• Automate parts of the backanalysis process

• Reduce calibration cycles while improving model accuracy

For complex projects—where time, risk, and reliability are critical—these advantages are not optional; they’re essential.

Technical methodology

In platforms like DAARWIN, machine learning is applied to structured and unstructured geotechnical data, including borehole logs, lab results, and monitoring records. The system uses supervised learning to link input parameters with observed field behavior, revealing which variables most affect settlements, deformations, or pore pressures.

In parallel, thousands of numerical simulations are automatically generated and evaluated—testing combinations of parameters, refining them based on real-time data, and learning from the results. Outputs are displayed through intuitive dashboards, parameter influence charts, and comparative plots between predicted and observed behaviors.

This provides engineers with a clear roadmap of what matters most in their models—and why.

Key benefits for senior engineers

Machine learning in ground investigation delivers real value to engineering and design teams:

• Focus calibration where it has the most impact

• Shorten analysis time from weeks to hours

• Improve FEM model accuracy and reduce uncertainty

• Increase traceability of assumptions and decisions

• Avoid overdesign through smarter interpretation of data

By aligning model behavior with real-world ground performance, teams can optimize both safety and efficiency.

Practical implementation in DAARWIN

DAARWIN brings machine learning directly into geotechnical workflows, without the need for specialized coding or data science knowledge. The platform enables users to:

• Analyze borehole and lab data to detect trends and outliers

• Identify the most influential parameters for model calibration

• Run automated backanalysis using real monitoring data

• Visualize the impact of each variable on ground behavior

Whether calibrating a tunnel model or validating an excavation sequence, machine learning tools in DAARWIN help reduce uncertainty, improve transparency, and accelerate decision-making.

Conclusion

Machine learning is no longer just an academic tool or future technology—it’s already transforming how ground investigation is conducted. For engineers who want to move beyond static models and assumptions, it offers a smarter, faster, and more objective approach.

DAARWIN enables this transformation by integrating machine learning into daily project workflows—bringing clarity, speed, and confidence to geotechnical decision-making.

🔗 Want to see it in action?👉 Watch the video and explore the DAARWIN demo: https://www.saalg.com/ground-investigation-data-management