Early Warning System for Landslide Risk and SHM by Means of
Reinforced Optic Fiber in Lifetime Strain Analysis
Renato Zona, Martina De Cristofaro, Luca Esposito, Paolo Ferla, Simone Palladino,
Elena Totaro, Lucio Olivares and Vincenzo Minutolo
Università della Campania “L. Vanvitelli”, via Roma 29, Aversa(CE), Italy
Keywords: Early Warnings, Big Data, BODTA, Soil Movement, Sensors Network, Internet of Things, Strain,
Displacement, Structure Health Monitoring.
Abstract: Nowadays Sensors Networks (SN) are intensively used for environment monitoring and structural health
monitoring. Sensors Network can be greatly useful for data collection in hazard sites or sites of cultural
heritage. For the latter is meant structure with historical value as masonry ancient construction, while the first
one has to be intended as landslide risk zone. Collecting data in terms of strain and displacements is
particularly crucial when anticipating the risks of disasters. When integrated into the Internet of Things and a
Big Data database, the SN offers an innovative way to have a health state of the monitored site. The paper
describes a prototype of a land-sliding risk early warning system hosted that consists of an optical fiber sensor,
called S.T.R.A.I.N, that collects values of deformations in soils or structures in time continuous analysis. This
offers an online database readable in remote control from a server or a smartphone. The developed prototype
collects and displays strain values, soil movement and structure displacements.
1 INTRODUCTION
The basis of structural health monitoring (SHM) has
been laid by Housner et al. (Housner, et al., 1997),
where it is defined as the continuous measurement
and analysis of the main structural and environmental
parameters under operating conditions in order to
detect anomalous behavior of structures in the initial
phases and then as a prevention tool.
Sensors Network can be greatly useful for data
collection in several hazard sites: the field of Cultural
Heritage is probably one of the most emblematic of
the potentialities offered today by modern techniques
and methods of surveying and monitoring. In the past
few years, applications of Sensor Networks (SNs),
Wireless Sensor Networks (WSNs) and their modern
variant offered by the Internet of Things (IoT)
infrastructures have been developed mostly for
increasing users’ entertainment and engagement
experiences with cultural objects and sites (Marulli,
Pareschi, & Baldacci, The Internet of Speaking
Things and Its Applications to Cultural Heritage.,
2016), (Marulli & Vallifuoco, The imitation game to
cultural heritage: a human-like interaction driven
approach for supporting art recreation, 2016),
(Amato, Di Martino, Marulli, Mazzeo, & Moscato,
2017) and for behavioral modeling and monitoring of
users in cultural sites, as in museums and public
exhibitions (Chianese, Marulli, Piccialli, Benedusi,
& Jung, 2017), (Marulli & Vallifuoco, Internet of
things for driving human-like interactions: a case
study for cultural smart environment, 2017).
Anyway, SNs, WSNs, and IoT haven’t been
exploited yet at their effective potential to design and
build systematic methodologies for threats prevention
and vulnerabilities discovery actions, in the cultural
heritage domain.
By hazard site it is intended as landslide risk zone,
and cultural heritage means structure with historical
value as masonry ancient construction.
It is also possible to find in the literature a large
number of studies concerning the use of optical fibers
in the medical field, for example in colonoscopy
practices to increase patient comfort (Zhao, Soto,
Tang, & Thévenaz, 2016).
Distributed strain measurements by optical fiber
sensors have great advantages with respect to
measurements done by traditional gauges such as
resistive or mechanical ones and among others those
based on Brillouin optical time-domain analysis
