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Comparative Study on different Bridge Scour Monitoring Techniques: A

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Conference Paper · December 2015

DOI: 10.13140/RG.2.1.3197.1925

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HYDRO 2015 INTERNATIONAL IIT Roorkee, India, 17-19 December, 2015
20th International Conference on Hydraulics,
Water Resources and River Engineering

Comparative Study on different Bridge Scour Monitoring Techniques: A

Review
Sreedhara B M1, Manu2, Pruthviraj U2
1Research Scholar, Department of Applied Mechanics and Hydraulics, National Institute of Technology
Karnataka, Surathkal, Mangalore-575025, India.
2 Assistant Professor, Department of Applied Mechanics and Hydraulics, National Institute of Technology

Karnataka, Surathkal, Mangalore-575025, India.

Email: bmshreedhar@gmail.com, manunitk77@gmail.com, pruthviu@gmail.com

Abstract
Scour is the erosion of sand and rock by the action of flowing water, which causes stream stability problems, as
well as bridge failures. Scour around bridge foundations is one of the leading causes of bridge failure which
affects hundreds of thousand bridges and costs hundreds of millions of dollars in direct repair costs. Scour is a
common soil-structure interaction problem. Hence, this necessitates a profound research in the area of scour
protection to avoid the bridge failures. This paper intends to discuss various types and mechanisms of scour and
existing laboratory experimental setup for the bridge scour monitoring. The current review also explains several
pier scour monitoring devices such as Sonars, Magnetic Sliding Collars (MSC), Float out devices, Time Domain
Reflectometry (TDR), Fibre Bragg Grating (FBG), Ground Penetrating Radar (GPR) which are used to measure
the scour depth around the pier. The review also discusses about Soft Computing techniques such as ANN,
SVM, and ANFIS etc. are recently used for prediction of bridge scour using field or experimental data. The
paper also provides the advantages and limitations of the scour monitoring devices and soft computing
techniques.

Keywords: Scour; Scour Monitoring Devices; Soft Computing; Bridge protection

1. Introduction

Bridges are the life line structures which spanning and providing passage over a gap or a barrier, such
as a river or roadway. The engineering design of a hydraulic structure such as a river bridge requires
consideration of the factors that affect the safety of the structure. Among them, two of the most
important variables are bridge foundation scour and construction cost, because Scour is the one of the
main causes for Bridge failure. Scour is the removal of sediment around or near structures located in
flowing water. It means the lowering of the riverbed level by water erosion, such that there is a
tendency to expose the foundations of a bridge.
The mechanism of scour around bridge piers is often complicated and it is difficult to establish a
general empirical relation for its prediction under different field conditions. In order to understand the
mechanism of bridge scour, many investigators have studied pier scour in Laboratory experiments
such as, Ettema et al. (2015), Ismael et al. (2015), Verappa Devaru et al. (2013), Akib et al. (2012)
etc.,. Study further carried out for developing the soft Computing models ANN, ANFIS, SVM, etc.,
Using experimental data by Akib et al. (2014), Yasser (2013), Hong et al. (2012), Azamathulla et al.
(2011), Goel et al. (2009), Pal et al. (2011) etc,. And also the soft computing results are compared
with some numerical equations.

1.1 Bridge Scour

Bridge scour is the removal of sediment such as sand and rocks from around bridge abutments or
piers. When water flows through a bridge opening with sufficient velocity the bed, in general, will
change in elevation. This change in elevation is more significant near the abutments and piers. The
magnitude of these changes depends on many factors, including the flow and sediment parameters,
structure size and shape, local and global channel characteristics, etc.

