Fiber Optic Traffic Sensor

Fiber Optic Traffic Sensor

ASEMINAR REPORT

ON

FIBER OPTIC TRAFFIC SENSOR

Abstract

Over the last few years, optical fiber sensors have seen an increased acceptance

as well as a widespread use for structural sensing and monitoring in civil engineering.

In this paper, review the operating principles, sensor types, benefits and applications

of optical fiber sensors. This monitoring accomplishes two main tasks: it assesses the

health of the structure and provides useful data for traffic monitoring/control

applications. It reviews the installation of traffic sensors on the roadway and outlines

the benefits of developing a fiber optic weigh-in-motion sensor. And reviews fiber

optical sensor health monitoring in various key civil structures, including buildings,

piles, bridges, pipelines, tunnels, and dams.

Transportation Division 1


INTRODUCTION Chapter 1

Information on the type, size, weight, speed, and density of traffic is required for the proper

design and maintenance of highways. Sophisticated sensing systems are used to collect this

data. These systems utilize sensors embedded in the pavement and computers with

specialized software for recording and sending the data to a centralized location and solar

panels to supply power to the systems. Highway engineers use this information to develop

traffic and pavement management schemes for all state and federal highways. The

management schemes are tied to federal budgets that require legislative approval.

Currently utilizes a combination of electromagnetic loops, piezoelectric sensors, and data

acquisition system to obtain classification and weigh-in-motion (WIM) data. The fiber

optic sensor deforms, the intensity of the light passing through the fiber decreases,



allowing roadside computer systems to use this intensity for vehicle classification or

weigh-in-motion. Laboratory testing of sensors was performed pneumatically to

simulate tire pressures, enabling comparable load, optical light intensity, and deflection

data to be obtained for bare and encapsulated sensors.

In general, a typical Stuctural Health Monitoring(SHM) system includes three major

components: a sensor system, a data processing system (including data acquisition,

transmission and storage), and a health evaluation system (including diagnostic

algorithms and information management). The sensors utilized in SHM are required to

monitor not only the structural status, for instance stress, displacement, acceleration

etc., but also influential environmental parameters, such as wind speed, temperature and

the quality of its foundation. Since a large number of sensors will be involved in a

health monitoring system, the acquisition, transmission and storage of a large quantity

of data for such continuous monitoring is a challenging task.

FIBER OPTIC TRAFFIC SENSORS Chapter 2

2.1 Optical fiber structure & characteristics

At the heart of this technology is the optical fiber itself. A hair-thin cylindrical filament

made of glass that is able to guide light through itself by confining it within regions

having different optical indices of refraction. A typical fiber structure is depicted in

Fig.1. The central portion where most the of the light travels is called the core.

Surrounding the core there is a region having a lower index of refraction, called the

cladding. From a simple point of view, light trapped inside the core travels along the

fiber by bouncing off the interfaces with the cladding, due to the effect of the total

internal reflection occurring at these boundaries. In reality though, the optical energy

propagates along the fiber in the form of waveguide modes that satisfy Maxwell’s

equations as well as the boundary conditions and the external perturbations present at

the fiber.



Figure 1. Schematic of an optical fiber

2.2 Introdution to fiber optic sensors

Optical fibers are also attractive for other applications such as in sensing, control and

instrumentation. In these areas, optical fibers have made a significant impact and are

being the subject of substantial research over the last few years. In general, for these

applications fibers are made more susceptible and sensitive to the same external

mechanisms against which fibers were made to be immune for their effective operation

in telecommunications. In its simplest form, an optical fiber sensor is composed of a

light source, optical fiber; sensing element and a detector (see Fig. 2). The principle of

operation of a fiber sensor is that the sensing elements modulates some parameter of the

3 optical system (intensity, wavelength, polarization, phase, etc.) which gives rise to a



change in the characteristics of the optical signal received at the detector

Figure 2. Basic elements of an optical fiber sensor

The fiber sensor can be either an intrinsic one if the modulation takes place directly in

the fiber or extrinsic, if the modulation is performed by some external transducer as

depicted in Fig. 3.