(BOTDA) that are discussed here. (Minutolo,
Esposito, Ferla, Palladino, & Zona, 2019)
Zona, R., De Cristofaro, M., Esposito, L., Ferla, P., Palladino, S., Totaro, E., Olivares, L. and Minutolo, V.
Early Warning System for Landslide Risk and SHM by Means of Reinforced Optic Fiber in Lifetime Strain Analysis.
DOI: 10.5220/0009817205210525
In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security (IoTBDS 2020), pages 521-525
ISBN: 978-989-758-426-8
Copyright
c
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
521
As a matter of fact, life-long measurement is the
new challenge in Civil Engineering, especially with
respect to construction maintenance and
management. It is clear that life-long monitoring
requires that the sensors are positioned in situ and left
in place permanently; consequently, they need to be
protected from the injuries caused by time and their
environment (Fajkus, et al., 2017).
Optical fibers for telecommunications, which are
the sensing devices when BODTA is used, are very
robust and do not suffer time degradation, but the
fiber is rather fragile and requires some protection
against mechanical shocks. For the scope, it is
desirable that the fiber is protected by means of
coating. Different ways of coating are planned for
optical fiber, they are mainly based on combinations
of carbon fiber layers and glass fiber layers,
depending on the mechanical characteristics required
for the environment they are included. This has two
main advantages: at first, protection for optical fibers
and then as an instrumented reinforcement.
Barrias and Bao (Barrias, Casas, & Villalba,
2016), (Bao & Chen, 2011) describe the evolution of
the SHM with a review of the major experiments and
results carried out to date to demonstrate the
effectiveness of the use of optical fiber sensors.
In this paper, a novel type of sensor is presented.
The S.T.R.A.I.N. (that is the name chosen by authors)
sensor is a transducer made by optical fiber sensors
coated with fiber carbon in order to give stiffness to
the transducer. The transducer is instrumented with
connected technology that allows it to be always
remote connected with a server, and the data acquired
always available by PCs or even, smartphones,
removing the need of an operator to be present in the
main control center.
2 METHOD
BODTA sensors provide as output the value of the
strain measured along a spatial dimension that
depends on the used acquisition device. Civil
engineering applications do not need a high spatial
resolution, but the availability of measurements of
strain and displacements, and also temperature, is
very useful in order to interpret the structure’s health
state. In this section, a brief explanation of the method
used to read the fibers output in terms of
displacements is reported. The S.T.R.A.I.N. sensor
consists of an optical fiber coated with carbon fiber.
The carbon fiber has the function of protecting the
optical fiber, keeping it in a fixed position so that it is
possible both to obtain reliable measurements and to
reinforce the monitored structures.
In the following a brief explanation of how the
sensor acquires and converts data to be used for safety
evaluation. To validate the present results, it is
necessary the comparison between theoretical and
experimental results through the choice of a
constitutive relation as the explicit one of Mander et
al (1988) (Mander, Priestley, & Park, 1988)
introduced in the following steps.
The concrete compression stress, namely σ
c
, is a
function of the strain and the yield stress too.
Moreover, a measure of the yield concrete stress,
named σ
cy
, has been carried out testing a
15cmx15cmx15cm cubical specimen and resulting
about -13.75 MPa.
As previously introduced, the Mander equation is
adopted through a modified form as shown inside the
following equations:
𝜎
0 ,𝜀 0.0035
𝜎
𝜎

