A comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research trends

Abstract

Rapid development in structural health monitoring systems has led to the invention of various sensing technologies. Nonetheless, difficulties in deploying and maintaining traditional wired sensors and managing vast amount of data collated from a dense array of wired sensors were fundamental drawbacks of using such systems. Wireless sensor networks (WSNs) were thus introduced to overcome the noted shortcomings. However, the energy required to power WSNs has become an important concern due to battery limitations. Energy harvesting technologies have been developed to extend the lifetime of WSNs by addressing the energy constraint problem. Recently, a new generation of WSNs based on self-powered sensors have become a reality by bridging the gap between the harvested energy and the energy required for sensing, computation, storage, and communication. Self-powered sensors are increasingly being used and establishing themselves as promising solutions to conventional WSNs in civil infrastructure. This review paper summarizes the applications of self-powered sensors in civil infrastructure during the last decade. First, a general introduction to self-powered sensing and its significance in civil engineering are presented. Thereafter, various self-powered sensors currently used in civil engineering arena are reviewed. Finally, the advantages of deploying these sensors are presented, and future research trends for their innovative use are highlighted.

Full text

This is a preprint draft. The published article can be found at: https://doi.org/10.1016/j.engstruct.2021.111963
Please cite this paper as: Salehi, H., Burgueño, R., Chakrabartty, S., Lajnef, N. and Alavi, A.H., 2021. A
comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research
trends. Engineering Structures. https://doi.org/10.1016/j.engstruct.2021.111963
1
A Comprehensive Review of Self-Powered Sensors in Civil
Infrastructure: State-of-the-art and Future Research Trends
Hadi Salehia,e
1
, Rigoberto Burgueñob,c, Shantanu Chakrabarttyd, Nizar Lajnef e, Amir H. Alavi f
a Department of Aerospace Eng, University of Michigan, Ann Arbor, Michigan, USA
b Department of Civil Eng, Stony Brook University, Stony Brook, New York, USA
c Department of Mechanical Eng, Stony Brook University, Stony Brook, New York, USA
d Department of Electrical and Systems Eng, Washington University in St. Louis, St. Louis, Missouri, USA
e Department of Civil and Environmental Eng, Michigan State University, East Lansing, Michigan, USA
f Department of Civil and Environmental Eng, University of Pittsburgh, Pittsburgh, Philadelphia, USA
Abstract
Rapid development in structural health monitoring systems has led to the invention of various
sensing technologies. Nonetheless, difficulties in deploying and maintaining traditional wired
sensors and managing vast amount of data collated from a dense array of wired sensors were
fundamental drawbacks of using such systems. Wireless sensor networks (WSNs) were thus
introduced to overcome the noted shortcomings. However, the energy required to power WSNs
has become an important concern due to battery limitations. Energy harvesting technologies have
been developed to extend the lifetime of WSNs by addressing the energy constraint problem.
Recently, a new generation of WSNs based on self-powered sensors have become a reality by
bridging the gap between the harvested energy and the energy required for sensing, computation,
storage, and communication. Self-powered sensors are increasingly being used and establishing
themselves as promising solutions to conventional WSNs in civil infrastructures. This review
paper summarizes the applications of self-powered sensors in civil infrastructures during the last
decade. First, a general introduction to self-powered sensing and its significance in civil
engineering are presented. Thereafter, various self-powered sensors currently used in civil
engineering arena are reviewed. Finally, the advantages of deploying these sensors are presented,
and future research trends for their innovative use are highlighted.
1
H. Salehi, Email: hsalehi@umich.edu

This is a preprint draft. The published article can be found at: https://doi.org/10.1016/j.engstruct.2021.111963
Please cite this paper as: Salehi, H., Burgueño, R., Chakrabartty, S., Lajnef, N. and Alavi, A.H., 2021. A
comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research
trends. Engineering Structures. https://doi.org/10.1016/j.engstruct.2021.111963
2
Keywords: Self-powered sensors, Wireless sensor networks, Energy harvesting, Civil engineering,
Structural engineering, Civil infrastructure, Structural health monitoring, Smart cities
1. INTRODUCTION
Civil engineering structures/infrastructure, such as bridges, buildings, dams, and others, are key
elements to ensure the safe and efficient function of our society. The intricacy of civil infrastructure
projects, by themselves and in the network that they are part of, along with economic constrains
make their maintenance a high priority. However, the same complexity and budget confines
increases their risk to the effect of extreme events, natural hazards, and degradation with age.
Public demand for effective monitoring and assessment of civil engineering structures that lead to
improved public safety and minimize the cost of repair have thus been notably increased. To this
end significant attention has been devoted to the development of novel sensing technologies for
identifying the onset of damage in civil infrastructures [1,2].
Structural health monitoring (SHM) is emerging as a multidisciplinary technology solution for
long-term condition and damage assessment of civil, mechanical, and aerospace structures owing
to rapid advancements of sensing technologies [3–10]. SHM refers to the monitoring of a structure
using data collected from sensors, extracting damage sensitive features, and analyzing them for
condition assessment of the monitored structure. The information collected by an SHM system can
be utilized to determine structural integrity in real-time, in continuous time/delayed manner, or
after a critical event. Further, given that several potential structural problems (e.g., buckling,
fracture, etc.) happen fairly locally and very quickly, a dense array of sensors is typically required
to capture variations in structural response resulting from damage. Nevertheless, realizations of
wired systems are restricted by high cost. Further, challenges in deploying and maintaining
traditional wired sensors and managing the massive amount of data collected from a dense array
of wired sensors are major shortcomings of using such systems. Wireless sensor networks (WSNs)
have thus been introduced to overcome the noted drawbacks due to their low-cost attributes [1–
3,11–14]. WSNs are widely being utilized as alternatives in conventional structural monitoring
systems, with the aim of advancing the state-of-practice in SHM and condition assessment of civil
infrastructure.