2.3 Optical fiber Bragg grating sensors



Fiber Bragg gratings (FBGs) have—over the last few years—been used extensively in

the telecommunication industry for dense wavelength division de-multiplexing,

dispersion compensation, laser stabilization, and erbium amplifier gain flattening, all at

1550 nm. In addition, FBGs have been studied for a wide variety of mechanical sensing

applications [2-7] including monitoring of civil structures (highways, bridges,

buildings, dams, etc.), smart manufacturing and non-destructive testing (composites,

laminates, etc.), remote sensing (oil wells, power cables, pipelines, space stations, etc.),

smart structures (airplane wings, ship hulls, buildings, sports equipment, etc.), as well

as traditional strain, pressure and temperature sensing. The main advantage of FBGs for

mechanical sensing is that these devices perform a direct transformation of the sensed

parameter to optical wavelength, independent of light levels, connector or fiber losses,

or other FBGs at different wavelengths. The advantages of FBGs over resistive foil

strain gauges include:

Totally passive (no resistive heating),

Small size (can be embedded or laminated),

Narrowband with wide wavelength operating range (can be highly

multiplexed),

Non-conductive (immune to electromagnetic interference),

Environmentally more stable (glass compared to copper), and

Low fiber loss at 1550 nm (for remote sensing).

Potential for very low cost due to device simplicity and high volume

telecommunication usage.

Operating principle

A fiber Bragg grating is wavelength-dependent filter/reflector formed by introducing a

periodic refractive index structure, with spacing on the order of a wavelength of light,

within the core of an optical fiber. Whenever a broad-spectrum light beam impinges on

the grating, will have a portion of its energy transmitted through, and another reflected

off as depicted in Fig. 4 The reflected light signal will be very narrow (few nm) and

will be centered at the Bragg wavelength which corresponds to twice the periodic unit



spacing. Any change in the modal index or grating pitch of the fiber caused by strain,

temperature of polarization changes, will result in a Bragg wavelength shift.

Figure 4. Transmission and reflection spectra of a fiber Bragg grating

Mechanical stresses can be measured using FBG by properly mounting them on or

embedding into the substrate of interest. One of the advantages of this technique is the

fact that the detected signal is spectrally encoded, so that transmission losses in the

fiber are of no concern.

A fiber Bragg grating with a refractive index profile:

n(r) = no + n1cos (K .r )………………………… (1)

Where no is the average index, n1 is the amplitude of the grating (typically 10-5 to 10-2)

and r is the distance along the fiber, allows light with wavevector ki to be scattered in a

direction given by the diffracted wavevector kd = ki- K. Here K=2p/λ is the grating

vector, its direction is normal to the grating planes. λ is the grating period. If the

diffracted wavevector matches that of the free wave at the incident frequency, strong

Bragg diffraction into the kd direction occurs. The value of λ needed to reflect light

guided in a single-mode fiber core is given by the first order Bragg condition:

λ = λ b /2nm………………………….…………… (2)

2.4Types of fiber optic sensors



• Interferometric

– Measure optical phase difference between two lightwaves (Sagnac, Micahelson,

Mach Zehnder)

• Intensity

– Alteration of the guided light power

• Resonant

– Measure optical resonant frequency of an optical cavity (Fabry-Perot)

• Polarimetric

– Measure state of polarization of guided lightwave

• Spectral Interference

– Measure frequency of lightwave interfering with a periodic structure (fiber Bragg

grating)

Fiber Optic Traffic Sensors in flexible pavement Chaper3

The sensors have performed well in the pavement when placed in six-foot-

long narrow vertical grooves. The objective of this chapter is to determine how the

sensor functions in this vertical configuration. And as the fiber optic sensor deforms,

the intensity of the light passing through the fiber decreases, allowing roadside

computer systems to use this intensity for vehicle classification or weigh-in-motion.

Laboratory testing of sensors was performed pneumatically to simulate tire pressures,

enabling comparable load, optical light intensity, and deflection data to be obtained for

bare and encapsulated sensors.

Traffic data from the sites, including axle counts, vehicle classes, and axle loads, are

transmitted. The sensor’s flexibility, corrosion resistance, and immunity to

electromagnetic interference alleviates several of the problems causing sensor failure of

the piezoelectric sensors.



