Machine Learning in Structural Monitoring

What this topic covers

Pattern recognition, classification and forecasting as decision-support tools.

Core monitoring ideas

ML can detect patterns across many sensor channels.

Training data often contains few examples of real structural damage.

Model drift, operational change and sensor replacement can reduce accuracy.

Interpretation limits

Sampling and storage should match the phenomenon: slow settlement and rapid vibration need different strategies.

Evidence and data quality

Analytics should surface uncertainty, missing data and sensor faults rather than hide them inside a score.

Lifecycle use

AI, machine learning and digital twins support review; they do not replace structural engineering accountability.