Transforming Soil Laboratory Testing with AI Technology

Why soil and rock testing is evolving

Soil and rock testing is undergoing a profound transformation. Traditional soil laboratory testing methods, long relied upon to assess soil stability, nutrient composition, and rock mechanics, are evolving to meet the growing demand for faster, more accurate, and actionable data. Across infrastructure, energy, and environmental projects, engineers, planners, and researchers now require real-time insights to support critical decisions, from foundation design to sustainable land management.

Emerging technologies such as artificial intelligence (AI), automation, and real-time data analysis are at the forefront of this evolution. By combining advanced pattern recognition, predictive modelling, and automated laboratory workflows, these innovations allow for more precise characterisation of soils and rock formations than ever before. They not only accelerate testing timelines but also enhance reliability, enabling stakeholders to anticipate challenges, optimise design, and reduce project risks.

In this article, we explore why soil and rock testing is evolving, the drivers behind these innovations, and how AI, automation, and real-time data are shaping the future of geotechnical and environmental projects.

The drivers for innovation in geotechnical testing

Growing industry demand in the energy and infrastructure sectors is leading to advancements in Geo-data requirements. Towards the end of 2021, Fugro increased laboratory testing capability by 50% to meet this demand, with development in three key laboratories in Wallingford, Houston and Brussels.

Infrastructure expansion and resource development

Soil testing is used in infrastructure construction to assess soil stability, load-bearing capacity, and suitability for foundations. Laboratory tests can identify compressive strength of the soil, supporting resource and project management with accurate insight.

Increasing complexity of various rock types

The geological make-up of Earth is changing, with the depletion of accessible mineral deposits. With orebodies buried deep beneath the surface, we turn to remote sensing tools like lidar and seismic surveys to identify these elements.

Advanced laboratory data analysis highlights these challenges within the complex rock types and soil compositions by assessing:

• soil pH

• soluble salts

• organic matter

• nitrate

• phosphorus

• potassium

• lime

Need for rapid, reliable data in environmental projects

There is a growing focus on developing sustainable construction practices, including the use of eco-friendly materials and ground improvement technique. Advanced soil tests help monitor soil pH and identify potential environmental problems like soil degradation or pollution.

The need to access reliable soil health data fast is rising with the growing challenges presented by climate change. Governing bodies, civil engineers and urban planners require accurate data to inform decision making.

AI-powered soil and rock analysis

AI is reshaping how we characterise and model ground conditions. By combining real-time monitoring, advanced pattern recognition, and predictive analytics, AI enables us to process large volumes of geotechnical and geological data with unprecedented speed and accuracy.

Soil and rock mechanics analysis

Machine learning algorithms are applied to both laboratory and in-situ test datasets to detect patterns in the mechanical response of soils and rock masses. This capability allows us to predict key parameters such as triaxial compressive strength, shear strength, and permeability with high reliability. These predictions feed into foundation design, slope stability assessments, and excavation planning, reducing uncertainty and improving safety margins.

Rock fracture analysis

High-resolution survey methods such as lidar, ground-penetrating radar, and seismic imaging, can capture information on subsurface rock structures. AI-driven analysis of this data helps quantify fracture density, orientation, and connectivity, providing accurate insight into rock mass behaviour under various loading and environmental conditions.

Predictive modelling for Earth structures

AI-powered predictive modelling enables engineers to simulate the performance of both natural and engineered earth structures, from slopes and embankments to tunnels, bridges, and deep foundations. By incorporating environmental variables such as rainfall, temperature fluctuations, or seismic activity, these models forecast how structures will respond over time, supporting proactive design adjustments and maintenance planning.

Site characterisation

By integrating real-time sensor data with historical records, AI delivers rapid and comprehensive site characterisation. These insights inform construction methods, sequencing, and risk mitigation strategies, while also providing predictive forecasts for how site conditions may evolve during the project life cycle.

Slope stability and geological modelling

AI and machine learning combine historical and real-time geotechnical data, including slope monitoring, pore pressure, displacement, rainfall, and laboratory test results, to produce robust geological models and assess slope stability. By simulating multiple “what-if” scenarios, these tools estimate failure probabilities, stress-test slopes under varying conditions, and refine site models over time, enhancing early warning capabilities and supporting both design and long-term monitoring.

Laboratory automation and robotics

Advances in automation and robotics are transforming soil testing laboratories, streamlining everything from sample handling to instrument calibration. Previously, laboratory procedures required experts in field and in the laboratory - but that's all changing with advancements in automation and robotics.

By removing variability in repetitive tests and accelerating processing times, these technologies deliver consistent, high-quality data that engineers can trust for critical design and construction decisions.