𝜀
𝜀

𝑛
𝑛1
𝜀
𝜀

,0.0035𝜀 0.002
𝜎

250000𝜎

𝜀
1000𝜎

𝜀 ,0.002𝜀0
(1)
In order to perform a more accurate investigation,
concrete crashes and cracks have been considered.
Moreover, the stress has been considered to vanish for
tensile strain under the value of -0.0035.
Instead, the steel has been considered an elastic,
perfectly plastic material with a Young’s modulus
𝐸
210𝐺𝑃𝑎. In the same way of the concrete part
of the structure, the steel stress yielding resulted to be:
𝜎
380𝑀𝑃𝑎
(2)
The stress-strain curve linked to the steel, has the
following equations (3):
𝜎
0
𝜎
𝜎

,𝜀 0.01
,0.01𝜀 0.002
𝜎
𝐸
𝜀 ,0.002𝜀 0.002
𝜎
𝜎

𝜎
0
,0.002 𝜀 0.01
,0.01 𝜀
(3)
Finally, the stress-strain curves for the concrete and
steel, are reported in Figure 1.
a
)
b)
Figure 1: Stress-strain laws for a) concrete on the
compressive range, b) steel.
AI4EIoTs 2020 - Special Session on Artificial Intelligence for Emerging IoT Systems: Open Challenges and Novel Perspectives
522
For the previous definitions, equations (1) and (3), the
moment-curvature was carried out using numerical
integration of the stress-strain curves through an
iterative procedure composed by the following steps:
Fix a curvature value.
Solve for the horizontal neutral axis and splitting
the cross section into the parts where the stress
resultants are opposite.
Integrate the stress moment along the beam
thickness referring to the neutral axis.
The so obtained moment is the one corresponding
to the prescribed curvature.
The results of the procedure, applied to the
introduced structures, allowed to report the moment-
curvature diagram drawn in Figure 2.
Figure 2: Bending moment versus curvature diagram.
The diagram in Figure fulfills with the one proposed
by Kwak and Kim (2009) (Kwak & Kim, 2010),
where
the three linear parts of the diagram represent
pure elastic, cracked concrete and steel yield phase of
the cross section respectively. The calculated cracks
and crushes moments of the concrete have been
carried out considering that the stress vanishes under
the ultimate strain.
𝜀0𝑎𝑛𝑑𝜀𝜀

(4)
As shown in the fig.7, the concrete limit corresponds
to the decreasing moment
1
.000065
m
c =
value linked to the curvature.
Moreover, a little range of residual moment can
be seen in figure 2 starting from the circle to the
ultimate value. This fact is linked to the steel bars that
never reach their failure point for the curvature here
considered.
Starting from known values of stress and strain, it
has been possible to derive a curvature-moment
diagram using a numerical procedure that consists of
the integration of the curvature-moment relationship
imposing the normal effort equal to zero and carrying
out the height of the neutral axis to be input.
A curvature range is defined as follows:
𝜒

10

/2
𝜒

10

/
2
(5)
Within this range a certain curvature value of the
cross section has been imposed:
𝜒

𝜒𝜒

(6)
which will be given by the equation:
𝜒𝜒

∙𝑡
1𝑡
∙𝜒

(7)
With
𝑡
𝑖
𝑖

(8)
For each bending moment value inside the interval (6)
the deformation trend is evaluated as follow:
𝜀𝑦𝜒𝑦𝑦
(9)
Starting from it, the stresses are obtained fulfilling the
equations (1) and (2) parameterized with respect to
the neutral axis depth. From the resultant stress and
moment values, the depth of the neutral axis has been
determined by searching the solution in a numerical
way of the equilibrium equation along the beam axis:
𝑁𝑦