This is a preprint draft. The published article can be found at: https://doi.org/10.1016/j.engstruct.2021.111963
Please cite this paper as: Salehi, H., Burgueño, R., Chakrabartty, S., Lajnef, N. and Alavi, A.H., 2021. A
comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research
trends. Engineering Structures. https://doi.org/10.1016/j.engstruct.2021.111963
3
Although use of WSNs has reduced wiring costs, these systems suffer from their own
shortcomings, such as those associated with time synchronization and reliability [15]. More
importantly, WSNs have a fundamental limitation in terms of energy. Due to the limited capacity
of non-rechargeable batteries for WSNs, the energy demanded for sensing data is an important
concern. Energy harvesting, which refers to the conversion of mechanical energy to electrical
energy, has been introduced to tackle this issue [16–19]. Energy harvesting technologies have
attracted considerable attention to extend the lifetime of WSNs, with the goal of addressing the
energy constraint problem.
Self-powered sensors have been recently developed by overcoming the gap between the achievable
harvested energy and the energy demanded for sensing, computing, storage, and communication,
leading to the advancement of energy-efficient WSNs. Self-powered sensors are capable of
efficiently harvesting the needed power from the signal being sensed as well as from other energy
sources (e.g., ambient vibrations, solar energy, etc.), thereby enabling reliable and efficient
structural/infrastructural health monitoring and condition assessment.
The civil engineering community has witnessed an increasing interest in the use of self-powered
sensors for various applications. This survey paper focuses on self-powered sensors that have
attracted considerable attention over the last decade, including piezoelectric self-powered sensors,
self-powered strain sensors based on self-sensing composites, and triboelectric self-powered
sensors. The emphasis is placed on piezoelectric self-powered sensors since they are increasingly
being employed and establishing themselves as promising solutions to traditional wired sensors
for a variety of civil engineering applications. The objective of the review is to summarize the
background of the aforementioned self-powered sensors, discuss the research developments
concerning their use in civil engineering, and feature promising research trends.
The use of wireless sensors in civil engineering has been previously reviewed. Lynch et al. [1]
presented a comprehensive survey on the use of WSNs for SHM and their importance in the field.
Miller et al. [20] studied WSNs for SHM applications; particularly, a solar harvesting energy
technology with solar panels and rechargeable batteries was presented. A study for development
of sensing systems that can be embedded within a monitored structure was carried out by Farinholt
et al. [21], where the required power was harvested from the ambient environment using

This is a preprint draft. The published article can be found at: https://doi.org/10.1016/j.engstruct.2021.111963
Please cite this paper as: Salehi, H., Burgueño, R., Chakrabartty, S., Lajnef, N. and Alavi, A.H., 2021. A
comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research
trends. Engineering Structures. https://doi.org/10.1016/j.engstruct.2021.111963
4
piezoelectric and thermoelectric transducers. An extensive study of smart sensing systems for
damage detection and condition assessment was conducted by Yun and Min [2]. A survey on
WSNs for bridge health monitoring was carried out by Zhou and Yi [22]. Godinez-Azcuaga et al.
[23] studied applications of self-powered WSNs for the prognosis of steel and concrete bridges.
Lee et al. [24] carried out a review on the progress of ultralow power circuit design for WSNs for
SHM, while a survey on energy-efficient deployment strategies in SHM with WSNs was
conducted by Fu et al. [25]. A comprehensive review on the use of wireless smart sensors for
multi-scale monitoring and control of civil infrastructures was performed by Spencer et al. [26].
Recently, a survey on applications of smart sensing systems was conducted by Cha et al. [27], and
a literature review of advanced sensors for SHM of civil engineering structures was carried out by
Das and Saha [28].
Although the aforementioned review papers showcased applications of smart sensing systems in
civil engineering, they primarily focused on WSNs and did not cover the emerging concept of self-
powered sensing studied in this survey. In addition, survey papers on piezoelectric sensors merely
highlighted the application of this type of self-powered sensors in SHM and did not review their
utilization for other civil engineering applications. Nonetheless, there has been significant progress
in the technology development as well as increased use of self-powered sensors in diverse civil
engineering applications over the last few years. Hence, this paper presents a comprehensive
review of research efforts on the utilization of self-powered sensors in a variety of civil engineering
applications (e.g., SHM, transportation infrastructure, structural vibration control) over the last
decade and it identifies future research avenues and emerging trends for use of these sensors in
civil infrastructure.
The review paper is structured as follows. The research method used for performing the content
analysis to select the reviewed literature is outlined in Section 2. An overview of self-powered
sensors being used in the civil engineering domain is presented in Section 3. Section 4 summarizes
applications of self-powered sensors in civil engineering over the last decade. Section 5 discusses
future research directions and potential use of self-powered sensors in novel civil engineering
applications, while presenting enabling technologies and methods for enhancing use of these
sensors in the field. Finally, concluding remarks are provided in Section 6.