The Florida Department of Transportation(FDOT) was conducted a survey that ,50 six-

foot-long FOTSs were installed at four locations in both flexible and rigid pavements in

Brevard County, Florida. Thirty sensors were installed vertically and the other 20

installed horizontally. The sensors in these field sites were evaluated periodically over a

24-month time frame. The majority of the sensors functioned properly either as on-off

switches in a classification system, or for evaluation by the researchers for signal

quality and repeatability. The few sensors that failed (four failures were noted during

the 24 months) were installed horizontally. Also, signals from the horizontal sensors

were less repeatable than signals from vertically installed sensors. Very small surface

irregularities along the six-foot grooves for the horizontal sensors caused large

variations in sensor performance. If small depressions existed, the tires would bridge

over them, and if small ridges existed, the tires would impart stress concentrations. This

problem did not occur with the vertically installed sensors, because of the narrow width

of the groove into which the sensors were placed. Also, the sensors installed vertically

displayed longer lives than those installed horizontally. However, the mechanism that

enabled the longer lasting sensors in vertical grooves to perform was not clearly

understood. Therefore, analytical modeling was used, with model results correlated to

lab and field data, to develop an understanding of this phenomenon.

3.1 Sensor Overview

The FOTS is a flexible sensor with input and output leads that produce crisp, clean,

uninterrupted light signals at a measurable intensity to and from the sensor. It is sealed

from the environment and can be constructed in any desirable length. Sensors placed in

pavements are typically six-feet long. The sensor cross section is approximately 1.9

mm thick by 7.65 mm wide. The sensors are installed in the field into saw-cut grooves

by grouting them flush with the pavement surface, preventing water intrusion and

sensor damage. Fiber optic leads from the sensor are connected to an interface

electronics device that converts light intensity to voltage. As vehicles pass over the

sensors, loads from the vehicle tires deform the sensors and cause the light intensity to

decrease. A roadside computer system can use the change in light intensity with

specially developed algorithms to classify and weigh the vehicles. Sensors placed in

flexible pavements can be installed either horizontally in 12.5-mm-wide by 25-mm-



deep grooves or vertically in narrow 3-mm-wide, 25-mm-deep grooves. A cable from

the interface electronics device connects to the classification or WIM computer port

provided by the manufacturer for input from the piezoelectric sensor. The connection

allows the FOTS to be connected just as the piezoelectric sensor would be to the

computer. This equipment is stored in a roadside cabinet.

3.2 Fiber Optic Microbend Sensors

The FOTS was developed using a concept known as the fiber optic microbend theory.

Although there are many types of fiber optic sensors used for measuring pressures or

strains, the most economical ones are those based on the microbend principals. If

properly designed, these sensors can be constructed with relatively inexpensive

components. The light source, for example, can often be a light emitting diode (LED),

while the fiber needs little or no preparation before placement in the sensing region.

Other types of fiber optic sensors require costly lasers as the light source and very

sophisticated electrical equipment for interpreting the signal. A typical optical fiber

cable, containing two mediums through which light can pass, the core and cladding, in

addition to the acrylic buffer that protects these materials. The light intensity in a

microbend sensor decreases when an external force or pressure mechanically deforms

the core and cladding as it is pressed against a relatively small screen or mesh, as

shown in Fig.5(b). As the fiber bends over the small radii mesh strands, light focused

into the fiber’s inner core is refracted out of the core into the fiber’s protective buffer

layers, causing the decrease in light intensity .The change in light intensity is monitored

using data acquisition systems to yield percentage variations from the original intensity.

Microbends can be applied to optical fibers either from both sides, which is termed

double sided, or from one side of the fiber, termed single sided. The FOTS is a single-

sided microbend sensor consisting of 50/125/250 multimode fiber placed against a

0.254-mm-diameter mesh with 20 openings per 25.4 mm. The numbers identifying the

fiber denote a 50 mm inner diameter glass core, a 125 mm cladding diameter, and a 250

mm buffer diameter.

To date there has been minimal analytical work published concerning the sensor

pavement interaction during vehicular loading. This complex problem requires an

understanding of the stresses and deflections in and around the sensor. Although the



FOTS gives signals for all types of vehicles, the relationship between the field response

and the lab calibration curve was not understood, making field evaluation very difficult.

