Structural health monitoring (SHM) involves implementing a strategy to detect and characterize damage in engineering structures. It uses sensors to measure structures, data acquisition systems to collect sensor readings, and data processing techniques like feature extraction and statistical modeling to determine whether damage is present. SHM is important as it improves structural safety and functionality by enabling timely warning of failures and more cost-effective maintenance through condition-based monitoring and assessment.

1. STRUCTURAL HEALTH
MONITORING
M. Mayur
S.I.E.T.K
Puttur.

2. Contents:
 Definition…
 What is SHM?
 Pattern of SHM
 Importance of SHM
 Components
 Conclusion.

3. Structure:

5. Types:

6. Damages Due to:
 mismanagement in construction,
 lack of quality in control,
 temperature conditions……
 Damages such as surface cracks, segregation,
settlements etc…

8. Damage:
 Changes in:
 geometric properties ,
 boundary conditions ,
 system connectivity…
which adversely affect the structure’s
performance.

9. What should we do?

10. In 19th
Century:
 rail road wheel tappers - used the sound of a
hammer when striked against the wheel of
train to detect the damage.
 In rotating machinery, vibration monitoring
is used as performance evaluation technique.

11.  Then, these techniques are utilized to detect
the damages in the structure, and then a new
field emerged namely Structural Health
Monitoring.

12. What is Structural Health
Monitoring?
 The process of implementing a damage
detection and characterization strategy for
engineering structures is referred as
Structural Health Monitoring.
(in short…)

14. Pattern of SHM:
 Operational Evaluation,
 Data Acquisition and Cleansing,
 Feature Extraction & Data Compression, and
 Statistical Model Development for Feature
Discrimination

15. Operational Evaluation:
 Under which operations, the structure
services and damage.
 Life safety and economic justification for
performing SHM.
 Limitations of acquiring data in SHM.

16. Data Acquisition:
 This parts deals with:
 number of sensors,
 types of sensors,
 selecting their excitation methods &
 data storage techniques.

17. Data Normalization:
 separating changes in sensor readings from
damage to those caused by varying
operational & environmental conditions.

18. Feature Extraction:
 Feature extraction gives the technical
literature to distinguish between damaged and
non damaged items of buildings.

19. Statistical Model
Development:
 Statistical Model Development is used for
determining damaged and undamaged
structures.

20. Importance of SHM:
 SHM improves - safety & functionality of
structures.
 Monitoring - develop innovative design
methodologies - timely warning of impending
failures.
 Structural condition monitoring and assessment
are required for timely and cost-effective
maintenance.

21.  Embedment of sensors during construction
and measurement of structural responses
during service will enable condition
assessment and remaining life estimation
easy and convenient
 Monitoring scheme helps to gather data on
the realistic performance of the structures,
which in turn will help to design better
structures for the future.

22. Saptha Suthras:
 All materials have inherent laws or defects
 The assessment of damage requires a comparison
between two system states
 Identifying the damage differs than the type and
vulnerability of the damage, which requires skill.

23.  Sensors cannot measure damage. Feature extraction
and statistical classification is required to convert
sensor data to damage information.
 Damage information depends upon the intelligence of
sensor’s feature extraction.

24.  There is a trade-off between the sensitivity to
damage of an algorithm and its noise
rejection capability.
 DS α 1/RE
(damage size) α (1/ frequency range of
excitation)

25. Components:
 Structure
 Sensors
 Data acquisition systems
 Data management
 Data transfer
 Data interpretation and diagnosis

26. Why SHM?

27. Sensors:
 Sensors measure the physical quantity of damage and
sends it to computer.
Good Sensor :
Is sensitive to the measured property
Is insensitive to any other property likely to be
encountered in its application
Does not influence the measured property.

28. DA systems:
 Data acquisition is the process of sampling signals
that converts the resulting samples into digital
numeric values.
 Sensors
 Signal conditioning circuitry
 Analog-to-digital converters

29.  Data management system manipulates the
management of data obtained from sensors.
 Data transfer systems are used to transfer the
data to systems which help in predicting the
failures of structures.

35. SHM monitoring for a dam in China:

36. Conclusion:
 There is always no particular
conclusion for any technology
related concept.
 But, this concept ends with
conclusion with that even structures
have life and we (civil engineers)
are here to protect it from various
diseases.

37. Thank
you...