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2. RESEARCH METHOD FOR SELECTING THE LITERATURE
In this survey, the reviewed literature was chosen using content analysis [30]. Content analysis is
generally used for making rational inferences based on collected data, with the aim of disclosing
central aspects of prior studies. Such analysis enables qualitative and quantitative operations. As a
result, content analysis is capable of providing an inclusive disclosure of self-powered sensors
applications in civil engineering, thus leading to reliable and valid results from this study. Sample
collection was carried out through the search and selection of peer-reviewed articles collected from
well-accepted academic databases. The procedure for literature search and selection is illustrated
in Figure 1 and is summarized as follows:
• For article search and selection, the academic databases Web of Science, Science Direct,
Scopus, Wiley Online Library, ASCE Library, Engineering Village, Sage, Emerald, etc.
were used.
• Keywords such as “self-powered sensors”, “self-powered sensors in civil and structural
engineering”, “wireless networks in civil engineering”, “wireless sensor networks in
structural engineering”, “energy harvesting”, and “energy harvesting civil engineering”
were used to search the noted databases. This procedure led to the identification of
academic articles regarding the application of self-powered sensors in civil engineering.
Considering the time period under review, from 2010 to 2020, approximately 170
candidate articles were identified.
• To select the final candidates/articles, a two-round article selection technique was used.
In the first round, titles, abstract, and keywords of the noted articles were checked to
determine if they were within the scope of this survey. In the second round, the entire
article was analyzed to ensure that all of the selected articles were closely related to the
review objective. Finally, 127 articles were chosen and used for the present review.

This is a preprint draft. The published article can be found at: https://doi.org/10.1016/j.engstruct.2021.111963
Please cite this paper as: Salehi, H., Burgueño, R., Chakrabartty, S., Lajnef, N. and Alavi, A.H., 2021. A
comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research
trends. Engineering Structures. https://doi.org/10.1016/j.engstruct.2021.111963
6
Figure 1. Schematic of a research method for selecting literature
For the review, both qualitative and quantitative analyses were performed to identify various self-
powered sensing technologies being used in the civil engineering domain and their applications in
the field. As a result, the most novel applications of self-powered sensors and future research
avenues were identified and chosen for presentation in this survey paper.
3. OVERVIEW OF SELF-POWERED SENSORS IN CIVIL ENGINEERING
As previously noted, energy harvesting technology, i.e., that for the conversion of mechanical
energy into electrical energy, was introduced to extend the lifetime of WSNs by addressing the
energy constraint problem of WSNs. In the context of sensing and monitoring, the most common
sources of energy are ambient mechanical vibration, solar energy, and wind. Numerous self-
powered sensing technologies employing diverse energy harvesting methods have been developed
for use in civil engineering. This section provides a summary on the most commonly used self-
powered sensors for civil engineering applications over the last decade.
3.1. Piezoelectric Self-powered Sensors
Energy harvesting sensors based on ambient vibrations are commonly categorized into three
classes according to their powering mechanisms, see Figure 2. The first class of sensors utilizes an
external on-board energy device, where the sensor operates by harvesting energy from ambient
sources or a remote source of power. For the second class, a passive powering scheme is used. The
sensor is powered once an interrogation signal is present, which eliminates the need for an on-
board energy storage. The third class is based on self-powering, where the sensor directly
Article
Search
Academic
Databases
Keywords
Article
Selection
Two-round
Selection Technique

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Please cite this paper as: Salehi, H., Burgueño, R., Chakrabartty, S., Lajnef, N. and Alavi, A.H., 2021. A
comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research
trends. Engineering Structures. https://doi.org/10.1016/j.engstruct.2021.111963
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scavenges the sensing, computational, storage, and communication power from the signal being
sensed [31]. Self-powered sensing has been identified as one of the key solutions towards
achieving autonomy in long-term monitoring, which enables the sensors to continuously monitor
the signals without relying on an auxiliary energy source. Three main approaches have been
studied to convert structural motion to electrical energy for self-powered sensing: electromagnetic,
electrostatic, and piezoelectric harvesters. Piezoelectric harvesters have been found to be the most
efficient one due to their large power density and ease of application. Using a piezoelectric
element, strain variations can be sensed, while the sensing signal can be used to empower the
storage and computational functions.
Figure 2. Illustration of three classes of energy scavenging sensors with: (a) Re-chargeable energy storage, (b)
Passive powering, and (c) Self-powering [31]
Piezoelectric self-powered sensors have been widely utilized for a variety of civil engineering
applications due to their ability to transform mechanical strain and vibration energy to electrical
energy [32,33]. This enables implementing sustainable energy through self-sustained
multifunctional sensing and energy harvesting for civil engineering applications. Two commonly
used piezoelectric material classes are a ceramic composed of lead zirconate titanate (PZT) and
semi-crystalline plastic polyvinylidene fluoride (PVDF) [31,34,35]. Huang et al. [34] introduced
a piezo-floating-gate (PFG) self-powered mechanical strain monitoring sensor based on a
piezoelectricity driven impact-ionized hot electron injection principle, where the floating-gate is
used as non-volatile memory. This sensing technology combines the physics of piezoelectric
power harvesting and hot-electron injection for sensing, computation, and storage of mechanical
usage statistics. The self-powered sensor harvests energy from micro-strain variations and is able