Fig.5. (a) Typical optical fiber; (b) light intensity losses due to deformation from

single-sided microbend sensor

3.3 Model Approach Of FOTs

A series of laboratory and field tests were conducted to provide data for finite-element

modeling of the FOTS. The lab tests included sensor characterization under

approximated field loading conditions. The field tests comprised falling weight

deflectometer (FWD) tests on two of the 20 sensors installed in one of the flexible

pavement sites to quantify their response to a range of impulse loads.

Both the pavement and the fiber optic sensor display nonlinear load-deformation

characteristics that vary with load or stress level. However, to develop a modeling tool,

the loaddeformation properties of this system were simplified and ‘‘assumed’’ to be

linear. The modeling was performed using MacNeal-Schwendler’s NASTAN for

Windows, which limited the mesh development to a maximum of 2,000 nodes.

Three(finite-element models FEMs) were developed and calibrated by comparing

model output results to experimentally determined data. The two FOTSs were installed

in the flexible pavement access road at the APAC-Florida MacAsphalt plant,



Melbourne, Florida. The first model simulated the stress-strain response in the surface,

base, and subgrade layers of this pavement. The model calculated deflections were

compared to FWD true deflection data to determine the estimating power of the model.

The second model was used to describe the stress-strain behavior of the FOTS when it

was encapsulated in a bonding agent. Model calibration was accomplished by varying

the elastic properties of the FOTS and the encapsulation material that were input to the

model until the deflections calculated by the model were approximately the same as the

deflections experimentally measured when the encapsulated sensors were tested

pneumatically in the laboratory under 310 kPa impulse pressures.

The third model was developed to simulate field conditions when the FOTS was

installed in a flexible pavement at the APACFlorida MacAsphalt plant. Using the

elastic properties determined from calibrating Models 1 and 2, the predicted sensor

deformations and associated light intensity losses were correlated with light losses

measured in the field. This third model enabled the stress distributions surrounding

embedded traffic sensors to be evaluated. This model was designed to simulate a sensor

centered in a narrow vertical groove, 3 mm wide by 25 mm deep, and encapsulated in a

material having elastic properties that were determined by calibrating Model 2. The

input elastic properties from Model1 were used for the surface, base, and subgrade

layers of the pavement.



Chapter 4

Traffic Monitoring Using Fiber Optic Grating Sensors

4.1Problem statement

Information on the type, size, weight, speed, and density of traffic is required for the proper

design and maintenance of highways. Sophisticated sensing systems are used to collect this

data. These systems utilize sensors embedded in the pavement and computers with

specialized software for recording and sending the data to a centralized location and solar

panels to supply power to the systems. Highway engineers use this information to develop

traffic and pavement management schemes for all state and federal highways. The

management schemes are tied to federal budgets that require legislative approval.

Traffic monitoring may be divided into three different categories: traffic light detection,

traffic counting, and traffic classification. The U.S. Department of Transportation defines

traffic classification as “the observation of highway vehicles and the subsequent sorting of

the resulting data into a fixed set of categories” (USDOT Traffic Monitoring Guide, 1992)

(i.e., vehicle type, speed, headway, gap, length, and weight).



The Florida Department of Transportation (FDOT) currently utilizes a combination of

electromagnetic loops, piezoelectric sensors, and data acquisition system to obtain

classification and weigh-in-motion (WIM) data. Reports from field personnel indicate that

the piezoelectric sensors corrode, are destroyed by sharp objects being dragged across

them, and are affected by power surges and vehicles in adjacent lanes. These factors,

combined with high truck tire pressures and extreme temperature and moisture variations,

severely impact the sensor’s working environment.

FDOT currently manages approximately 350 traffic monitoring sites. Traffic data, which

includes volume counts, vehicle classes, and axle weight, are telemetered by modem to

FDOT’s Traffic Statistics coordination office in Tallahassee. These sites include thousands

of sensors, which need to be maintained and replaced when failure occurs. Extremely high

costs associated with sensor repair have prompted an interest within FDOT to find new

techniques in traffic monitoring. To replace piezoelectric sensors, low-cost fiber optic,

microbend sensor technologies have been explored.

