# A streaming feature-based compression method for data from instrumented infrastructure

## TL;DR

This paper presents a streaming compression method for sensor data from instrumented infrastructure, enabling efficient storage and analysis of pedestrian-event signals while preserving key features for structural health monitoring.

## Contribution

It introduces a novel streaming compression technique tailored for civil engineering sensor data that maintains critical event features, improving data handling efficiency.

## Key findings

- Effective compression with minimal loss of pedestrian-event features
- Demonstrated on real strain and acceleration data from a footbridge
- Trade-off analysis between compression ratio and accuracy

## Abstract

An increasing amount of civil engineering applications are utilising data acquired from infrastructure instrumented with sensing devices. This data has an important role in monitoring the response of these structures to excitation, and evaluating structural health. In this paper we seek to monitor pedestrian-events (such as a person walking) on a footbridge using strain and acceleration data. The rate of this data acquisition and the number of sensing devices make the storage and analysis of this data a computational challenge. We introduce a streaming method to compress the sensor data, whilst preserving key patterns and features (unique to different sensor types) corresponding to pedestrian-events. Numerical demonstrations of the methodology on data obtained from strain sensors and accelerometers on the pedestrian footbridge are provided to show the trade-off between compression and accuracy during and in-between periods of pedestrian-events.