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comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research
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to exploit computational primitives inherent in the interface physics among floating-gate
transistors and piezoelectric transducers. For the PFG sensor, piezoelectric materials could
generate large voltages signals (
>10𝑉
), while they exhibited limited current driving capability
(
< 1𝜇𝐴
). This made the transducer an ideal choice to operate floating-gate-injectors. A calibration
algorithm was also developed to design self-powered analog processor. The self-powered sensor
was capable of recording different levels/rates of the strain signal using programmable thresholds
with multiple channels of data logging. Figure 3 illustrates the principle of the PFG sensor, system
diagram, and an energy source for powering the functionality of the sensor.
Figure 3. The PFG self-powered sensor: (a) Underlying physics, (b) System diagram, and (c) An energy source to
power the functionality of the sensor [35]
As noted, the PFG sensor allows continuous capturing and storing of local and cumulative
information from dynamic loading conditions of the host structures in a non-volatile memory. The
fabricated prototype shown in Figure 4 (a) was connected to a piezoelectric transducer, which was
used for sensing strain variations inside a mechanical structure, as well as self-powering of the
processor. Functionality of the integrated PFG self-powered sensor (i.e., piezoelectric transducer
and analog processor) was demonstrated via experimental tests with a mechanical testing system
as depicted in Figure 4 (b).

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Figure 4. (a) The packaged prototype of the level-crossing processing, (b) Experimental setup used for evaluation of
the analog processor integrated with a PVDF transducer [34]
3.2. Self-powered Strain Sensors based on Self-sensing Composites
Multifunctional thin films made with poly(3-hexythiophene) (P3HT) have been used for
monitoring corrosion and cracks without the need for an electric energy supply [36,37]. The P3HT-
based thin films are able to generate direct current, which varies its magnitude with the applied
tensile strain, when subjected to tensile test. The drawback of the P3HT-based thin films is that
their use is limited when light is not available. To tackle this issue, the P3HT-based thin films are
integrated with mechano-luminescent (ML) materials that are able to generate light under
mechanical excitation [38]. There is a growing interest in the use of ML sensors since they operate
without external energy and can be simply incorporated into design [39]. Pulliam et al. [38,40]
designed a mechano-luminescent-optoelectronic composite self-powered strain sensor based on
the integration of ML copper-doped zinc sulfide (ZnS:Cu) and mechano-optoelectronic (MO)
P3HT to measure strain with no external energy supply. For the self-powered sensor,
ZnS:Cu/polydimethylsiloxane (PDMS) elastomeric composites were fabricated by embedding
ZnS:Cu in an elastomeric PDMS matrix. The P3HT-based thin films were fabricated on a PDMS
substrate. The photonic energy/light required for P3HT-based strain sensing thin films was
converted from mechanical energy by ZnS:Cu/PDMS. Also, direct current was generated using
the P3HT-based thin films by absorbing photonic energy released from the ZnS:Cu/PDMS. The
P3TH-based thin films fabricated on PDMS and dog-bone shape ZnS:Cu/PDMS that were used
for the experiments are shown in Figure 5. The authors showed the applicability of the proposed
self-powered strain sensor for corrosion monitoring by measuring direct current voltage in the
vicinity of the ZnS:Cu/PDMS subjected to tensile loading.

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comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research
trends. Engineering Structures. https://doi.org/10.1016/j.engstruct.2021.111963
10
Figure 5. (a) ZnS:Cu/polydimethylsiloxane (PDMS), (b) Three-dimensional printed mold designed for fabrication
of dog-bone shape ZnS:Cu/PDMS specimen [38]
3.3. Triboelectric Self-Powered Sensors
Triboelectric sensing technology has recently gained remarkable attention as an efficient and
sustainable technology [41,42] for civil engineering applications. Garcia et al. [41] presented a
self-powered triboelectric sensor using PVDF and polyvinyl pyrrolidone (PVP) nanofibers for
energy detection in composite structures. PVDF nanofibers attract electrons from other materials,
while PVP nanofibers donated electrons. The rough and porous surfaces of the nanofibers resulted
in extending the contact area among frictional materials. Further, the nanofibers layers were
prepared using electrospinning. For the structural design of the triboelectric sensor, two layers of
polymer nanofibers and copper electrodes were considered. The assembly process of the
triboelectric sensor illustrated in Figure 6 consisted of four steps: (a) placing PVP nanofibers on
copper foil, (b) depositing a PVDF nanofiber on copper foil, (c) stacking the top and bottom part
of the sensor, and (d) sealing the sensor with polyethylene terephthalate film to prevent variations
in the sensor electric responses due to environmental changes. This study revealed the applicability
of triboelectric self-powered sensors in detecting impacts in composites structures.
Figure 6. Structure and fabrication process of the triboelectric self-powered sensor [41]