4.2Traffic classification

Efforts are under way to harness optical fiber for use as an accessory to vehicle and

roadway monitoring. The use of fiber Bragg grating sensors for traffic studie began by

Blue Road Research (BRR) as an effort with the Oregon Department of Transportation

(Oregon DOT) to monitor composite bridge strengthening efforts with fiber optic

sensors. The fiber grating sensors were able to confirm an increase in the weight

capacity of the bridge after the laminated overwrap was installed1. Yet they also

yielded some unexpected information about traffic characteristics2: Between formal

captures of data using a truck as a loading mechanism, traffic was allowed to pass over

the bridge. Figure.6 shows the Horsetail Falls bridge and traffic information collected.



Figure 6. (left) A truck is used as a loading mechanism to determine deflection on the

Horsetail Falls bridge; (right) Data collected from fiber optic sensors identifies various

types of traffic crossing the bridge.

Observing the graph shown in Figure6, from left to right, a minivan, an SUV, a small

car, and then pedestrian traffic is observed. Note that the amplitude and width of the

peaks correspond to expectations of weight and speed measurements as the traffic

approached the sensor embedded under the center beam of the bridge. Because the

bridge serves as a loading mechanism (e.g. weigh scale), individual vehicles crossing

the bridge can be classified by weight. However, the complexity of identification and

classification of vehicle types and weights intensifies as multiple vehicles enter the

loading scale, or when a suspended loading scale is not available, as is the case in a

common roadway.

Some experience with the test pad installation and sensor functionality relating to low-

speed traffic, two sensor types were installed into a busy traffic area on the Interstate 84

freeway outside Portland. A fiber optic backbone line was placed to facilitate readout

and testing from the Blue Road facilities about a third of a mile away.

Although the four initial sensors were damaged before the remainder of the system

came online, two sensors were still highly responsive, providing reflective spectral data

that could be converted to time and amplitude information used for traffic

classification. Because an unknown event--likely overstrain--had already detached the

fibers inside their packaged housings, it was determined that the fibers could possibly



last in their current condition for a period of up to decades. Also in their detached

states, the sensitivity of the fiber traffic sensors compared well with conventional traffic

sensors, but the heavy vehicle strains on the roadway caused a baseline-shift, due to the

physical relocation of the grating, as shown in the graph of Figure7.

Figure 7. Data recorded by detached fiber optic sensors.

There are many concepts for using fiber optic sensors to monitor

structures and provide traffic monitoring.

4.3. Weigh in Motion (WIM)

The first WIM concept leverages off of a “smart” bridge bearing with embedded

multiaxis fiber grating strain sensors (Figure ). In order to weigh a vehicle in motion, a

composite load cell, similar to that used in the bridge bearing, is embedded into the

roadway (Figure 9.) Embedded in to this load cell are one or more multiaxis fiber

grating strain sensors capable of detecting transverse strain. As the vehicle rolls over

the panel, the sensor(s) measure the strain, which can be related to a quantitative

weight for each of the vehicle’s axles and summed up for a total weight reading.



A second WIM concept is shown in Fig.10. In this concept, a beam is placed into a

roadway. This beam will flex under the weight of a vehicle and exert an axial strain on

a fiber grating sensors attached to the bottom of the beam.



For wait and speed measurements, a relatively fast demodulation system is required to

capture these events. One such device is a faster fiber grating demodulation box

(Fig.11). While majority of WIM applications will probably not require demodulation

speeds above 300Hz,this particular demodulator can run up to 10Hz for fast event

application.



4.4 Advantages:

An advantages of the fiber grating WIM now proposed is that it has the unique

advantage of being embedded up to three inches below the surface, providing

highly environment that serves to lengthen its life span , potentially through

multiple pavement resurfacing .

A fiber optic WIM sensor is anticipated to be competitive in cost but simpler to

install and maintain, with a life time comparable to traditional WIM sensors.

The fiber optic WIM sensor monitoring in remote areas does not have any

electrical requirements and thus becomes easier to do.

Fiber Optic Sensors for structural health monitoring Chapter 5

Conception of such FOSs originated from fiber optic communications. Optical

fiber experiences geometrical (size and shape) and optical (refractive index and mode

conversion) changes due to various environmental perturbations while conveying light

from one place to another. These phenomena perplexed efforts to minimize such

adverse influences so that signal transmission is smooth and reliable. However, it is

found that such optical changes can be employed to measure the external environment

parameters. Optic fiber thus found its niche in sensor applications. Investigations

showed that the sensitive perturbations in temperature, strain, rotation, electric and

magnetic currents, etc., can be converted or encoded into corresponding changes, such

as amplitude (intensity), phase, frequency, wavelength and polarization in the optical

properties of the transmitted light.



