1. SENSOR BASED HEALTH
MONITORING OF STRUCTURES
By,
M. Mayur,
Siddharth Institute of Engineering and Technology,
Puttur.
Abstract:
Structure is an element composing of many
components such as beams, columns, roofs,
slabs, foundations and basements. Without
beams and columns, no structure is able to
stand on ground. But these structures also
damage due to temperature conditions they
expose, mismanagement during construction
and lack of quality of control in
construction. The damage is defined as
changes to the material or geometric
properties of a structural system, including
changes to the boundary conditions and
system connectivity, which adversely affect
the system’s performance. The SHM process
involves the observation of a system over
time using periodically sampled dynamic
response measurements from an array of
sensors, the extraction of damage-sensitive
features from these measurements, and the
statistical analysis of these features to Introduction:
determine the current state of system health.
After extreme events, such as earthquakes or The process of implementing a damage
blast loading, SHM is used for rapid detection and characterization strategy for
condition screening and aims to provide, in engineering structures is referred to as
near real time, reliable information Structural Health Monitoring (SHM).
regarding the integrity of the structure.
Qualitative and non-continuous methods
have long been used to evaluate structures
for their capacity to serve their intended
purpose. Since the beginning of the 19th
century, railroad wheel-tappers have used
the sound of a hammer striking the train
wheel to evaluate if damage was present. In

2. rotating machinery, vibration monitoring has Operational Evaluation
been used for decades as a performance
evaluation technique. In the last ten to Operational evaluation attempts to answer
fifteen years, SHM technologies have four questions regarding the implementation
emerged creating an exciting new field of a damage identification capability:
within various branches of engineering.
Academic conferences and scientific  What are the life-safety and/or
journals have been established during this economic justification for
time that specifically focuses on SHM. performing the SHM?
These technologies are currently becoming  How is damage defined for the
increasingly common. system being investigated and, for
multiple damage possibilities, which
cases are of the most concern?
 What are the conditions, both
operational and environmental, under
which the system to be monitored
functions?
 What are the limitations on acquiring
data in the operational environment?
Data Acquisition, Normalization and
Paradigm approach in SHM: Cleansing
The paradigm approach of an SHM is The data acquisition portion of the SHM
mainly divided in to four parts namely: process involves selecting the excitation
methods, the sensor types, number and
 Operational Evaluation, locations, and the data
 Data Acquisition and Cleansing, acquisition/storage/transmittal hardware.
 Feature Extraction and Data Again, this process will be application
Compression, and specific. Economic considerations will play
 Statistical Model Development for a major role in making these decisions. The
Feature Discrimination. intervals at which data should be collected is
another consideration that must be
When one attempts to apply this addressed.
paradigm to data from real world
structures, it quickly becomes apparent Because data can be measured under varying
that the ability to cleanse, compress, conditions, the ability to normalize the data
normalize and fuse data to account for becomes very important to the damage
operational and environmental identification process. As it applies to SHM,
variability is a key implementation issue. data normalization is the process of
These processes can be implemented separating changes in sensor reading caused
through hardware or software and, in by damage from those caused by varying
general, some combination of these two operational and environmental conditions.
approaches will be used. One of the most common procedures is to