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Please cite this paper as: Salehi, H., Burgueño, R., Chakrabartty, S., Lajnef, N. and Alavi, A.H., 2021. A
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11
4. APPLICATIONS
Applications of the self-powered sensors highlighted in previous section in civil engineering over
the last decade are summarized herein. The number of research publications showcasing the use
of self-powered sensors in the field is presented in Figure 7, from which it can be seen that the
utilization of these sensors for structural health monitoring (SHM) and transportation
infrastructures has drawn the most attention from researchers. It is noted that the reduction in the
number of papers published on self-powered sensors in SHM and transportation infrastructures
from 2017-2018 to 2019-2020 does not indicate a decreased research interest. Such reduction is
due to the fact that year 2020 is ongoing and therefore, not all the publications in this time interval
are reported in this paper. Further, as can be observed from Figure 7, the significant increase in
publications showcasing the use of self-powered sensors in SHM and condition assessment is
evident over the last decade. Table 1 presents a listing of works concerning the applications of
self-powered sensing in civil engineering over the last decade. The publications are ordered based
on the applications/domains. As can be observed, piezoelectric self-powered sensors have been
widely used for diverse civil engineering applications (e.g., SHM, transportation monitoring,
structural vibration monitoring, etc.) as compared to self-powered strain sensors based on self-
sensing composites and triboelectric sensors. Detailed information on each individual
application/domain with representative studies/examples are provided in the following sub-
sections.

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Please cite this paper as: Salehi, H., Burgueño, R., Chakrabartty, S., Lajnef, N. and Alavi, A.H., 2021. A
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Figure 7. Publications on the utilization of self-powered sensors in civil engineering over the last decade.
Table 1. Applications of self-powered sensors in civil engineering
Self-powered Sensor
Domain
Reference
Piezoelectric-floating-gate sensor
Infrastructure health monitoring
[43]
Mechano-luminescent-
optoelectronic composites sensor
Structural health monitoring
[38,40,44]
Piezoelectric-floating-gate sensor
Structural health monitoring
[45–52]
Sensor with parametric frequency
increase energy harvesting
Structural health monitoring of bridges
[53]
Piezoelectric sensor
Structural health monitoring
[54]
Mechano-luminescent-
optoelectronic composites sensor
Structural health monitoring
[55]
Piezoelectric-floating-gate sensor
Structural health monitoring of bridges
[56,57]
Piezoelectric sensor
Structural health monitoring
[58,59]
Wireless sensor with solar energy
harvesting
Smart cities
[60]
Population
20
10
Structural Health
Monitoring
Transportation
Infrastructures
Corrosion
Prediction
Structural
Vibration Control
2009-2010
2011-2012
2013-2014
2015-2016
2017-2018
2019-2020

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13
Self-powered Sensor
Domain
Reference
Piezoelectric-floating-gate sensor
Infrastructural IoT for Smart Cities
[35]
Piezoelectric sensor
Structural health monitoring
[61]
Sensor with solar energy harvesting
Smart cities
[62–64]
Nanogenerator sensor
Transportation monitoring
[65]
Piezoelectric-floating-gate sensor
Pavement fatigue monitoring
[66]
Piezoelectric sensor
Transportation monitoring
[67]
Sensor with piezoceramic energy
harvesting
Intelligent transportation systems
[68]
ZigBee sensor with vibration energy
harvesting
Railway condition monitoring
[69]
Piezoelectric sensor
Pavement monitoring
[70]
ZigBee sensor
Real-time traffic monitoring
[71]
Piezoelectric-floating-gate sensor
Damage detection in asphalt concrete
pavements
[72–74]
Sensor with electromagnetic energy
harvesting
Vibration monitoring of stay cables
[75]
Sensor with electromagnetic energy
harvesting
Structural vibration control using
dampers
[76–80]
Sensor with pick-up coil energy
harvesting
Structural vibration control with
displacement sensor
[81]
Sensor with electromagnetic energy
harvesting
Structural vibration monitoring
[82,83]
Piezoelectric sensor
Structural vibration monitoring
[84]
Piezoelectric sensor
Vibration monitoring during earthquakes
[85]
Piezoelectric sensor
Vibration monitoring
[86]
Sensor with electrochemical noise
energy harvesting
Corrosion monitoring of steel
reinforcement in concrete
[87]
Sensor with corrosion energy
harvesting
Corrosion monitoring of reinforced
concrete structures
[88]
Piezoelectric sensor
Automated corrosion prediction of steel
reinforcement
[89]
Triboelectric sensor
Detection of energy impacts in composite
structures
[41]
Piezoelectric-floating-gate sensor
Monitoring of quasi-static structural
response
[90,91]