Automated soil laboratory tests

In modern geotechnical laboratories, automation plays a key role in improving the quality and efficiency of soil testing. Automated systems handle sample preparation, from sieving and moisture conditioning to loading specimens into testing apparatus, with precision and repeatability.

Calibration routines for instruments are also carried out automatically, ensuring that every measurement is traceable and compliant with testing standards. This approach delivers consistent results across repetitive analyses such as soil pH, organic matter content, and mineral composition, where even minor procedural variations can affect data quality.

Robotics in the soil testing laboratory

Robotic platforms are increasingly being integrated into soil testing workflows, particularly for high-volume projects where hundreds of samples must be processed under tight timelines. These systems can pick, place, and prepare samples with minimal manual intervention, while conducting multiple tests in parallel.

By reducing the scope for human error and maintaining strict procedural control, robotics enable faster throughput and ensure that results are standardised across all specimens. This consistency is critical when comparing datasets over time or between sites, supporting more reliable geotechnical interpretations and engineering decisions.

Real-time data collection and integration

Advances in sensor technology, data transmission, and AI-driven analytics are transforming how geotechnical and geophysical data is acquired, processed, and applied. By deploying soil testing sensors directly on-site, engineers can collect continuous, high-resolution measurements in real-world conditions without the delays of traditional sampling and transport.

This live data stream, integrated seamlessly with laboratory systems, enables near-instant analysis using machine learning models. The result is faster, more reliable decision-making, improved safety through reduced field exposure, and a deeper understanding of ground behaviour over time.

On-site sample soil testing sensors

Modern soil testing sensors can be installed directly at investigation sites, embedded in boreholes, mounted on drilling equipment, or positioned at key surface locations. These instruments capture parameters such as moisture content, temperature, pH, electrical conductivity, and in some cases even in-situ shear strength or density.

Immediate analytics results

Once sensor data is received, it is processed by advanced analytics platforms, often powered by AI and machine learning algorithms trained on large geotechnical datasets. These models can rapidly detect patterns, anomalies, or correlations in the incoming data, providing insights that would take much longer to achieve with manual methods.

Variations in pore pressure, resistivity, or temperature can signal changing drainage or soil instability. Real-time analytics enables early mitigation, adaptive planning, and continuous refinement of test programmes and design parameters, replacing the traditional “sample first, analyse later” approach with near-instant visualisation for timely, informed decisions.

Continuous laboratory monitoring

Continuous monitoring combines the capabilities of on-site sensors with long-term data logging and automated analysis. Rather than treating soil testing as a one-off task at the beginning of a project, sensors can remain in place for weeks, months, or even years. This provides a dynamic record of how ground conditions evolve which is a valuable resource for both construction and operational phases.

Long-term datasets are particularly powerful when used to predict ground behaviour under varying conditions. Seasonal moisture variations, changing groundwater levels, or increased loads from nearby construction can all be tracked and modelled. With machine learning, we can correlate these changes to specific triggers, improving our ability to forecast potential issues such as settlement, slope movement, or reduced bearing capacity.

For infrastructure projects with long design lives, continuous monitoring also serves as an early-warning system. Any deviation from baseline conditions, whether caused by environmental factors or human activity, can be flagged for investigation, reducing the likelihood of costly failures.

By integrating real-time sensing, immediate analytics, and sustained monitoring into a unified workflow, geotechnical and geophysical teams can deliver a much more responsive and informed service. Projects benefit from faster decision-making, enhanced safety, and datasets that not only describe current conditions but also predict future performance.

Advanced data visualisation and interpretation

From Raw Data to Insight

Modern geotechnical and agronomic projects generate vast and complex datasets. Laboratory tests measuring soil pH, organic matter, compressive strength, and nutrient content, combined with in-field measurements of crop performance and environmental conditions, create a wealth of information that can be overwhelming to interpret. Advanced data visualisation tools transform this raw data into clear, interactive formats, enabling faster interpretation and more confident, insight-driven decision-making.

Integrating Geotechnical and Agronomic Datasets

These tools allow seamless integration of geotechnical and agronomic information, linking soil and rock mechanics with nutrient profiles, fertiliser response, and crop yield. By combining these perspectives, engineers and planners gain a holistic view of site conditions, allowing them to anticipate potential challenges and recommend solutions that balance structural stability with agricultural productivity.

Predictive Modelling and Scenario Analysis

Predictive models and visualisation platforms enable the simulation of “what-if” scenarios, illustrating how changes in soil and rock properties, nutrient levels, or environmental conditions could influence construction outcomes or crop growth over time. This capability supports proactive planning and helps teams make informed adjustments before issues arise.

