AI in Structural Monitoring

What this topic covers

How AI can assist screening and prioritization without replacing structural engineering.

Core monitoring ideas

AI can help summarize large datasets and prioritize unusual combinations.

Outputs depend on training data, assumptions and sensor quality.

High-consequence decisions should retain traceable human engineering review.

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.