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Please cite this paper as: Salehi, H., Burgueño, R., Chakrabartty, S., Lajnef, N. and Alavi, A.H., 2021. A
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14
4.1. Structural Health Monitoring and Condition Assessment
Among the diverse avenues for the use of self-powered sensors in civil engineering arena, they
have mainly been used for SHM and damage identification. Due to their ability to harvest the
required energy (for computational, storage, and transmission) from the signal being sensed as
well as from different energy sources (e.g., ambient vibrations, solar, etc.) self-powered sensors
have been establishing themselves as a promising alternative to conventional sensors for SHM
applications. Zhou et al. [54] proposed a self-powered wireless sensor node for structural damage
identification. The sensor node employed piezoelectric PZT (Lead Zirconate Titanate) wafers for
energy harvesting (see Figure 8 (a)). The feasibility of the self-powered sensing system for SHM
was shown through experimental tests using a cantilevered bimorph generator with a tip mass, as
illustrated in Figure 8 (b).
Figure 8. (a) Self-powered sensor , (b) Experimental set up with a cantilevered bimorph generator and piezoelectric
elements [54]
A self-powered embedded sensing system using Lamb waves was developed for SHM [58], for
which the energy harvesting module used piezoelectric elements was connected to the monitored
structure. An autonomous wireless transmitter (see Figure 9 (a)) and an autonomous wireless
receiver (see Figure 9 (b)) were used for generating Lamb waves and sending the result to the data
logger, respectively. Results from experimental test shown in Figure 9 (c) indicated the
applicability of the proposed sensing system for structural damage detection.

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Figure 9. (a) Autonomous wireless transmitter, (b) Autonomous wireless receiver, (c) Experimental test setup [58]
As previously highlighted, the PFG sensor shown in Figure 10 (a) is able to operate battery-free
using energy harvested from a structure’s vibration and capture historical statistics associated with
the strains experienced by the structure. This makes this sensor a suitable choice for structural
monitoring of large-scale bridges [35,56,57]. Laboratory tests were conducted on the PFG sensor
to characterize its performance before its deployment in real-world applications [57]. The
experimental setup and a close-up of the affixed transducers are illustrated in Figure 10 (b) and
Figure 10 (c).
Figure 10. (a) PFG self-powered sensor, (b) Experimental lab setup, (c) Close-up of the affixed transducers [57]

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For large-scale testing, the anticipated strain response levels for the areas on the bridge to be
monitored was estimated using numerical simulations. To establish a quasi-self-powered platform
for bridge monitoring, the PFG sensor was coupled to an active Radio frequency (RF)
communication link. Deployment of the PFG sensor was done in two phases [35]. In first phase,
an initial prototype was deployed on the Mackinac Bridge in Michigan to evaluate the procedures
for development of a quasi-self-powered platform, including a rough indication of the
environmental conditions that sensor would be exposed to during its deployment lifetime. The
initial prototype consisted of three PFG sensors connected to an off-the-shelf RF microcontroller,
where the microcontroller was used to collect data from the sensors and wirelessly transmit it back
to a moving vehicle on the bridge. An initial prototype of the sensor and the first installation on
the bridge are depicted in Figure 11 (a) and Figure 11 (b), respectively.
Figure 11. (a) First version of the PFG prototype, (b) First field installation of a long-range sensor box [35]
Findings from the first deployment indicated the suitability of the sensor for large-scale condition
assessment, but it also revealed the issues may arise during long-term monitoring. To overcome
this, an improved version of the PFG sensor, shown in Figure 12 (a), was used for the second
deployment on the Mackinac Bridge [57]. Figure 12 (b)) depicts a mock-up attachment of the
second version of the PFG sensors to steel plates for field deployment. Results showed that the
proposed PFG sensing system could be effectively used for a quasi-self-powered SHM platform.
Further, it was demonstrated that by optimizing the system parameters the sensor is expected to
continuously operate for longer time (e.g., for more than twenty years).

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Figure 12. (a) Final version of the PFG prototype, (b) Second field deployment of the sensor [57]
4.2. Transportation Infrastructures
Significant effort has been devoted to the utilization of self-powered sensing for transportation
infrastructure [92]. An autonomous self-powered sensing approach was developed for intelligent
transportation monitoring [71]. The proposed system composed of Internet of Things (IoT) sensor
nodes, an intelligent access point, and IoT cloud server enabled long-term autonomous operation.
In addition, a self-powered ZigBee sensor node was introduced and used for railway condition
monitoring [69]. For the experiments, the sensors connected to the ZigBee end device were
empowered by the energy harvester (see Figure 13). The harvesters were subjected to the vibration
generated via the wheel set system. The feasibility of the self-powered sensing approach for
railway condition monitoring was evaluated through field tests.
Figure 13. (a) Self-powered sensor node prototype, (b) Field setup [69]
A self-powered intelligent monitoring system of road transportation was developed with the aim
of reducing the power consumption [67]. The system included a sensing node, a gateway node,
and a cloud platform. Solar energy and piezoelectric energy were used for the gateway and sensing
nodes, respectively. As part of the self-powered sensing system, a motherboard including