Optimising Outcomes Through Visualisation

By presenting complex datasets in intuitive, actionable formats, visualisation tools empower engineers and agricultural advisors to optimise outcomes. Structural designs can be refined for safety and efficiency, while crop management strategies can be tailored to maximise yield and quality. These insights help reduce project risk and improve resource allocation across both engineering and agricultural projects.

Actionable Insights for Engineers and Advisors

Ultimately, advanced data visualisation turns multi-layered research into practical, actionable insights. Whether assessing foundation stability, evaluating slope performance, or enhancing crop productivity, these tools provide a unified view of the site that bridges the gap between detailed data and effective decision-making.

From laboratory to field – connecting stakeholders

Translating Soil Testing into Actionable Insights

Translating complex soil testing and geotechnical analyses into practical insights is essential for effective decision-making across projects.

Several industries rely on accurate laboratory data presented in formats that are easy to understand and act upon. Clear visualisations and concise reports from the laboratory help bridge the gap between research and field implementation, ensuring that findings from soil testing, including key parameters like compressive strength, directly inform project strategies.

Collaboration Through Open-Access Platforms

Cloud-based, open-access platforms have transformed soil testing collaboration by centralising laboratory results for real-time review and input. This reduces miscommunication, speeds project timelines, and ensures measurements, such as compressive strength, are effectively applied in field planning. By linking laboratory data to practical applications, stakeholders can optimise construction, assess site stability, and support agricultural productivity, improving outcomes and reducing risk.

Case examples – where emerging laboratory tech is delivering impact

With six advanced geotechnical testing laboratories globally, and a rising demand for geotechnical services, Fugro have been challenged with a growing number infrastructure and renewable energy projects that require advanced soil and rock testing services in recent years.

Laboratory testing services for SSE Thermal for vital carbon capture station

Between 2022 and 2023, Fugro were working with SSE Thermal to undertake a geotechnical ground investigation and engineering analysis to support the development of a carbon capture power station. Our experts were tasked with understanding the soil conditions to identify potential project risks.

With 60 CPTs completed, and over 1,000 laboratory soil and rock tests performed, Fugro were able to identify geohazards that allowed SSE Thermal to curate an insight driven approach to their new carbon capture facility.

Ultra-deep seabed cone penetration tests for offshore windfarm for ScottishPower Renewables

In 2020, Fugro were asked to undertake advanced geotechnical surveys to support the development of a renewable wind farm in the UK's North Sea for ScottishPower Renewables East Anglia Hub. The project consisted of 263 wind turbine generators, which will create enough to power up to 2.7 million homes.

Samples were collected from deep within the seabed using Fugro’s innovative SEACALF® MkV Deep Drive® CPT system. Soils were analysed in our Wallingford laboratory and supported with the seabed mobility assessment, foundation design and cable-route engineering. ScottishPower Renewables used insights to drive the project forward with an accurate risk assessment and insight-led decision making.

Looking ahead – the future of soil and rock testing

Self-learning rock and soil testing

Advances in AI and machine learning are paving the way for self-learning soil and rock testing systems. These platforms can adapt laboratory and field procedures in real time, adjusting sample handling, testing protocols, and data analysis based on incoming measurements. By continuously refining their approach, these systems improve accuracy, reduce human error, and ensure that soil testing and rock assessments consistently deliver reliable results.

Predictive modelling for optimised outcomes

Integrating soil testing and rock mechanics data with predictive modelling allows engineers and agricultural advisors to anticipate challenges before they arise. Models can simulate how soils and rock masses will behave under varying environmental conditions, informing construction decisions such as foundation design or slope stability, and agricultural interventions such as nutrient management or crop planning. By combining adaptive testing methods with predictive analytics, projects can achieve best results while reducing risk and optimising resource use.

A Smarter Future for Field and Laboratory Work

The convergence of real-time data, AI-driven testing, and predictive modelling promises a future where laboratory and field work are more efficient, insightful, and integrated than ever before. Engineers and planners will be able to make proactive, data-driven decisions that improve both infrastructure performance and agricultural productivity, while ensuring that every stage of soil testing contributes to safer, smarter, and more sustainable outcomes.

Conclusion

As the pace of technological change accelerates, embracing emerging innovations in laboratory testing and data management has never been more critical. From AI-driven predictive modelling to real-time field sensing, these tools empower engineers, planners, and decision-makers to act with greater speed, precision, and confidence.

Fugro stands at the forefront of this transformation, delivering advanced testing services that seamlessly connect the laboratory, the field, and those making key decisions. By integrating cutting-edge technology with deep expertise, Fugro enables clients to navigate complex projects with clarity and insight, turning data into actionable solutions.