Table1 lists the FOSs available to civil engineering applications and their categories.

One method of classifying FOSs is based on its light characteristics(intensity,

wavelength, phase, or polarization)that is affected by the parameter to be sensed.

Another method classifies an FOS by whether the light in the sensing segment is

modified inside or outside the fiber (intrinsic or extrinsic). FOSs can also be classified

as local (or point), quasi-distributed and distributed sensors depending on the sensing

range. This method of classification is adopted here to organize the rest of this section.

Table 1. Fiber optic sensors for civil structural health monitoring

5.1 Recent progress of FOS health monitoring in civil engineering

FOSs offer great potential for SHM applications. Their significance to health

monitoring applications stems from the following facts: (1) Long life cycle. They are

made from a very durable material (i.e. silica) that is corrosion resistant and withstands

high tensile loading (up to 5% elongation, i.e. 50,000 μ ); (2) High temperature

endurance. They can measure temperature from −200 to 800 °C with a silica core and

1500 °C with a sapphire core. The measuring resolution can be better than 0.1 °C; (3)

Flexibility. They can be applied to complex surfaces and difficult-to-reach areas (i.e.

around the circumference of a round object, along sharp corners or across welds),

capable of both local and distributive measurements (ranging from 1 mm to tens of

kilometers); (4) Immunity to EMI. They can operate in electrically noisy environments

and can transfer sensing data over a long distance without EMI contamination; (5)


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Electrical isolation. They are non-conductive and suitable for embedment with

minimum impact to the host structure; (6) Quasi-distributed or distributed sensing

capacity. They can perform in-situ sensing at multiple locations required by health

monitoring of large civil structures and can be easily multiplexed by time or

wavelength methods; (7) Economy. They are already cost-competitive against

conventional sensors and their prices will still decrease with the rapid development of

fiber optic communication industry and wide exploration of FOSs.

Integration of FOSs with civil infrastructure for SHM is an active research field. While

the benefits of long term structural monitoring are yet to be fully realized, several

applications have been demonstrated to date. In these applications, optical time domain

reflectometry, Bragg gratings, Fabry–Perot and the LGFOSs have effectively

complemented or even replaced some sensors. The rest of this section will review these

demonstrations.

5.1.1 Buildings

FOSs have been successfully applied to buildings for strain and temperature

measurement. Displacement resolution of 0.1 μm and temperature resolution of 0.1 °C

were achieved with the technique of fiber Fabry Perot tunable filter demodulation

system. The eight-month period measurement showed that the temperature was

consistent with seasonal variation and the displacements were not substantial. Along

with the high resolution of FOSs, the advantage of embeddability is often exploited in

health monitoring. Recently, in Japan, 64 FBG sensors were embedded in a 12-floor

steel frame building with the damage tolerance construction technique, which employs

dampers to absorb seismic energy. These embedded sensors can measure relative

displacements, strains, and temperatures. They were multiplexed in six single optical

fibers to monitor the performance of these dampers.

5.1.2 Piles

Piles are very important to support structures and protect buildings from shocks or

earthquakes. In December 2001, 30 FBG sensors were multiplexed into six optical fiber



arrays for driving test monitoring of two composite marine piles. Among them, four

arrays consisting of six FBGs along the pile were used to monitor the strains and two

arrays consisted of three FBGs were used to monitor the temperatures. The FBG

sensors were interrogated by an unit using fiber Fabry–Perot tunable filter technology.

The piles tested are 60-ft long with a diameter of two ft. A three-point bending test was

performed to ensure the survivability of al the FBGs and the insensitivity of the FBG

temperature sensors to mechanical strain. Then, driving tests were conducted and real-

time monitoring showed that apparent bending existed in the pile. Although the

survivability of FBGs in pile driving was verified, the fiber lead of a strain-sensing

array was broken and the readings of the FBG temperature sensor suffered from strain

cross-sensitivity.

