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Abstract. Over the past several decades, substantial amounts of sensors and sensing systems have
been developed for civil infrastructure systems. This special issue focuses on state-of-the-art robotics
and automation technologies, including construction automation, robotics, instrumentation, monitoring,
inspection, control, and rehabilitation for civil infrastructure. The issue also covers construction
informatics supporting sensing, analysis and design activities needed to operate smart and sustainable
civil infrastructure. Examples include robotic systems applied to civil infrastructure and equipped with
various sensing technologies, such as optical sensors, laser sensors, wireless sensors, multi-sensor
fusion, etc. This special issue is published in an effort to disseminate current advances of various
robotics and automation technologies for civil infrastructure and built environment.
Introduction
For several decades, various sensors and sensing systems have been developed to monitor and
assess the safety condition of structures. Structural health monitoring (SHM) is an essential component
in civil engineering for safety and integrity of civil structures such as buildings, bridges, power plants,
off-shore structures, and tunnels. Meanwhile, advanced sensing and robotics technologies greatly
facilitated construction automation of infrastructure systems. This survey paper discusses sensing and
robotics technologies in SHM, inspection, and construction automation systems. These include robotic
systems equipped with various sensing and sensor fusion technologies, such as optical sensors, laser
sensors, accelerometers, mobile wireless sensors, etc.
Thus far, robotic development for SHM has concentrated primarily on the inspection of structures
rather than the continuous monitoring of structures. This is because inspection for civil infrastructure
is a time consuming and labor intensive procedure as civil infrastructure often consists of large scale
engineering systems. Robots specialized for SHM applications have only recently been developed.
These robots have unique locomotion systems to provide mobility in the structures to be inspected,
and are equipped with various sensors such as cameras, lasers, RF (radio frequency), optical fibers,
accelerometers, etc. to monitor and assess structural condition.
Compared with decades ago, modern civil engineering has the unprecedented advantages of
benefiting from state-of-the-art sensing technologies. Both in research and in practice, the civil
engineering field has seen more and more real-world and large-scale sensor applications. While some
instrumentation systems remain cabled, others have started the revolutionary shift to miniature
wireless sensing devices or even mobile sensors (i.e. small tetherless robots carrying wireless sensors).
Either way, the rapidly evolving sensing technologies are providing unprecedented amounts of
information about our complex civil structures operating in harsh conditions in the field. The advances
in large-scale structural sensing, identification, and monitoring have been remarkable and
revolutionary over the past few decades.
In Section 2, recent advances in robotics and automation for civil infrastructure are discussed, with
a focus on sensing systems and robotic inspection systems. In Section 3, the papers published in this
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special issue are briefly discussed. Conclusions are given in Section 4.
2. Recent advances in robotics and automation for civil infrastructure
2.1 Sensing technology and multi-sensor fusion for civil infrastructure
An SHM system measures structural performance and operating conditions with various types of
sensing devices, and evaluates safety conditions using damage diagnosis or prognosis methods (Chang,
et al. 2003, Farrar, et al. 2003, Sohn, et al. 2004, Glaser, et al. 2007). Seismic structural displacement
measurement using line-scan cameras has been investigated (Nayyerloo, et al. 2011). For measuring
foundation pile movements, multiple displacements and motions of any structure can be determined in
real time at rates over 1 kHz. A novel edge tracking algorithm is proposed that enables high-resolution
measurement of large motions using relatively low-resolution, line-scan cameras.
Among many advances in SHM research, smart wireless sensors capable of embedded computing
and wireless communication have attracted wide interest. The ability to incorporate sensors with
wireless communication and embedded computing has motivated a flurry of research in developing
wireless sensors for SHM (Straser and Kiremidjian 1998, Glaser 2004, Lynch, et al. 2004, Shinozuka,
et al. 2004, Spencer, et al. 2004, Liu, et al. 2005, Wang, et al. 2005, Lynch and Loh 2006, Zhang and
Li 2006, Kim, et al. 2007, Nagayama and Spencer 2007). Not only laboratory demonstrations, but also
large-scale field applications of wireless SHM research have grown at an impressive rate (Lynch, et al.