1.1.1 Types of Bridge Scour

HYDRO 2015 INTERNATIONAL IIT Roorkee, India, 17-19 December, 2015
20th International Conference on Hydraulics,
Water Resources and River Engineering

Scour around the pier and pile supported structures and abutments can result in structural collapse and
loss of life and property. The amount of this reduction below an assumed natured level is termed
scour depth. Scour is usually divided into categories.
For analysis purposes, it is convenient to divide bridge scour into the following categories:
 General scour,
 Long term aggradation/degradation,
 Contraction scour,
 Local structure-induced pier and abutment scour.
General scour is the general decrease in the elevation of the riverbed. It occurs irrespective of the
existence of the bridge. Lowering of the streambed across the channel or stream over relatively short
time periods. General scour depth may be uniform across the bed or non-uniform.
Long term aggradation and degradation refers to the change in the bed elevation over time, over an
entire reach of the water body. Aggradation involves the deposition of material eroded from upstream
channel or watershed. Degradation involves the lowering or scouring of the stream bed due to the
upstream sediment supply deficit.
Contraction scour occurs when a channel’s cross-section is reduced by natural or man-made features.
The reduction of cross sectional area results in an increase in flow velocity due to conservation of
flow. This may cause the condition of more sediment leaving than entering the area and thus an
overall lowering of the bed in the contracted area.
Local scour can be defined as the degradation of river banks and/or bed that is localized to a specific
area due to a sudden change in the parameters associated with the river. Local scour involves the
removal of bed material around a structure located in moving water. It is the result of flow field
changes due to the presence of a structure. Local scour can occur as either clear-water scour or live
bed scour. Clear-water scour refers to the situation where no sediment is supplied from upstream into
the scour zone. Live-bed scour, on the other hand, refers to the situation where sediment is
continuously being supplied to the areas subjected to scour.
Total Scour refers to the total depth of scour at the particular bridge foundation. It is the sum of long-
term degradation, general (contraction) scour, and local scour.

Figure 1 Types of scour that can occur at a Bridge

1.1.2 Mechanism of Local Scour

The flowing pattern of a normal flow comes to sudden change when in encounters a pier on its path.
Large scale eddy structure or the system of vortices develop at the base of the pier. The eddy structure
is normally composed these components.
 Horseshoe vortex
 Wake vortex system
A flow running at a particular velocity, when approaches to the pier comes to complete rest which
results in increase of pressure at the water surface near the pier. The velocity of the flow gradually
decreases from top to bottom and consequently the pressure also decreases from top to bottom. This
creates a downward pressure gradient that forces the flow to move downward like a jet of water. This
HYDRO 2015 INTERNATIONAL IIT Roorkee, India, 17-19 December, 2015
20th International Conference on Hydraulics,
Water Resources and River Engineering

vertical jet when strikes the bed makes a hole in the immediate vicinity of pier base. The strength of
the down flow reaches maximum just below the bed level. The down flow rolls up as it continuous to
create a hole and through the interaction with incoming flow converts into a complex vortex system of
horseshoe shape and hence called horseshoe vortex. The horseshoe vortex is very effective at
transporting the removed particles away from the pier. The transport rate from the base region is
greater than that from the wake region.
The separation of flow at the pier sides produces so called wake vortex. Wake vortices are rotate
about vertical axis and also erode sediment from pier base. The wake vortex system somewhat acts
like a vacuum cleaner that sucks the material and carries away. The intensity of the wake vortices
drastically reduces with distance downstream, such that sediment deposition is generally immediately
downstream of the pier. The horseshoe and wake both the vortices work at the same time to scour
around the pier.

Figure 2 Mechanism of scour at a Circular pier (Hamill, 1998)

2. Bridge Scour Monitoring Techniques

Scour, is the leading cause of bridge failure and monitoring scour is important for the continued safe
operation of the bridge. Scour monitoring is of different types such as field observations using scour
measuring instruments, conducting laboratory experiments for the respective field condition and
developing soft computing and numerical models using field or laboratory data.

2.1 Bridge scour monitoring devices

Many types of scour monitoring devices are available for measurement of local scour around bridge
pier. The most common types are Sonars, Magnetic Sliding Collars (MSC), Float out devices, Time
Domain Reflectometry (TDR), Fibre Bragg Grating (FBG), Ground Penetrating Radar (GPR) etc.