Similar pile drivings were conducted to test the foundation of a new factory, which

requires a highly stable base, in the Tainan Scientific Park, Taiwan. All the six tested

piles had the same dimension with a diameter of 1.2 m and a length of 35 m. Nine 4-m

LGFOSs were utilized in the compression and pullout tests of a pile to measure the

strain and load eccentricity. Sixteen 4-m LGFOSs were installed in parallel on the

opposite side of a pile to monitor the average curvature, which were used later to

compute the horizontal displacement by double integration. From the above tests,

Young’s modulus, longitudinal strain, vertical displacement and force in the piles were

measured. In addition, properties of soil, critical strain when crack occurs in the pile,

ultimate load capacity of a pile, and failure mode in the interface of soil and pile were

also measured. The sensors and demodulation system were provided by Smartec SA.

5.1.3 Bridges

Although FOSs have been embedded or attached to many concrete bridges, steel

structures equipped with FOSs are not so common. This may be is attributed to the fact

that it is almost impossible to embed an FOS in a steel structure element. Surface-

bonding, at present, is the only way to integrate an FOS with a steel structure, and the

benefits of utilizing FOSs in such situations are not evident. . This bridge is a 67 m steel

truss bridge spanning the Winooski River. Thirty-six chloride FOSs were embedded at

various points along the bridge to monitor the chloride penetration into the deck. The



chloride sensor is based on the interaction between the chloride ions and a sol–gel film,

which is positioned between the input–output fiber. The film’s transmission

characteristics changes in terms of color (from milky white to pink); thus overall fiber’s

transmission change and chloride ion’s concentration are determined. Another 16 FBG

strain sensors were placed at points of the reinforcement bars with maximum strains to

monitor the strain variations. Their efforts showed that instrumentation of FOSs may

cost up to 10% higher in certain cases.

5.1.4 Highway traffic monitoring

Although FOSs embedded in the Beddington Trail Bridge is intended for long term

SHM and therefore employ low rate data sampling system, they can still weigh vehicles

running slowly. However, these FOSs are inadequate for traffic monitoring, such as

classifying vehicles, on a regular highway without traffic interruption since the FOSs

used for such purposes demand a high-sampling-rate data acquisition system in addition

to high measuring sensitivity.

A traffic sensor is basically a sensor embedded on the surface of a road to detect traffic

flow. The dynamic testing system developed by Udd et al. can achieve less than 0.1

micro-strain resolution with a dynamic range of 400 micro-strain at 10 kHz sampling

rate, which can satisfy such traffic monitoring requirements. They installed 28 specially

designed FBG traffic sensors (26 survived) in surface-cut slots of the Horsetail Falls

Bridge in the Colombia River Gorge National Scenic Area of the United States, and

tested the monitoring system by running vehicles of different weights at a speed of 10–

18 km per hour. Then, five long gage FBG sensors were installed in the I-84 freeway to

test the ability of these sensors as vehicle classifier and counter. Over half a year’s

monitoring showed that the sensing systems are sufficient to discriminate tractor–trailer

and buses, and even the traffic in adjacent lanes in some cases. The amplitude of the

signal appears to be closely proportional to the vehicle weight, the speed of a vehicle



and its driving direction are determined by the separation of peaks and their order of

appearance in adjacent FBG sensors.

In the above tests, the FBG sensors displayed many obvious advantages over traditional

loop inductors, piezo-ceramic weigh-in-motion systems and regular vehicle monitoring

devices because of its immunity to radio and electrical interference. In addition, the

FBG sensors have the potential to act as road parameter sensor (humidity, ice,

temperature, etc.) and they can be integrated into one FOS intelligent transportation

system for infrastructure maintenance planning, traffic surveillance, traffic monitoring,

and road tolls.

5.1.5 Pipelines

Not only can average strains be measured, but cracks can also be monitored by FOS.

Cracks of a concrete pipe by attaching a continuously distributed fiber optical sensor

with a bend configuration to its interior surface during loading tests. This special design

improved the spatial resolution of BOTDR, and small cracks, which are beyond the

ability of a traditional strain gage, could be identified. Another approach to detect a

crack formation and propagation is to use a kind of “zig-zag” sensor monitored by an

OTDR. The backscattered optical power in the fiber decreased obviously if crack

opening developed beyond 0.1 mm. This sensor has the ability to detect cracks that

intersect the fiber at an angle other than 90°.

