# Exploring Scalable, Distributed Real-Time Anomaly Detection for Bridge Health Monitoring

arXiv: https://arxiv.org/abs/2203.02380 · HTML: https://mlsanity.com/paper/2203.02380/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 69 references that are in the map.
- First posted on arXiv 4 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [Deep Anomaly Detection with Deviation Networks](https://mlsanity.com/paper/1911.08623/) (2019)
- [History PCA: A New Algorithm for Streaming PCA](https://mlsanity.com/paper/1802.05447/) (2018)
- [Damage Detection in Bridge Structures: An Edge Computing Approach](https://mlsanity.com/paper/2008.06724/) (2020)
- [Structural Health Monitoring system with Narrowband IoT and MEMS sensors](https://mlsanity.com/paper/2104.13029/) (2021)

## Built on by (most cited first, 2 of 2)

- [Foundation Models for Structural Health Monitoring](https://mlsanity.com/paper/2404.02944/) (2024)
- [AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties](https://mlsanity.com/paper/2410.24178/) (2024)

## Abstract (arXiv)

Modern real-time Structural Health Monitoring systems can generate a considerable amount of information that must be processed and evaluated for detecting early anomalies and generating prompt warnings and alarms about the civil infrastructure conditions. The current cloud-based solutions cannot scale if the raw data has to be collected from thousands of buildings. This paper presents a full-stack deployment of an efficient and scalable anomaly detection pipeline for SHM systems which does not require sending raw data to the cloud but relies on edge computation. First, we benchmark three algorithmic approaches of anomaly detection, i.e., Principal Component Analysis (PCA), Fully-Connected AutoEncoder (FC-AE), and Convolutional AutoEncoder (C-AE). Then, we deploy them on an edge-sensor, the STM32L4, with limited computing capabilities. Our approach decreases network traffic by ≈8·105× , from 780KB/hour to less than 10 Bytes/hour for a single installation and minimize network and cloud resource utilization, enabling the scaling of the monitoring infrastructure. A real-life case study, a highway bridge in Italy, demonstrates that combining near-sensor computation of anomaly detection algorithms, smart pre-processing, and low-power wide-area network protocols (LPWAN) we can greatly reduce data communication and cloud computing costs, while anomaly detection accuracy is not adversely affected.