# Data Interpretation in Structural Health Monitoring: Toward a Universal Language

## TL;DR

This paper proposes a new way to interpret data in structural health monitoring by treating it like a language to improve consistency and reduce bias.

## Contribution

The paper introduces a linguistic framework for data interpretation to standardize SHM practices and reduce subjectivity.

## Key findings

- Subjective decisions in data processing introduce biases that affect diagnostic outcomes.
- Context-aware algorithms and integrated data sources can reduce these biases.
- A universal 'language of data' could improve consistency across disciplines.

## Abstract

Structural Health Monitoring (SHM) relies on the effective communication between sensors and diagnostic systems, yet data interpretation remains inconsistent and subjective. This paper introduces a novel perspective, viewing data as a form of language with its own syntax, semantics, and pragmatics. By adopting this linguistic framework, the study emphasizes the need for standardized “grammars” in data collection, processing, and analysis to reduce ambiguity and enhance diagnostic reliability. Using case studies from SHM, the paper illustrates how subjective decisions in variable selection, cluster labels, preprocessing, and modeling introduce biases that affect the outcomes. The findings highlight the potential of context-aware algorithms and integrated data sources to mitigate these biases. This conceptual approach has broader implications for data science, suggesting a universal “language of data” that fosters consistency and collaboration across disciplines. By recognizing the constructed nature of data, this work offers a path toward more accurate, efficient, and reliable structural diagnostics, advancing both SHM practices and data interpretation methodologies.