FOSs can also monitor cracks or accidents that occur in long pipelines. In Indonesia, a

110 km pipeline was equipped with a vibration sensor to monitor its integrity and to

alert ongoing damages because of third party interference from excavation equipment,

theft, landslide or earth movement . Based on the principle of modal-metric interference

effect, the vibration monitoring unit pinpoints the location of anomaly by detecting the

changes of backscattered light characteristics caused by disturbances of fiber

compression, elongation or twist. In October 1998, the system operator was alerted of a

major event that caused damages to the pipeline at 46.81 km. Upon further inspection,

it was discovered that a landslide had occurred and a section of 24-inch pipeline was



fully exposed. This system can monitor a fiber of length up to 50 km with a resolution

of 0.1 km. The monitoring technology demonstrated its unique potential in health

monitoring of long pipelines since such accidents to pipelines could not be easily

detected by other conventional methods.

Moreover, new types of leakage detecting sensors are being developed to exploit this

huge potential market. It is reported that several novel FBG based pressure, flow, and

seismic sensors could be used in reservoir, oil and gas pipeline, and well-drilling

applications.

5.1.6 Tunnels and dams

Tunnels and dams are large civil structures to utilize FOSs. The Champ Baly tunnel,

built by the cut-and-cover method, connects Lausanne with Bern in Switzerland. FOSs

were used to measure the behavior of different components of the tunnel (foundations

and vaults). The 4-m long gage displacement sensors were embedded on the rebars in

two adjacent sections of the tunnel before concrete pouring. One section was built of

ordinary concrete, and the other one of high performance concrete. In the ordinary

concrete section, six sensors were installed in the foundation, and three in the vault. The

reading unit was placed in the tunnel. Although one sensor in the foundation was

damaged during the pouring, the monitored displacements in about seven months

clearly showed three periods of the concrete deformation process: dormant period,

thermal swelling period, and shrinkage period. This result was consistent with that

measured by thermocouples. In addition, one sensor detected a 50-μm crack opening at

the interface of the foundation and the vault, which was caused by different thermal

stress between two parts and was later visually confirmed.



Conclusions

Several laboratory sensor characterization tests were designed to produce data

on the behavior under static and pneumatic loading conditions similar to those

encountered in the pavement. Data was obtained on the load to study (1) sensor

light loss under both static and pneumatic loads, and (2) deflection versus light

loss under pneumatic loads. The load-deflection, light loss data was input into

several finite element models that were used to study the sensor pavement

interaction. According to these models, when the sensors ware placed in vertical

grooves, they have an optimal placement zone near the surface.



A microbend fiber-optic traffic classification and WIM sensor was developed.

Based on the microbend fiber-optic sensor technology, the least expensive of

the fiber-optic sensor technologies currently available, this sensor can be

constructed with off-the-shelf materials for a cost similar to the piezoelectric

sensors. It can be placed in a narrow groove in either flexible or rigid

pavements. This embedment procedure would minimize pavement damage. An

opto-electronics interface device has been developed that allows the light-

intensity signals from the sensor to be converted to voltage. Various traffic

classifiers and WIM computer control systems can read the voltage output from

this box.

The sensors are corrosion resistant, reliable, and immune to electromagnetic

interference. The installation procedure is simple a masonry blade in a standard

saw is all that is required for cutting the groove.

the FBG sensors displayed many obvious advantages over traditional loop

inductors, piezo-ceramic weigh-in-motion systems and regular vehicle

monitoring devices because of its immunity to radio and electrical interference.

References

Paul J. Cosentin, and Barry G. Grossman, “Analysis of Fiber Optic Traffic

Sensors in Flexible Pavements”, From ASCE, Vol.129, No. 5, September 1,

2003.



Whitten L. Schulz.a, John Seima, and Eric Udda, “Traffic monitoring/control

and road condition monitoring using fiber optic based systems”, Blue Road

Research.

E. Udd, M. Kunzler, W. Schulz, S. Kreger, J. Corones et al., Fiber grating

systems for traffic monitoring, [email protected].

Paul Cosentino, optimization and implementation of fiber optic sensors for

traffic cliassification and weight-in-motion systems, Summary of Final Report

by the Florida Institute of Technology, October 2000.

Dong-Sheng Li and Gang-Bing, “Engineering Structures” Volume 26, Issue

11, Pages 1647-1657 September 2004.


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Fiber Optic Traffic Sensor