The SAIN technology uses acoustic sensors and machine learning to continuously monitor infrastructure assets like bridges, dams, and railways in a non-intrusive way. It analyzes the acoustic signatures of structures to detect changes that may indicate issues. This allows for remote, long-term monitoring without direct sensor attachment. The system provides real-time alerts, monthly health reports, and integrates with existing asset management tools. It aims to improve on current sporadic monitoring techniques by providing a comprehensive, low-maintenance monitoring solution for the many aging infrastructure assets worldwide.

1. SAIN Overview
(Sonic Analysis of INfrastructure)

2. Arnmore Limited
Dr Gary Bamford
Mr Mark Tovey
Georgia State University
Prof. Stuart Jefferies
The idea;
to use a solar analysis technique to probe the
internal structures of assets.
2
The Team - The Idea

3. Current monitoring techniques are limited
3
Dams
Road
Bridges
Tunnels Track
Rail
Bridges

4. Increasing Rail Bridge Risk Factors
4
Ageing
infrastructure
coming under
increased
pressure
Flood damage
Wind damage
Earthquake risk
(natural and
man made
‘fracking’)
Heavier traffic
Bridge
Collisions
Increased
speed of trains
Increasing
length of trains
Increased
frequency of
trains

5. The Challenge (bridges opportunity)
5
UK
• 35,000 rail bridges
• 3% year on year growth of
rail traffic
• 30% of bridges >100 years
old
• 74,000 road bridges
• 3,000 dams
USA
• 614,000 road bridges
• 1 in 11 road bridges rated
structurally deficient
• 40% of rail bridges >50 years
old and near end of design
life
• 100,000 rail bridges
• 91,000 Dams
Europe
• 300,000 rail bridges
• 300 bridges at risk of
failure in Italy 2019
Worldwide
• 10 major bridge failures in
2018 alone
Asia
• High frequency of seismic
activity

6. UK Bridge Numbers
6
Assets in the UK Numbers
Rail Bridges 35,204
Road Bridges 74,045
Other not defined -
Total 109,249

7. The Technology
Data gathering, analysis and transfer

8. Bridge Acoustic Signature
8
Monitored
Bridge
Data
Collection
Data
Analysis

9. Monitoring Assets
9
Analysis
• Machine
Learning
• Frequency
• Trend
Detection
• Remote device
• Acoustic detection
• Calibration
Transfer of data to client
• WiFi
• Mobile Network
• LoRaWAN (Low power Wide
Area Network)
• Real time data transfer

10. Detection
10
Small stand
alone
device
•No need to fix it
directly onto an
asset
Long term
life
expectancy
•Enables
continuous data
collection
•Less maintenance
Acoustic
sensors
•Whole-body
signature
•Network of
sensors

11. Data Analysis
11
Machine
Learning
•Improves accuracy of
health signature of asset
Frequency
analysis
•Actual data recorded by
device
•Transformed into a
format that can be
compared to baseline
asset health signature
Trend
Analysis
•Provides data on effects
of seasons/traffic etc
•Provides an asset health
lifecycle view from
healthy to a critical failure

12. Data Transfer to Clients
12
Asset Owners
• Identification of
maintenance needed
• Location of asset
• Integration into
existing tools
Train/Freight
Operating
Company
• Monitoring of
rollingstock
• Flagging of possible
delays
Real time
data transfer
• Monthly reporting
• Immediate flagging
after critical failure

13. Summary
13
SAIN Features Current Monitoring Sensors
Continuous Monitoring Limited time on critical bridges
Realtime active notifications Spot checks
Long term unmanned monitoring Physical checking
Acoustic sensor remote from asset Vibration sensor directly on asset
Machine learning to improve accuracy Complex analysis
Monthly reporting on asset health
Independent review of data (Univ. of Georgia)

14. The Proposition
14
 Represents an innovative business opportunity
 Able to generate revenue and return
 Reduce risk and increase safety of critical assets
 Simple to deploy
 Able to give immediate results
 Supports broader business opportunity
 An immediate need
 A real opportunity to penetrate the market