2006, Zimmerman, et al. 2008, Pakzad 2010, Rice, et al. 2010). Recently, Rowe et al. (2011)
presented a sensor network called Sensor Andrew for managing the large-scale infrastructure. Lin et al.
(2012) developed a detection method of the damage location of the building with a wireless sensing
system. In a typical application, wireless sensing nodes are associated with high-precision
accelerometers for vibration measurements. Although recent development in wireless sensing devices
helps reduce hardware expenses, the cost of a wireless node with a high-precision accelerometer is still
at least several hundred dollars, which makes dense instrumentation still prohibitively expensive for
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large structures.
Compared with static sensor networks, mobile sensor networks offer flexible architectures with
adaptive and high spatial resolutions that can provide abundant detailed information about the
structure. It is anticipated that by implanting mobility into traditional sensors, mobile sensor networks
will play an essential role in next revolution of sensor networks (Kahn, et al. 1999, Akyildiz, et al.
2002, LaMarca, et al. 2002). Motivated by the interest to incorporate mobility into traditional sensors,
a few inspection robots have been developed for SHM. For example, a robot able to crawl on a 2D
surface was developed for visually inspecting aircraft exterior; the robot used ultrasonic motors for
mobility and suction cups for adherence (Backes, et al. 1997). A beam-crawler has been developed
for wirelessly powering and interrogating battery-less peak-strain sensors; the crawler moves along the
flange of an I-beam by wheels (Huston, et al. 2001). Based upon magnetic on-off robotic attachment
devices, a magnetic walker has been developed for maneuvering on a 2D surface (Brian, et al. 2004).
More recently, a remotely-controlled model helicopter was illustrated for charging and
communicating with wireless sensors (Todd, et al. 2007). Furthermore, researchers have recently
developed several mobile sensing nodes (MSN) for SHM. One example prototype incorporates a
flexible compliant beam between two magnet-wheeled cars (Lee, et al. 2009). As demonstrated by
Guo, et al. (2009), the accelerometer can be attached onto or detached from the structural surface by
bending or straightening the compliant beam of the flexure-based MSN, which also offers flexibility
for transiting over concave or convex corners of a steel portal frame (Zhu, et al. 2010). The MSNs are
also demonstrated to be able to navigate on a steel pedestrian bridge on Georgia Tech campus (Zhu, et
al. 2012), successfully illustrating the potential of MSNs in providing high spatial resolution for sensor
data collection.
Robotic inspection for civil infrastructure
Robotic inspection technologies have been broadly used for civil infrastructure. Chen et al. (2014)
developed a lightweight bridge inspector, which uses a dual-cable suspension mechanism lifting
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camera below the bridge to capture the crack under the bridge. Su et al. (2013) developed a real-time
pavement inspector, which uses a dual-light inspection method to improve the accuracy of the original
image processing method. Recently, the researches utilizing UAVs in civil engineering are increasing.
Many researchers use UAVs to capture aerial photos and these photos are used to build digital 3D
model through image processing and digital modeler technology. Using UAVs to capture aerial photos
has two advantages: low cost and high mobility. UAVs can enter the place where humans are not easy
to enter.
Based on the locomotion mechanisms, inspection robots can be classified as follows. It should be
noted that only recent developments are addressed and more examples can be found in Myung, et al.
(2014).
1) Wheeled robots moving on horizontal surfaces: Lim et al. (2011) proposed a mobile robot
equipped with a camera and a laser sensor for crack inspection of bridges. Lee and Mita
(2012) proposed a mobile robot with accelerometers for evaluating structural conditions and a
laser range finder (LRF) for simultaneous localization and mapping (SLAM).
2) Wall-climbing / Crawling robots: For movement on vertical walls or uneven surfaces, geckolike
locomotion using directional adhesion, a vacuum-creating suction adhesion, and a
climbing mechanism using adhesive materials have been developed. Teleoperated climbing
inspection robots have been developed by Bao et al. (2012) and a bio-inspired miniature wallclimbing
caterpillar robot has been developed by Zhang et al. (2010). A micro aerial vehicle
type wall-climbing robot was also developed (Shin, et al. 2013), which used thrust forces
induced by the four rotors not only to fly but also to stick on the wall.