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piezoelectric transducer was used, while an acceleration sensing node was characterized with self-
powered supply. It was shown that the proposed sensing system could decrease the communication
and energy consumption required for data processing. Li et al. [68] introduced a self-powered
sensing mechanism for decision making of intelligent transportation systems. The authors used
piezoceramics as a power supply to convert vibration energy to voltage output. Accordingly, the
data collected from the self-powered sensing units were used to obtain traffic information. The
schematic of the preload measuring platform for cylindrical ceramics with experimental
connections is shown in Figure 14. The applicability of the sensing mechanism with cylindrical
piezoceramics was demonstrated for intelligent transportation monitoring.
Figure 14. Proposed self-sensing platform with experimental connections [68]
The PFG sensor previously discussed was also used for damage detection purposes in asphalt
concrete pavements [72,73]. Experimental tests were performed on a slab with hot mix asphalt to
evaluate the performance of the sensing system (see Figure 15 (a)). For the experiments, an H-
shape packaging, shown in Figure 15 (a), was used to protect the sensing system from damage
during manufacturing of the asphalt concrete specimen. The applicability of the PFG sensor for
pavement monitoring was shown in terms of detecting cracking in asphalt concrete pavements.
Figure 15. (a) Sensor packaging design for PVDF protection, (b) Experimental setup [73]

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4.3. Structural Vibration Control using Dampers
Self-powered sensing has been gaining attention in structural vibration control using
Magnetorheological (MR) dampers, which are type of semi-active control devices [76,80,81].
Power supply is essential to activate electromagnetic coils inside MR dampers in order to provide
the magnetic field. On the other hand, a sensor is required to measure the dynamic response of an
MR damper such as relative displacement or velocity across the damper [93–95]. Self-sensing
controllable dampers with energy harvesting ability have thus been introduced to supply the
electrical energy required by the damper from the mechanical energy, and they also address the
need for an external sensor to measure dynamic response [96–98]. A self-powered, self-sensing
MR damper was developed based on the integration of energy harvesting, sensing and controllable
damping technologies [76]. A prototype of the damper was fabricated and tested, where study on
power generation and velocity-sensing mechanism was conducted. It was shown that the proposed
self-powered and self-sensing damper could lead to energy savings and higher reliability. An MR
damper with an integrated self-powered displacement sensor was proposed for structural vibration
control applications [81]. The energy is harvested by the pick-up coil of the MR damper with an
integrated self-powered displacement sensor, see Figure 16 (a). The applicability of the MR
damper with self-powered sensing was evaluated through an experimental test as depicted in
Figure 16 (b). Results indicated that the self-powered damper can be effectively used for structural
vibration control applications due to its ability to harvest electrical energy from mechanical energy
and having self-sensing ability along with a large range of controllable damping force.

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Figure 16. (a) MR damper with an integrated self-powered displacement sensor, (b) Experimental setup for testing
the characteristics of the MR damper with self-powered sensor [81]
4.4. Corrosion Prediction in Reinforced Concrete Structures
Corrosion is one of the severe environmental issues that affects the functionality of aging
infrastructure. In particular, corrosion affects the service life of embedded steel reinforcement in
concrete members and hinders its wide application [87,89,99]. The corrosion of steel
reinforcement in concrete is an electromechanical process, which is essentially an energy release
process. Numerous studies have been conducted on corrosion detection and prediction using
WSNs. Yet, a battery-free sensor network is desired for effective corrosion prediction. Yu et al.
[87] developed a self-powered sensor for corrosion detection in reinforced concrete structures, for
which the electromechanical noise (EN) generated by corrosion was utilized as a sensing signal,
as well as a power supply. Electromechanical noise refers to the fluctuations of the potential current
during corrosion, which implies its electron transporting character. For the self-powered sensing
approach, each sensing unit collected power and EN. Once the energy reached the required level,
other sensing units were activated. It was shown that the self-powered sensing can be used for
corrosion monitoring in the SHM domain. A self-powered sensor network was proposed for
automated corrosion prediction of steel reinforcement [89]. The authors introduced a battery-free
corrosion sensor coupled with wireless modules, which included a gateway and base station, thus
enabling continuous real-time data collection. To minimize power consumption for data
communication, data transmission and routing were designed. For the purpose of energy

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harvesting, a piezoelectric material was placed on the surface of the steel reinforcement as shown
in Figure 17 (a). A voltage was induced due to deformation of the piezoelectric material as a result
of vibration of the steel reinforcement. The prototype self-powered sensing mechanism mounted
on a reinforced concrete deck is presented in Figure 17 (b). It was revealed that the proposed self-
powered sensing technology could monitor and predict the long-term corrosion of embedded steel
reinforcement, improving the service life of concrete infrastructures.
Figure 17. (a) Schematic of self-powered sensing approach, (b) Prototype of self-powered corrosion sensor installed
in reinforced concrete deck [89]
5. FUTURE DIRECTIONS AND RESEARCH TRENDS
Prior sections showcased the significance and effectiveness of self-powered sensors for civil
engineering applications, including SHM and condition assessment, transportation infrastructure,
corrosion prediction in reinforced concrete structures, etc. It is anticipated that utilization of self-
powered sensing will increase due to their unique abilities previously discussed. This section
provides some insight and thoughts on future avenues for self-powered sensors and their emerging
applications in civil and structural engineering, while highlighting enabling technologies that can
be employed to fully take advantage of self-powered sensors’ unique features.
5.1. Smart Cities
The concept of a smart city has been recently gaining significant research interest among various
engineering disciplines. The goal of a smart city is to deploy smart sensors within a city’s
infrastructure to improve sustainability, safety, and efficiency. In the context of a smart city, there
is a need to tackle important challenges, including energy, civil infrastructure, mobility, etc. These