2.1.1 Sonars

A parameter that is fundamental to the operation of the sonar transducer is the speed of sound in water
(Fisher et al. 2013). Sonar instruments, also known as acoustic transducers, use sound waves to ping
the bottom of a waterway. The corresponding echo will indicate depth to the riverbed. When the
transducer is angled toward an area of potential scour, the sensor will measure the level of erosion
occurring. The data logger controls the sonar device and data collection functions. The data
logger is programmed to take measurements at prescribed intervals. These instruments can
track both the scour and refill processes (Leuker et al. 2010). Most sonar instruments are
mounted directly to a pier or substructure of a bridge. Sonar device is affected by aerated flow and
bed load. This type of sensor device is not structurally robust, but the device may be mounted
in a variety of elevations out of the way of debris.
HYDRO 2015 INTERNATIONAL IIT Roorkee, India, 17-19 December, 2015
20th International Conference on Hydraulics,
Water Resources and River Engineering

Figure 3 Sonar mounting plate and various components (Nassif et al. 2003)

2.1.2 Ground Penetrating Radar (GPR)

Ground-Penetrating Radar is an effective technique for measuring scour before and after a flood,
when the water is low. GPR data can be readily collected in any area around the bridge using a
portable, inflatable boat as a deployment platform. GPR is also an effective tool to measure scour
around the entire perimeter of piers and along the upstream and downstream bridge faces (Boehmler
et al. 2000). The basic principle underlying GPR is that by transmitting electromagnetic pulses into
the ground, or other medium, it is possible to identify the internal structure and its properties by
measuring the signals, which are reflected from the interfaces between different geological materials
(Forde et al. 1999). GPR was used to provide continuous profiles of the streambed surfaces.
GPR was found to be an effective tool for detecting existing scour holes, infilled scour holes, and
previous scour surfaces at bridge sites (Olimpio 2000). The equipment is relatively expensive and data
may be contaminated by noise (Webb et al. 2000).

2.1.3 Magnetic Sliding Collars (MSC)

Magnetic sliding collars slide on rods or masts that are driven or augured into the streambed. A collar
with magnets is placed on the streambed around the rod and triggers sensors in the rod. If the
streambed erodes, the collar moves or slides down the rod into the scour hole. The depth of the collar
provides information on the scour that has occurred at that particular location. The magnetic sliding
collar may be automated or manually read. The automated type is driven into the bed and is connected
to a datalogger using flexible wires that convey magnetic switch closures (Leuker et al. 2010).
Both versions of the sliding collar method should be firmly driven into the streambed. Installations are
easier in shallow rivers and during low-flow events. The distance between the centerline of the steel
rod and the pier was about 200 mm, which was also within the region of maximum scour in front of
the pier. One of the limitations of the SMC measurement system is that it cannot detect riverbed
deposition during flood recession. The sliding magnetic collar has a cylindrical shape with three
round-bar magnets fully enclosed in three stainless-steel housings to prevent corrosion. A stainless-
steel plate was welded outside the SMC to minimize the possible damage from debris impact (Jau
Yau Lu et al. 2008).

Figure 4 Magnetic sliding collar system and its components (Nassif et al. 2003)
HYDRO 2015 INTERNATIONAL IIT Roorkee, India, 17-19 December, 2015
20th International Conference on Hydraulics,
Water Resources and River Engineering

2.1.4 Fiber Bragg Grating (FBG)

Fiber Bragg grating (FBG) sensors are highly attractive owing to their inherent wavelength response
and their multiplexing capability for the distributive sensing network (Lin et al. 2004). FBG types of
sensors operate based on the concept of measuring strain along embedded cantilever rods to generate
electrical signals, which can indicate the progression of scour along the rod. An embedded rod that
becomes partially exposed due to scour will be subjected to hydrodynamic forces from the flow of
water that induce bending in the exposed rod. This bending allows the strain sensors to detect that the
rod is free. If a number of strain gauges are positioned along the rod, the progression of scour may be
monitored (Prendergast et al. 2014). FBG scour-monitoring system can also measure both the process
of scouring and the variation of water level changing. Based on the concept of a button like
mechanism, the FBG sensor in the system is covered with a waterproof rubber seal like a button
housing. A stop bolt and a spring inside each button housing prevent damage to the FBG sensor in
case of a flood or if debris in the flow (Lin et al. 2006).