3) Snake-like robots: These robots are suitable for inspecting narrow spaces and rough terrain
that are hard to navigate. Tokyo Tech’s Hirose-Fukushima Lab has developed softly moving
mechanical snakes (Mori and Hirose 2006).
4) Modular robots: It is possible to build a robot capable of going through rough terrain and
uneven surfaces by cascading multiple small modular robots. A camera-installed cable
inspection robot composed of two modules has been developed by Xu et al. (2011). The
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proposed robot stably climbs random inclined cables and lands smoothly by using a gas
damper with a slider-crank mechanism.
5) Aerial vehicles: Robots in the form of quadrotors, helicopters, and airships can freely fly
through and inspect structures. Quadrotors and miniature helicopters have been extensively
tested. Most of the current prototypes have limited payload capacity and thus cannot carry
many sensors (Pratt, et al. 2008, Shin, et al. 2013).
6) Underwater vehicles: Submersible robots have been developed to inspect underwater
structures. Several prototypes have been designed to inspect oil storage tanks (Abdulla, et al.
2010) and pipelines (Conte, et al. 1996, Bodenmann, et al. 2009).
Various sensors, such as optical sensors, lasers, and their combinations have been incorporated for
robotic structural inspection purposes (Myung, et al. 2011, Jeon, et al. 2011, Jeon, et al. 2012).
3. Summary of this special issue
The following papers included in this special issue on “Robotics and Automation for Civil
Infrastructure” properly address these current topics of interest and are briefly summarized here.
3.1 Structural health monitoring using robotic technologies
Adhikari et al., in their paper, “Automated condition assessment of concrete bridges with digital
imaging”, present an approach of automating the prediction of condition rating for bridges based on
digital image analysis. The proposed methodology encompasses image acquisition, development of 3D
visualization model, image processing, and condition rating model. Under this method, scaling defect
in concrete bridge components is considered as a candidate defect and the guidelines in the Ontario
Structure Inspection Manual (OSIM) have been adopted for developing and testing the proposed
method. The automated algorithms for scaling depth prediction and mapping of condition ratings are
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based on training of back propagation neural networks.
Jeon et al., in their paper, “Vision-based remote 6-DOF structural displacement monitoring system
using a unique marker,” propose a vision-based displacement measurement system with a uniquely
designed marker to monitor the 6-DOF structural displacement with high accuracy. The system is
composed of a uniquely designed marker and a camera with a zooming capability, and relative
translational and rotational displacement between the marker and the camera is estimated by finding a
homography transformation. The novel marker is designed to make the system robust to measurement
noise based on a sensitivity analysis of the conventional marker and it has been verified through
simulations and experiments.
3.2 Sensing technology and multi-sensor fusion for civil infrastructure
Wang et al., in their paper, “Fatigue performance monitoring of full-scale PPC beams by using the
FBG sensors,” conducted an experimental study of fatigue performance monitoring of partially
prestressed concrete (PPC) structure by using Fiber Bragg Grating (FBG) sensors. The strain is
determined by measuring the wavelength shift and the temperature shift. The experiments are
conducted on full-scale post-tensioned PPC beams under fatigue loading. The performance of both the
FBG and resistance strain gauge (RSG) sensors is evaluated and compared. The experimental results
show that FBG sensors have relatively good resistance to fatigue loading compared with RSG sensors,
and hence are suitable for long-term health monitoring of the tensile reinforcement in PPC structures.
Gomathi et al., in their paper, “Hand arm vibration measurement using micro-accelerometer in
different brick structures,” present a MEMS capacitive area changing micro-accelerometer for safety
monitoring of construction workers operating power tools. The accelerometer measures the hand arm
vibrations experienced by workers and warn them when breaching the nominal values specified by
ISO safety standards. The MEMS accelerometer exhibits superior performance in the low frequency
range combined with linearity, low noise performance and high sensitivity. Using different types of
blocks as operating subject, comparative experiments are carried out with a conventional
accelerometer to study the advantages and disadvantages of the MEMS accelerometer.