3. normalize the measured responses by the features identified from the undamaged and
measured inputs. When environmental or damaged system. The use of analytical tools
operational variability is an issue, the need such as experimentally-validated finite
can arise to normalize the data in some element models can be a great asset in this
temporal fashion to facilitate the comparison process. In many cases the analytical tools
of data measured at similar times of an are used to perform numerical experiments
environmental or operational cycle. Sources where the flaws are introduced through
of variability in the data acquisition process computer simulation. Damage accumulation
and with the system being monitored need to testing, during which significant structural
be identified and minimized to the extent components of the system under study are
possible. In general, not all sources of degraded by subjecting them to realistic
variability can be eliminated. Therefore, it is loading conditions, can also be used to
necessary to make the appropriate identify appropriate features. This process
measurements such that these sources can be may involve induced-damage testing,
statistically quantified. Variability can arise fatigue testing, corrosion growth, or
from changing environmental and test temperature cycling to accumulate certain
conditions, changes in the data reduction types of damage in an accelerated fashion.
process, and unit-to-unit inconsistencies. Insight into the appropriate features can be
gained from several types of analytical and
Feature Extraction and Data experimental studies as described above and
Compression is usually the result of information obtained
from some combination of these studies.
The area of the SHM process that receives
the most attention in the technical literature Statistical Model Development
is the identification of data features that
allows one to distinguish between the The portion of the SHM process that has
undamaged and damaged structure. Inherent received the least attention in the technical
in this feature selection process is the literature is the development of statistical
condensation of the data. The best features models for discrimination between features
for damage identification are, again, from the undamaged and damaged
application specific. structures. Statistical model development is
concerned with the implementation of the
One of the most common feature extraction algorithms that operate on the extracted
methods is based on correlating measured features to quantify the damage state of the
system response quantities, such a vibration structure. The algorithms used in statistical
amplitude or frequency, with the first-hand model development usually fall into three
observations of the degrading system. categories. When data are available from
Another method of developing features for both the undamaged and damaged structure,
damage identification is to apply engineered the statistical pattern recognition algorithms
flaws, similar to ones expected in actual fall into the general classification referred to
operating conditions, to systems and develop as supervised learning. Group classification
an initial understanding of the parameters and regression analysis are categories of
that are sensitive to the expected damage. supervised learning algorithms.
The flawed system can also be used to Unsupervised learning refers to algorithms
validate that the diagnostic measurements that are applied to data not containing
are sensitive enough to distinguish between examples from the damaged structure.

4. Outlier or novelty detection is the primary • Principle IV (a): Sensors cannot
class of algorithms applied in unsupervised measure damage. Feature extraction
learning applications. All of the algorithms through signal processing and
analyze statistical distributions of the statistical classification is necessary
measured or derived features to enhance the to convert sensor data into damage
damage identification process. information;
• Principle IV (b): Without intelligent
In total, feature extraction, the more sensitive
a measurement is to damage, the
Operation evaluation gives the conditions of more sensitive it is to changing
SHM, operational and environmental
conditions;
Data Acquisition gives the number and types • Principle V: The length- and time-
of sensors to be introduced in buildings, scales associated with damage
initiation and evolutions dictate the
Feature extraction gives the technical required properties of the SHM
literature to distinguish between damaged sensing system;
and non damaged items of buildings, • Principle VI: There is a trade-off
between the sensitivity to damage of
Statistical Model Development is used for an algorithm and its noise rejection
determining damaged and undamaged capability;
structures. • Principle VII: The size of damage
that can be detected from changes in
Principles of SHM: system dynamics is inversely
proportional to the frequency range
Based on the extensive literature that has of excitation.
developed on SHM over the last 20 years, it
can be argued that this field has matured to So far, we have known about SHM.
the point where several fundamental
Principles, or general principles, have Let us know about it in a deep
emerged. manner something about
Components of SHM.
• Principle I: All materials have
inherent laws or defects; Components of SHM:
• Principle II: The assessment of
damage requires a comparison  Structure
between two system states;  Sensors
• Principle III: Identifying the  Data acquisition systems
existence and location of damage  Data management
can be done in an unsupervised  Data transfer
learning mode, but identifying the  Data interpretation and diagnosis.
type of damage present and the
damage severity can generally only
be done in a supervised learning
mode; Data Interpretation and Diagnosis systems
consist of:

5. 1. System Identification, measured. Examples of this include
2. Structural model update, temperature, light intensity, gas pressure,
3. Structural condition assessment, fluid flow, and force.
4. Prediction of remaining service life.
Data management:
Sensors:
Data management comprises all the
Sensors are a device that measures a disciplines related to managing data as a
physical quantity and converts it in to a valuable resource. The official definition
signal that can be measured by an provided by DAMA International, the
instrument or by an observer. A sensor is a professional organization for those in the
device which receives and responds to a data management profession, is: "Data
signal. A good sensor obeys the following Resource Management is the development
rules: and execution of architectures, policies,
practices and procedures that properly
• Is sensitive to the measured property manage the full data lifecycle needs of an
• Is insensitive to any other property enterprise."
likely to be encountered in its
application Data transfer systems are used to transfer the
• Does not influence the measured data to systems which help in predicting the
property. failures of structures.
Data Acquisition Systems: Structure
Data acquisition is the process of sampling Conceptually, an accelerometer behaves as a
signals that measure real world physical damped mass on a spring. When the
conditions and converting the resulting accelerometer experiences acceleration, the
samples into digital numeric values that can mass is displaced to the point that the spring
be manipulated by a computer. is able to accelerate the mass at the same
rate as the casing. The displacement is then
This includes: measured to give the acceleration.
• Sensors that convert physical In commercial devices, piezoelectric,
parameters to electrical signals. piezoresistive and capacitive components
• Signal conditioning circuitry to are commonly used to convert the
convert sensor signals into a form mechanical motion into an electrical signal.
that can be converted to digital Piezoelectric accelerometers rely on
values. piezoceramics (e.g. lead zirconate titanate)
• Analog-to-digital converters, which or single crystals (e.g. quartz, tourmaline).
convert conditioned sensor signals to They are unmatched in terms of their upper
digital values. frequency range, low packaged weight and
high temperature range. Piezoresistive
accelerometers are preferred in high shock
applications. Capacitive accelerometers
Data acquisition begins with the physical typically use a silicon micro-machined
phenomenon or physical property to be sensing element. Their performance is

6. superior in the low frequency range and they of the die. By integrating two devices
can be operated in servo mode to achieve perpendicularly on a single die a two-axis
high stability and linearity. accelerometer can be made. By adding an
additional out-of-plane device three axes can
Modern accelerometers are often small be measured. Such a combination always
micro electro-mechanical systems (MEMS), has a much lower misalignment error than
and are indeed the simplest MEMS devices three discrete models combined after
possible, consisting of little more than a packaging.
cantilever beam with a proof mass (also
known as seismic mass). Damping results Micromechanical accelerometers are
from the residual gas sealed in the device. available in a wide variety of measuring
As long as the Q-factor is not too low, ranges, reaching up to thousands of g's. The
damping does not result in a lower designer must make a compromise between
sensitivity. sensitivity and the maximum acceleration
that can be measured.
Under the influence of external accelerations
the proof mass deflects from its neutral Building and structural monitoring
position. This deflection is measured in an
analog or digital manner. Most commonly, Accelerometers are used to measure the
the capacitance between a set of fixed beams motion and vibration of a structure that is
and a set of beams attached to the proof exposed to dynamic loads.[22] Dynamic loads
mass is measured. This method is simple, originate from a variety of sources
reliable, and inexpensive. Integrating including:
piezoresistors in the springs to detect spring
deformation, and thus deflection, is a good • Human activities - walking, running,
alternative, although a few more process dancing or skipping
steps are needed during the fabrication • Working machines - inside a
sequence. For very high sensitivities building or in the surrounding area
quantum tunneling is also used; this requires • Construction work - driving piles,
a dedicated process making it very demolition, drilling and excavating
expensive. Optical measurement has been • Moving loads on bridges
demonstrated on laboratory scale. • Vehicle collisions
• Impact loads - falling debris
Another, far less common, type of MEMS- • Concussion loads - internal and
based accelerometer contains a small heater external explosions
at the bottom of a very small dome, which • Collapse of structural elements
heats the air inside the dome to cause it to • Wind loads and wind gusts
rise. A thermocouple on the dome • Air blast pressure
determines where the heated air reaches the • Loss of support because of ground
dome and the deflection off the center is a failure
measure of the acceleration applied to the • Earthquakes and aftershocks
sensor.
Measuring and recording how a structure
Most micromechanical accelerometers responds to these inputs is critical for
operate in-plane, that is, they are designed to assessing the safety and viability of a
be sensitive only to a direction in the plane