2.1.5 Time Domain Reflectometry (TDR)

Time-domain reflectometry (TDR) to measure the level of sediment around bridge pier or structure.
The apparatus includes a time domain reflectometer which transmits a series of electrical pulses, a
sensor which is connected with said time-domain reflectometer, and a signal analyzer which receives
and interprets the portion of the electrical pulses reflected back to the source from the stream bed
(Yankielun et al. 1998). They operate based on the principle that when the propagating wave reaches
an area where the dielectric permittivity changes (e.g. the water-sediment interface), a portion of the
energy is reflected back to the receiver. They can therefore be used to observe the variation of scour
depth with time (Elsaid et al. 2012). TDR may provide an accurate and reliable means of obtaining
real-time scour and sediment transport data and it has the potential of providing continuous dynamic
sediment scour and deposition data even during high flow (Yankielun et al. 1999).

2.1.6 Float out devices

Buried at strategic points near the bridge, float-outs are activated when scour occurs directly above
the sensor. The sensor floats to the stream surface and an onboard transmitter is activated and
transmits the float-out device’s digital identification number to a data logger (Leuker et al. 2010).
These devices are installed vertically into the bed, the internal radio transmitter triggers a signal when
the device is in horizontal position. It indicates that scour depth has reached a level and now the
system is in float out state. The device gives output in the form of two discrete values 0 and 1, 0
indicates the device is in vertical position and gives 1 if the device is float out. These devices are
buried beneath the bed, they are not possible to damage from debris. They are also very easy to install
in dry beds and riprap. The float out devices gives an estimation of local scour only at the device
installed location (Hurlebaus et al. 2011).

2.2 Bridge Scour Monitoring using oft Computing Techniques

Artificial intelligence has been used in a wide range of fields including hydrological problems, coastal
parametric prediction, weather and climate forecasting, sediment transport prediction etc. Artificial
Intelligence (AI) is usually defined as the science of directing computers to do things that require
intelligence when done by human beings. Fuzzy logic, Artificial Neural Network (ANN), Adaptive
Neuro Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and some hybrid models
are the important tools used for the assessment of bridge scour. The overall form of these approaches
will be dictated by the nature of the problem, the data type available and the kind of prediction
needed. Data collected from laboratory experiments will be categorized, compiled and organized in a
systematic data base and will be divided into two sets training and testing the soft computing models.
2.2.1 Artificial Neuron network
HYDRO 2015 INTERNATIONAL IIT Roorkee, India, 17-19 December, 2015
20th International Conference on Hydraulics,
Water Resources and River Engineering

Artificial neural network models are adaptive and capable of generalization. They can handle
imperfect or incomplete data, and can capture nonlinear and complex interactions among variables of
a system (Kaya 2010). Multilayer Perceptron (MLP) and Radial Basis Function (RBF) network has
trained with the experimental data and an appropriate model of each of the network can be identified
(Begum et al. 2012). A typical three-layered network structure with an input layer ( I ), a hidden layer
( H) and an output layer (O) shown in figure 5. The parameters governing the equilibrium scour depth
around circular pier are the fluid flow pattern, bed sediment properties, and pier geometry (Ismail et
al. 2013). The performance of all ANN configurations was assessed based on calculating the mean
absolute error (MAE), and the root mean square error (RMSE) (Bateni et al. 2007). Drawbacks of
ANN include that it needs substantial training time and the difficulties in detecting hidden neurons in
hidden layer for better predictions (Azamathulla et al. 2010).