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Bai et al., in their paper, “Crack location in beams by data fusion of fractal dimension features of
laser-measured operating deflection shapes,” propose a method for beam crack localization using data
fusion of fractal dimension features of operating deflection shapes (ODSs). Data fusion of Katz’s
fractal dimension (KFD) curves of various ODSs is used collectively to create a fractal damage feature,
i.e. the overall KFD curve. The method is demonstrated in numerical and experimental cases of
cantilever beams with single/multiple cracks. For the experimental case, a scanning laser vibrometer is
used to acquire high-resolution ODSs.
Luo et al., in their paper, “Development of a dynamic sensing system for civil revolving structures
and its field tests in a large revolving auditorium,” discuss a wireless dynamic sensing system for a
large revolving structure (RS). Revolving structures are a unique structural form which is seen in
innovative architecture design. Such structures revolve around themselves or along a certain track.
Safety design and health monitoring of RS are critical, but have not attracted much attention from
researchers. The proposed system consists of one sink and multiple vibrating wire sensors (VWS)
nodes for strain measurement. It dynamically assigns the addresses of sensor nodes when RS position
changes. The proposed method is validated with field tests on a rotating auditorium. The work
demonstrates effective implementation of WSN for civil structures.
Chiu et al., in their paper, “Integration of in-situ load experiments and numerical modeling in a
long-term bridge monitoring system on a newly-constructed widened section of freeway in Taiwan,”
propose a real-time bridge monitoring system. The research considered several results of the bridge
experiment and established the structural models of the bridge. The research provides the simulation
approach for real bridges and can be utilized for bridge management.
3.3 Robotic inspection for civil infrastructure
Lee et al., in their paper, “Two-module robotic pipe inspection system with EMATs,” introduce a
two-module robotic pipe inspection system with ultrasonic NDE device to evaluate the integrity of
pipe structures. The proposed robotic platform has high mobility. The two module mobile robot
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platform overcomes pipe obstacle structures such as elbow, or T-branch joints by cooperative
maneuvers. Also, it can climb up the straight pipeline at a fast speed due to the wheel driven
mechanism. For inspection of pipe structures, SH-waves generated by EMAT are applied with
additional signal processing methods.
Liu et al., in their paper, “A review of rotorcraft Unmanned Aerial Vehicle (UAV) developments
and applications in civil engineering,” extensively overview the unmanned aerial vehicle applied to
civil structures including its hardware (frame structure, electromechanics, flight controller and
telemetry control system), software and related technologies (control, power management, navigation
and image processing). Their paper also analyzes the opportunities and challenges of applying UAV in
civil engineering applications including seismic risk assessment, transportation, disaster response,
construction management, surveying and mapping, and flood monitoring and assessment.
4. Conclusions
Although sensor technologies have revolutionized over the past few decades, critical challenges
for civil infrastructure applications still exist. Besides measurement accuracy, the multi-faceted issues
includes sensor miniaturization, low cost, support of wireless communication (or even further –
mobility for mobile sensors), sensor smartness (with embedded computing), long-term powering,
durability, etc. Nevertheless, it is expected that in the coming decades, civil infrastructure systems will
continue benefiting from rapidly evolving sensing technologies. On the other hand, for civil
engineering researchers, sensor data inundation is becoming more and more of a reality. There is a
growing demand for data fusion techniques that are able to scour through megabytes and gigabytes of
data, collected by tens or hundreds of various types of sensors, and convert the large volume of data
into information that is meaningful and actionable for infrastructure stakeholders. There will be
increasing integration of sensor technologies, mobile robots, wireless communication for resource
monitoring, management, and automated construction. History will see that advances in both sensor
technologies and sensor data processing are among the most remarkable revolutions for civil
engineering of our era.
Furthermore, using robotics to assist civil infrastructure inspection procedures can save significant
time, labor, and money. Technological advances are providing unprecedented opportunities of
adopting robots for SHM and inspection applications. Carrying a variety of sensors, the robots with
unique locomotion mechanisms can provide autonomous mobility while inspecting structures.
Examples for different locomotion mechanism include wheeled robots moving on horizontal surfaces,
wall-climbing and crawling robots, snake-like robots, modular robots, aerial vehicles, underwater
vehicles, etc. Inspection robots have been applied to various target structures such as aircrafts, bridges,
power lines, pipelines, ducts, tubes, sewages, etc. Although robots specialized for SHM applications
have only recently been developed, their use is expected to largely increase in the near future.