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

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 fiber’s 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

, is a

function of the strain and the yield stress too.

Moreover, a measure of the yield concrete stress,

named σ

, 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



𝑛

𝑛1

𝜀







,0.0035𝜀 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

,0.01𝜀  0.002

𝜎



𝐸



𝜀 ,0.002𝜀  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.

)

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0 𝑎𝑛𝑑 𝜀𝜀



(4)

As shown in the fig.7, the concrete limit corresponds

to the decreasing moment

.000065

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







ℎ/2

𝜒







ℎ

(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



(8)

For each bending moment value inside the interval (6)

the deformation trend is evaluated as follow:

𝜀𝑦𝜒⋅𝑦𝑦





(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𝑑𝜀0

(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⋅𝑦𝑦



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



𝑑

(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  0.73𝑚0.26𝑚  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



ℎ

𝑐

(16)

here ℎ 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,

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

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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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