Figure 5 Structure of an artificial neural network (Lee et al. 2007)

2.2.2 Adaptive Neuro Fuzzy Inference System

Artificial Neuro-Fuzzy Inference Systems (ANFIS) was first introduced by Jang in 1993. It is a
combination of least-squares and back propagation gradient decent methods used for training Takagi-
Sugeno type fuzzy inference system which is used for an effective search for the optimal parameters.
It can provide a starting point for constructing a set of fuzzy ‘if-then’ rules with appropriate
membership functions to generate the fixed input-output pairs (Azamathulla et al. 2010). ANFIS
contains five layers in its architecture: a fuzzify layer, a product layer, a normalized layer, a
defuzzifier layer, and a total output layer as shown in figure 6 (Akib et al. 2010). The performance of
ANFIS model in training and testing sets is validated in terms of the common statistical measures,
(R2) coefficient of determination, RMSE, MAE, and (δ) average absolute deviation (Keshavarzi et al.
2012).

Figure 6 ANFIS architecture (Akib et al. 2010)

2.2.3 Support Vector Machine

A SVM for regression has been proposed by Vapnik et al. 1997. A SVM constructs a separating
hyperplane between the classes in the n-dimensional space of the inputs. The hyperplane maximizes
HYDRO 2015 INTERNATIONAL IIT Roorkee, India, 17-19 December, 2015
20th International Conference on Hydraulics,
Water Resources and River Engineering

the margin between the two data sets of the two input classes. SVR requires setting of fewer user-
defined parameters. In addition to the choice of a kernel, SVR requires the setting up of kernel
specific parameters (Pal et al. 2011). There are two types of kernel functions are radial based and
polynomial based function. Seven input parameters namely pier shape factor (Ps), pier width (Pw),
skew of the pier to approach flow (skew), velocity of the flow (V), depth of flow (h), D50 (i.e. the
grain size of bed material in mm for which50 percent is finer) and gradation of bed material (σ) were
used to predict the scour depth (Mahesh Pal et al. 2011). The correlation coefficient (CC) and root
mean square error (RMSE) values are used for the performance evaluation of models and comparison
of the results for prediction of scour using SVMs. The higher value of correlation coefficient and a
smaller value RMSE mean a better performance of the model. Further, measured scour values were
plotted against the computed values obtained with SVMs algorithm (Goel et al. 2009).

Figure 7 Network architecture of SVM (Sujay et al. 2014)

3. Summary

Scour, is the leading cause of bridge failure and monitoring scour is important for the continued safe
operation of the bridge. Based on the literature, some of the conclusions and limitations of scour
monitoring techniques are as follows.
 Sonars devices are suitable for sandy or riprap stream bed and less debris in the flow. Also
possible to affected by bed load and aerated flow.
 Ground penetrating radar is an effective tool for monitoring scour holes, infilled holes and
previous scour holes. Device is relatively expensive, data may be contaminated because of
multiple reflection and echoes from the footings. The device is effective for water depth less
than 9m.
 Magnetic sliding collars are suited for bridges with shallow stream maximum of 3m and it
cannot detect riverbed deposition during flood recession. MSC are of automated and manually
reading system but automated system is relatively costly.
 Fiber Bragg grating devices are capable to measure scour depth, variation in water level and
deposition height. They may possible to damage from the high velocity flood, debris and
sediments.
 Time domain reflectometry may provide reliable and accurate scour data and sediment
transport even in high flow. These sensor works well in clear water.
 Float out devices installed beneath the stream bed, they are damage free from debris. These
are easy to install in dry or riprap bed. The devices gives an estimation of local scour only at
the device installed location.
 Artificial neural network models can handle imperfect or incomplete data, and can capture
nonlinear and complex interactions among variables of a system. ANN models can predict the
scour depth in clear water, live bed condition and considering debris flow condition. These
models needs substantial training time and the difficulties in detecting hidden neurons in
hidden layer for better predictions.
 Adaptive neuro fuzzy inference models reaches the target faster that neural network and gives
good results because of combination of both neural network and fuzzy logic.
 SVMs can produce accurate and robust classification results even when input data are non-
monotone and non-linearly separable but computationally expensive, thus runs slow.
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HYDRO 2015 INTERNATIONAL IIT Roorkee, India, 17-19 December, 2015

20th International Conference on Hydraulics,
Water Resources and River Engineering

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