From Alarms to Foresight: How AI Is Rewriting the Rules of Geotechnical Monitoring
Ali Siamaki
The daily ritual of a geotechnical monitoring engineer has evolved significantly in the last decade, but the core logic remains dangerously stuck in the past.
We no longer live in spreadsheets. Today, we log into sophisticated, cloud-based data visualization platforms. We are greeted by sleek dashboards displaying real-time feeds from Automated Motorized Total Stations (AMTS), wireless tiltmeters, and shape arrays. The data is parsed, visualized, and updated automatically.
Yet, despite this leap in presentation, our method of interpretation has hardly changed.
We scroll through dozens of graphs, checking if the latest reading has crossed a static horizontal line, the "Alert Level." If the data stays below the line, we move to the next graph. If it crosses the line, we trigger a response.
This "static threshold" approach, while digitally streamlined, suffers from a fatal flaw: it is inherently reactive. By the time a static alarm triggers on your dashboard, the deformation has already occurred.
Furthermore, while our platforms are great at displaying data, they are often terrible at contextualizing it. We are drowning in data, yet often starving for insight. The sheer volume of noise in these massive datasets can obscure the subtle signals that precede a failure.
It is time to move beyond simple visualization. Here is how Artificial Intelligence (AI) and Machine Learning (ML) are fundamentally shifting geotechnical monitoring from reactive alarms to predictive, risk-based insight.
The Fallacy of “The Red Line"
In traditional monitoring contracts, we establish Trigger, Action, and Alarm (TARP) thresholds based on absolute design limits. For example: "Trigger an email if lateral displacement exceeds 25mm."
Most modern platforms automate this check perfectly. The problem, however, is not the software; it is the physics. Soil, rock, and concrete are not static materials. They are dynamic; they "breathe" in response to their environment.
Thermal Expansion: A concrete retaining wall will naturally expand and tilt during a summer heatwave. A static alarm set in winter might trigger a "false positive" in July, purely due to thermal elongation.
Hydrological Response: A vibrating wire piezometer will show spikes in pore water pressure during heavy rainfall.
When a platform treats these natural, elastic fluctuations the same way it treats a slope failure, the result is Alarm Fatigue. When an engineer receives 50 "Critical Alerts" in a week, and 49 of them are due to rain or temperature, they inevitably begin to ignore the 50th alert. That 50th alert might be the one that matters.
Anomaly Detection: Automated Data Hygiene
Even with robust data management systems, "dirty data" remains a massive operational headache.
Most platforms use simple "Min/Max" filters or "Rate of Change" spikes to filter out errors. However, these crude filters often fail to distinguish between a sensor malfunction and a real, sudden movement.
This is where AI outperforms standard software rules. Machine Learning algorithms (such as Isolation Forests) can be trained on historical datasets to distinguish between the signature of mechanical movement and sensor noise.
Consider the common headache of AMTS (Robotic Total Stations) in urban environments. Temporary obstructions (a crane passing by) or atmospheric refraction can cause wild spikes. A standard platform might flag this as a massive movement because it exceeds the "Rate of Change" limit.
An AI model, however, looks at the context. It can evaluate the stability of reference prisms in real-time. If the reference prisms show a deviation pattern identical to the target prism, the algorithm recognizes this as an atmospheric or systematic error, not ground movement. It can automatically flag the data point as "Suspect" before it ever triggers a panic alarm on the engineer’s dashboard.
The Benefit: The engineer stops wasting time investigating false spikes and focuses entirely on verified trends.
Dynamic Thresholding: Multivariate Analysis
Once the data is clean, we can move from static red lines to Dynamic Thresholds. This is where we use "Supervised Learning" to teach a model how a structure behaves under normal conditions.
Imagine a model that ingests multiple variables simultaneously: rainfall, barometric pressure, reservoir level, and ambient temperature. The AI learns the correlation between these external loads and your sensor readings.
Scenario A: A storm dumps 50mm of rain. The piezometer rises 2kPa. The AI model predicts this rise based on the rainfall input and determines the reading is within the predicted envelope. -> Status: Green.
Scenario B: It is a dry, cool day. The piezometer rises 2kPa. The AI model predicts that the pressure should remain flat. The deviation between the prediction and the reality is high. -> Status: Red.
In a static system, both scenarios look identical. In an AI-driven system, the context dictates the alarm. This allows us to detect "slow creep" anomalies that are technically below the absolute design limit but represent a deviation from expected behavior.
Predictive Analytics: The “Inverse Velocity" of Failure
Perhaps the most critical advancement is the ability to forecast trends. In slope stability, we are often racing against time.
AI models are particularly adept at applying the Inverse Velocity Method (based on the Voight relation) at scale. As a slope begins to fail, the velocity of movement accelerates asymptotically. By analyzing the rate of change (velocity and acceleration) of displacement data, algorithms can project the curve forward to estimate a potential Time of Failure (ToF).
This is not limited to landslides. In TBM (Tunnel Boring Machine) tunneling, predictive models can ingest machine data (cutterhead torque, face pressure, grout volume) and geology data to predict surface settlement troughs ahead of the machine.
This shifts the engineering conversation from "What happened yesterday?" to "What is likely to happen next week if we continue at this rate?"
The Prerequisite: Data Architecture
A warning for firms looking to adopt these tools: AI cannot fix a broken data architecture.
You cannot run machine learning models on a folder full of disconnected Excel spreadsheets. To leverage these tools, companies must transition to centralized, structured SQL databases or cloud-based data warehouses. The data needs to be accessible via API, standardized in format (like the AGS or DIGGS standards), and timestamped accurately.
If your data is siloed and unstructured, an AI implementation will fail. Digital transformation is 80% data engineering and only 20% algorithm development.
The Human-in-the-Loop
There is a pervasive fear that AI aims to replace the geotechnical engineer. This view fundamentally misunderstands the technology.
AI is a statistical engine. It is excellent at finding correlations, but it has zero concept of causality. An AI might find a correlation between "trucks arriving" and "inclinometer movement," but it doesn't know why that matters. It doesn't know that the inclinometer casing was kicked by a worker, or that a fault line exists ten meters below the sensor.
AI does not replace the geotechnical engineer; it acts as a force multiplier.
It handles the drudgery of processing millions of data points, filtering noise, and spotting multivariate correlations that the human brain simply cannot process. It presents the engineer with a refined, high-probability set of insights. It is then up to the engineer to apply domain expertise, geology, structural mechanics, and site context to validate the insight and make the critical safety decision.
Conclusion
The transition from spreadsheets to AI is not about chasing a buzzword; it is a necessary evolution for safety and risk management. As our infrastructure projects become larger and our urban environments denser, the margin for error shrinks.
We are collecting more data than ever before. We owe it to our clients and the public to use that data not just to document failures after they happen, but to predict and prevent them.
