From Messy Data to Actionable Insights: Benchmarking Agentic AI Frameworks Across Real-World SHM Data Scenarios
The effective deployment of Artificial Intelligence (AI) in Structural Health Monitoring (SHM) is consistently hindered by the gap between idealized mathematical models and the field data, which is inherently heterogeneous, and asynchronous. While the industry has achieved high Technology Readiness Levels for isolated sensor hardware, it continues to suffer from low Integration Readiness Levels, forcing engineers into repetitive, manual data-wrangling tasks that preclude high-level diagnostic reasoning. This paper introduces a Reference Framework that proposes an architectural logic designed to navigate these integration challenges through Large Language Models (LLMs). We present a design hypothesis centered on a hybrid, multi-layered workflow that utilizes LLMs cognitive. As a result, this framework enables a cognitive layer to semantically interpret user intent and delegate complex, multi-step tasks to a library of deterministic, verified algorithms. To mitigate persistent integration risks and realize the potential of autonomous diagnostics, we propose that future SHM system designs should prioritize Pre-Processed Data Ingestion. By positioning the LLM as a bridge, this framework establishes a foundation for transitioning from manual data wrangling to conversational, actionable insights