International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 5 Issue 4, May-June 2021 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
An In-depth Review on Bridge Crack Detection Approaches
Ketan Ovhal1, Ruturaj Lokhande1, Omkar Kamble1,
Prathamesh Nanaware1, Samarsingh Jadhav2
1,2Department
1Student, 2Professor,
of Computer Engineering, PVPIT, Pune, Maharashtra, India
How to cite this paper: Ketan Ovhal |
Ruturaj Lokhande | Omkar Kamble |
Prathamesh Nanaware | Samarsingh
Jadhav "An In-depth Review on Bridge
Crack Detection Approaches" Published in
International Journal
of Trend in Scientific
Research
and
Development (ijtsrd),
ISSN:
2456-6470,
Volume-5 | Issue-4,
June 2021, pp.957IJTSRD42401
959,
URL:
www.ijtsrd.com/papers/ijtsrd42401.pdf
ABSTRACT
Bridges are mega structures that have been utilized and built for millennia.
These structures are highly effective in achieving transportation and commute
between two highly inaccessible destinations easily. Bridges are also highly
effective in reducing traffic was by allowing the use of an alternate path for the
traffic flow to be resumed. This makes them highly versatile and extremely
effective in various scenarios. But as with any constructed structures, these
bridges need to be evaluated for their structural integrity and surveyed for
any flaws or cracks that have been emerged over time. This is usually done
manually by a civil engineer which is a time-consuming process and can also
introduce human error. Therefore to improve this procedure and number of
related works have been analyzed extensively to achieve bridge crack
detection through image processing methodologies. An effective approach has
been envisioned through the use of convolutional neural networks and
decision tree techniques to achieve bridge crack detection which will be
further elaborated in the next edition of this research article.
Copyright © 2021 by author (s) and
International Journal of Trend in Scientific
Research and Development Journal. This
is an Open Access article distributed
under the terms of
the
Creative
Commons Attribution
License
(CC
BY
4.0)
KEYWORDS: Convolutional Neural Networks, Decision Tree
(http://creativecommons.org/licenses/by/4.0)
I.
INTRODUCTION:
Bridges are effective and highly useful structures that allow
for transportation and computer between two points that
are not easily accessible. Bridges allow foreign keys in
computer and can effectively reduce the traffic problems by
creating another route for transport in the same amount of
available space. This allows overlapping roads to effectively
allow computers to go on their journey without any hiccups
or interruptions by traffic signals or other traffic
management devices. Bridges are also highly effective in
bridging the gap between accessible entities wherein a road
cannot be constructed between the two for allowing effective
transport. Bridges are highly useful and have been
constructed for millennia.
As the bridges are made in extreme environments they are
subjected to environmental forces which can be slowly
damaging to the colossal bridge structure. The bridges need
to be structurally perfect so that there is no mishap that can
occur while using those bridges for transportation or
commute. Engineers are effectively performing regular
service on various bridges that are still standing today to
determine the amount of damage that is being done onto the
bridge and provide any effective repairs before any mishap
occurs. As bridges are used extensively there is a need to
maintain the structural performance in order to produce
maximum performance from the bridges and achieve
effective improvements.
The bridges also can provide a path from one place to
another over extremely dangerous territories such as valleys
or rivers for people to commute every day. These bridges are
useful and can be used to transport goods provide railway
tracks or ease traffic conditions in a meaningful way. The
civil engineers design these bridges through an extensive
survey of various different conditions of the environment
and produce the most efficient design to help implement in a
safe and structurally sound manner. There are various
bridges across the world that are more than a hundred years
old and are still standing and being used regularly by the
people. The reason these bridges are still effectively useful is
due to effective design methodologies and principles along
with highly effective maintenance.
Usually, the process of bridge maintenance is done manually
where the engineers would physically examine the structure
of the bridge along with various important landmarks such
as beams columns, and other rigid structures for early signs
of weakness or structural loss of integrity by looking out for
cracks. This is a highly laborious process and it is extremely
time-consuming for the engineer to perform this survey
which is highly essential for maintaining the integrity and
the structural reliability of the bridge intact. Therefore there
is a need to improve this procedure through the
implementation of technology to achieve effective
maintenance of the bridge structures. For this purpose, a
number of researchers have been analyzed to achieve the
prescribed approach in the implementation of image
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processing approaches for identifying cracks in the bridge
structure.
A large number of researchers have been analyzed for the
purpose of achieving our goals of implementing image
processing approaches for bridge crack analysis. These
approaches have been significant in achieving our approach
for the implementation of an effective crack detection
methodology that utilizes image normalization along with
convolutional neural networks and the decision tree
approach.
This literature survey paper dedicates section 2 for analysis
of past work as a literature survey, and finally, section 3
concludes the paper with traces of future enhancement.
II.
RELATED WORKS
V.M. Jovanovic sequential-probability-ratio-test (SPRT) is
proposed and analyzed for Detector logic depends on the
optimal. [1] This proposed technology is also compared with
the heuristic sequential technique. Pseudo-noise waveform
is generated locally due to correlation received from the
waveform. The simple “generalized” up-down counter logic
is approximated detector with devised. Thus this new
detector is compared with some heuristic sequential which
are analyzed in past.
T.Thesken introduces Continuous-phase modulation (CPM)
for a relatively narrow spectrum. Is environment is coherent
by using an additive-white- Gaussian-noise (AWGN) for the
optimal receiver. [2] They calculated the channel’s cutoff
rate, Ro. Just to determine the good a channel is when
encoded. Thus to find the encoded data and to check the best
performance code. Thus by using random coding parameter
Ro behavior of the channel with coding is analyzed on
average.