7. structure. This type of monitoring is called information of the structural behavior of
Dynamic Monitoring. bridges obtained from the monitoring
system, maintenance costs could also be
WIRELESS MONITORING reduced, since inspection methods
TECHNIQUES BASED ON MEMS (addressed i.e. in the following chapter) can
be applied more efficiently. Only after
Existing monitoring systems use traditional certain changes in the structural behavior
wired sensor technologies and several other have been identified, an inspection (either
devices that are time consuming to install by means of non-destructive testing or visual
and relatively expensive (compared to the methods) is necessary and proper repair
value of the structure). They are using large could be done right after the occurrence of
number of sensors (i. e. more than ten) are the defect. This reduces the risk of further
expensive and will therefore be installed damage.
only on a few bridges. A wireless
monitoring system with MEMS (Micro- The analysis of measured data and the
Electro-Mechanical-Systems) sensors could knowledge of continuous changes of
reduce these costs significantly. MEMS are structural behavior will also improve the life
small integrated devices or systems that time prognosis of civil structures reducing
combine electrical and mechanical the overall maintenance costs of buildings
components that could be produced for less and transport networks. Data has to be
than 50 € each. The principle of such a continuously transmitted (e.g. using the
system is shown in the scheme given in Fig. internet) to the supervisor. Each sensor
1. device (mote), which is itself a complete,
small measurement and communication
system, has to be power and cost optimized.
Using multi-hop techniques, the data of the
sensor network has to be transmitted over
short distances of some 10 m to a base
station on site. There the data items are
collected and stored in a data base for
subsequent analysis. This data can then be
accessed by a remote user. If the central unit
detects a hazardous condition by analyzing
the data, it has to raise an alarm message.
The central unit also allows for wireless
Currently, a wireless sensor node with such administration, calibration and
a MEMS sensor could be fabricated at a reprogramming of the sensor nodes in order
price varying from 100 to about 400 € and to keep the whole system flexible. Each
future developments show the potential for mote is composed of one or more sensors, a
prices of only a few Euro. Monitoring data acquisition and processing unit, a
systems equipped with MEMS sensors and wireless transceiver and a battery as power
wireless communication can reduce the supply (Fig. 2, right) [3, 4]. The acquisition
costs to a small percentage of a conventional and processing unit usually is equipped with
monitoring system and therefore will a low power microcontroller offering an
increase its application not only in integrated analogue to digital converter
monitoring bridges. Due to the detailed (ADC) and sufficient data memory (RAM)

8. to store the measurements. This unit also
incorporates signal conditioning circuitry
interfacing the sensors to the ADC. In the
following sections, some components are
mentioned, but a more detailed description
is given elsewhere.
An example of Micro machined Silicon
sensor.
A typical example of hybrid sensor system
for wireless MEMS and DMS sensor data.
An example showing monitoring of dams.
A diagram showing sensors in structures.
An example showing sensors in beams.

9. It is a typical example showing electrical
generator and a sensor for health monitoring
A type of forest based sensor for trees.
of systems.
An example of sensor based health An example of dam’s health in China.
monitoring of structures.

10. A perfect Silicon Sensor for Structural
Health Monitoring.
Conclusion:
The inspection of building structures and
especially of bridges is mainly done visually
nowadays. Therefore, the condition of the
structure is examined from the surface and
the interpretation and assessment is based on
the level of experience of the engineers. An
approach to continuous structural health
monitoring techniques based on wireless
sensor networks were presented, which
provide data from the inside of a structure to
better understand its structural performance
and to predict its durability and remaining
life time. Using this technique, monitoring
of large structures in civil engineering
becomes very efficient. . Essential is that the
new system provides a more reliable impact
generation.