𝜎𝜀𝑑𝜀0
(10)
By the end, the corresponding normal stress is
obtained for each value of the curvature.
Moreover,
for the hypotheses of the Euler-Bernoulli beam
theory, the normal stress must have a zero value for
each depth of the neutral axis, except for fluctuations
due to the numerical algorithm.
Through the numerical quadrature of the stresses,
the bending moment has been obtained:
𝑀𝜒
𝜎𝜀𝑦𝑦
𝑑𝜀
(11)
In Figure 2, has been reported the numerical values of
the bending moment as a function of the curvature of
the cross section.
The maximum moment calculated is:
𝑀
14.20𝑘𝑁𝑚
(12)
The theoretical value of the limit moment allows us
to calculate the limit load, as
𝑀
𝐹
𝑑⇔𝐹
𝑀
(13)
Early Warning System for Landslide Risk and SHM by Means of Reinforced Optic Fiber in Lifetime Strain Analysis
523
Figure 3: Strain Along fiber paths.
By using the initial quantity:
𝑑 0.73𝑚0.26𝑚 0.47𝑚
(14)
𝐹
14.20𝑘𝑁𝑚
0.47𝑚
30.21𝑘𝑁13
(15)
From equation (13) the yield force can be assumed
about 30 kN.
In the following Table 5, the fiber optic strains at
sections located at the mid-span of the beam, i.e. at a
distance z from the left-wise support given by z = 0.91
m (see Figure 4), are reported; In particular, in the
table, the beam curvature and bending moment,
calculated analytically from recorded data, are
reported as well. By assuming that the Bernoulli
hypothesis holds, the curvature is calculated by
𝜒
𝜀
𝜀
𝑐
(16)
here 𝑐 is the distance between the top and fourth
fiber bar path along the cross-section height, which
also corresponds to the distance between the two
rebar sets. The reported value 𝑐 20𝑚𝑚 is the
distance of the bars from the upper and the lower side
of the section,
h
is the distance between the lower bar
path and the upper side of the
beam, 𝜀
and 𝜀
are
the
strains of the upper and lower bar path respectively.
As previously described, in the present experiment, a
single fiber string with a length of about 18 meters
was introduced inside the beam. The fiber has been
fixed to the reinforcements in a phase preceding the
casting one, and it runs along the beam four times in
four different positions according to 4 paths (top path,
second path, third path, bottom path) as shown in
figure 3. This Figure represents a diagram showing
the value of the deformations measured by the fiber
along its entire length. On the abscissa the whole
length of the fiber is reported; in order to know the
value of the deformation measured in the specific
position of the beam, it is necessary to read it on the
single path which describes the value of the
deformation according to the position where the fiber
is installed.
3 BIG DATA AND EARLY
WA RN ING S
This section explains how the proposed system could
be useful in landslide risk and SHM in lifetime
analysis, in self-learning of parameters of early
warning.
A lifetime monitored structure or soil generates a
great number of information in terms of strain.
As it has been shown in this contribution, this
information can be converted in terms of curvature
and displacements. This last information is needed in
order to evaluate the safety of soils and structure in a
conventional way.
In the very first instance, an evaluation of this type
is useful on structures or soils that show evident signs
of subsidence; therefore, it is suitable for immediate
intervention. This is the case explained in the
previous section.
However, things change when one decides to
monitor a structure or a soil that is apparently or
actually in a healthy state for its entire lifetime.
In this case, the sensor presented in this paper is
able to furnish information about strain,
displacements, and curvature continuously in time
and also in remote control.
This means that it is possible to check the status
of the displacement at every moment. The status is
always available because the sensor can be connected
to the internet, in order to make data disposable on a
server, a notebook or a smartphone.
This allows the user to know the static state of a
structure at every moment. Moreover, the user, is
indispensable only in the warning phase, because the
system is able to detect anomalies autonomously. For
instance, the system can avoid false alarms due to
peaks in strain, displacements, and curvatures.
Such a monitoring system represents a network of
sensors that are both signal and medium.
With the knowledge of the theoretical models
described in the previous section, it is possible to
filter the information by creating an early warning
smart system that, as the data collected approaches a
critical situation, allows to activate alerts that prevent
environmental disasters, or less drastically, which
leads to extraordinary maintenance that has less
impact on large structures, soils, and culturally
valuable assets.
In addition, a type of model is acquired that is
independent of experimental evidence by providing
an approach based on a time history analysis. In fact,
the system automatically recognizes the risk values to
which the structure or instrumented soils are
approaching. In this way, no type of numerical or
AI4EIoTs 2020 - Special Session on Artificial Intelligence for Emerging IoT Systems: Open Challenges and Novel Perspectives
524
analytical analysis is necessary, but the intelligent
monitoring system foresees the risk.
4 CONCLUSIONS
This system allows reaching different aims
simultaneously.
The big data collection leads to collect a load
history of the structure and soil movements. By doing
so, one can forecast, not only regular load cycles but
also load peaks due to extraordinary loads that do not
lead the structure to failure, i.e. high loaded trucks
(for streets) or trains (for railways), cranes for
maintenance work, etc. In this condition, false alarms
can be bypassed easily, by only warning the user
when needed.
But even if there is no warning, this system allows
the user to monitor the optical fiber continuous data
by real time information provided by the system.
Thanks to a smartphone application, all this
information can be reached not only by the main
control center in a fixed time and place but pretty
much whenever and everywhere with the handy and
most used device nowadays.
ACKNOWLEDGMENTS
The authors deeply acknowledge the contribution of
the financial grant VALERE: “VAnviteLli pEr la
RicErca”.
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