Gary Garrabrant presents Additive White Gaussian Noise
(AWGN) channels for the non-coherent detection of
orthogonal signals is implements trellis codes.[3] For a
communication system, an alternative coding technique is
developed by using non-coherent detection of the orthogonal
signal set where the encoding can be expanded. To prove the
trellis coding is successful it is compared with a non-binary
block code.
J.Zhang states beam bridges get cracks or girders due to
overweight freight vehicles, earthquakes. To detect the
crackings and faults there is a lot of research made in recent
years. [4] For fault detection purposes vertical and
longitudinal axial is observed by using the response signals.
Thus to cracking fault detection by using traditional vertical
vibration signal is very sensitive by measuring longitudinal
strain.
S.Zhang presents object detection thus to detect objects of
present categories in the field of computer vision. Thus by
using image recognition and video surveillance for accurate
object detection there are various applications developed.
[5] There various techniques used for crack detection such
as anchor-based detectors with the development of
convolutional neural network (CNN). According to statistical
features of an object, the proposed paper proposes an
Adaptive Training Sample Selection (ATSS).
Y.Cai describes one of the diseases that is commonly found
that is Cracks in tunnels, buildings, bridges, and other
structures. Cracks are often generated regularly or
irregularly it has to be repaired on time otherwise it will
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create a problem. [6] Thus the proposed paper implements a
crack measurement system with the combination of a high
magnification image acquisition system and laser ranging
system. Thus the proposed paper implements an effective
approach that is efficient and convenient.
H. Cho implements the detection of cracks in concretes by
using an image-based methodology. In recent years there has
been lots of research made in the field of crack detection but
they have to face inaccurate extraction of edge pixels. [7]
Thus in proposed paper consist of five steps such as crack
region search, crack width transform, hole filling, aspect
ratio filtering, and relative thresholding. Thus the
experiments were tested on synthetic and real images, which
has resulted has consistency in measuring crack and greater
accuracy.
M.Lin elaborates the issue of the road such as Cracks. In
recent years there is a lot of research and many funds
invested for the automatic detection of cracks. Thus the
proposed paper implements an accurate crack detection
method. [8] The method is divided into two parts one is
implemented using a hidden Markov random field model and
the adaptive line detector and the second is developed by
using the integrity conditional connection and by a
quantitative description of crack regions.
H Rowshande describes crack detection as one of the
promising topics of research in recent times. Depending on
the low-speed walking stick method they have used an ACFM
sensor for classification and detection of RCF cracks. [9] The
CTSM algorithm is the existing system that has low-level
accuracy thus to overcome this shortcoming thus the
proposed paper implements a method for detecting multiple
RCF cracks. Thus the detected cracks can be maintained can
be used to reduce inspection time.
Jingyi Li develops crack detection technology by using
Science and technology. Thus the proposed paper develops a
crack detection system by using digital image processing.
[10] This system is divided into two parts as one part is the
hardware part of the detection system and the second is the
software part of the crack detection system which is
extracted from the acquired images to detect the crack. Thus
in hardware image is collected from the car camera and
signal processors. Thus the proposed paper is known as an
effective approach.
Yuwen Quan describes road rehabilitation road crack
detection as known as important for road crack detection.
[11] On gray histogram is proposed on the basis of Otsu
threshold crack detection. Thus the proposed methodology
upgrades the accuracy of target extraction and keeps away
the prominent peaks. By using the road image processing of
various crack types. There are the various steps Crack Image
Pre-Processing, Crack Image Segmentation. Thus the
proposed paper results provide good consistency in
detecting cracks.
W.Li elaborates china is known as one of the developing
nations in high-speed railways. There are various challenges
affected such as damage from external factors and aging of
their structures. [12] Thus the proposed paper implements
the crack detection method in a very complex environment.
The implement is used for histogram equalization just for
reducing uneven illumination for the detecting cracks and
then Canny edge detection algorithm for adapting dual
thresholds.
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J.Han explains in the concrete structure inspection field is
known as one of the important tasks. There are much
techniques implemented such as convolutional neural
networks thus they can crack results and blurred crack
images [13]. Thus solutions for this introduces a multi-scale
feature fusion technique for crack detection. Thus the
proposed architecture of CNN develops an encoder-decoder
structure for image segmentation technique. Based on the
quantitative measurements such as F-measure, recall, and
precision.
III.
CONCLUSION AND FUTURE SCOPE
An effective methodology for the purpose of bridge crack
detection through the use of image processing techniques
has been envisioned in this survey article. Bridges are highly
useful structures that provide effective transportation and
commute that would have not been possible without the
construction of these structures. These structures are
usually massive in nature and allow for large trucks and
other transportation to be carried out effectively. These
mega structures are built by civil engineers through analysis
of various environmental conditions and a perfect plan for a
bridge is generated and the bridge is built. These bridges
also need to be effectively maintained and surveyed for any
flaws or that have emerged over time due to this bridge is
being subject to harsh environmental conditions. To improve
these survey capabilities which is usually performed
manually by engineer the utilization of image processing
techniques has been proposed in this survey article. This
server article has allowed for the creation of our approach
which implements convolutional neural networks along with
a decision tree to achieve effective bridge crack detection.
This approach will be further detailed in the upcoming
research article on this topic.
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