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challenges can be addressed through the novel concept of the Internet of Things (IoT), which
serves as an essential feature of smart cities aimed in the sustainable development of such cities.
Several structural monitoring platforms used as part of a smart city depend on external energy
sources for powering sensing systems. Self-powered sensing, however, can be effectively
employed to address this drawback.
Self-powered sensors integrated with IoT can be considered as building blocks for future smart
cities, where sensors are capable of harvesting the required energy (for sensing, storage, and
communication) from the host structure’s environment (e.g., vibrations, deformations, temperature
changes, etc.). The PFG self-powered sensors highlighted in this survey could form
structural/infrastructural IoT platforms for smart cities, where the condition of the host
structures/infrastructures can be monitored using a large number of embedded/mounted sensors
[35,43,56]. Clearly, such structural/infrastructural IoT platforms could potentially lead to
significant savings, as well as self-charging and self-diagnosis of damage well in advance of the
occurrence of failures.
5.2. Data-driven Structural/ Infrastructural Health Monitoring
According to the 2017 Infrastructure Report Card from the American Society of Civil Engineers,
significant attention has been devoted to raise awareness on the need to maintain, retrofit, and
replace many bridges in the United States due to their poor condition. This follows from the fact
that the average age of the nation’s bridges is rising, with many bridges approaching the end of
their designed lifespan. Data-driven frameworks for infrastructure health monitoring, enabling
intelligent and informed decision making, can help mitigate the noted issues. Research and
demonstration projects have highlighted the applicability of the PFG sensing mechanism for use
in a scalable framework of structural/infrastructural health monitoring [43,56]. However,
additional research is needed to further improve the performance of the noted decision making
frameworks for long-term monitoring of large-scale civil structures/infrastructures.
Data-driven techniques for SSHM strongly rely on intelligent algorithms (e.g., artificial
intelligence methods, etc.) to analyze data collated from embedded (mounted) smart sensors. The
use of intelligent algorithms in data-driven SHM systems is increasing, indicating that these

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techniques are becoming predominant for SHM and condition assessment. Through the
incorporation of self-powered sensing and intelligent algorithms, efficient assessment and
inspection of civil structures/infrastructure becomes possible using data-driven frameworks that
are capable of performing the structural evaluation remotely. Due to advancement in self-powered
sensing technologies, the constraint of energy availability for a sensor network in data-driven SHM
frameworks can be overcome, thus enabling the long-term intelligent, reliable, and efficient
monitoring and condition assessment of civil structures/infrastructure.
5.3. Enabling Technologies/Methods to Enhance Utilization of Self-powered Sensors
Previous sub-sections highlighted potential future trends for self-powered sensors. Nonetheless, to
fully take advantage of the unique features offered by such sensors, several important aspects with
regards to their use in civil engineering have to be considered. The following subsections present
recent progress on enabling technologies in self-powered sensors, with the aim of improving their
utilization in civil engineering applications.
5.3.1 Harvesting energy
Harvesting energy is clearly an essential feature of self-powered sensors. Power harvesting
mechanisms are being studied in the context of post-buckling response of elastic elements [100–
102], mechanical oscillators [103,104], etc. Due to their narrow operating frequency bandwidth,
vibration-based piezoelectric harvesters perform well only if excited near their resonance
frequency. Numerous studies have been conducted in terms of using vibration-based piezoelectric
harvesters for low-frequency excitation sources, which are below the resonance frequency of
piezoelectric materials, with the aim of increasing their operating bandwidth [105,106]. On the
other hand, elastic post-buckling and other types of elastic instabilities have been explored for
different applications (e.g., energy harvesting, actuation, sensing, etc.), where it was revealed that
elements can be configured to attain controllable bistable and multistable response [107]. Large
strain rates and energy release from elastic instabilities can be harnessed for designing power
harvesters. The post-buckling response of elastic slender strips/columns and beams has been
studied to develop power harvesting and damage sensing mechanisms under quasi-static loading
conditions. The snap-through buckling of axially loaded strip/column and beams transforms low-

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24
rate excitations (e.g., deformations) into high-rate acceleration/motion input for the power
harvester. Lajnef et al. [91,108] proposed a PFG self-powered sensing mechanism (illustrated in
section 3.1) that integrates the piezoelectricity driven hot-electron injection concept and
mechanical buckling in bilaterally constrained elastic strips/columns. The aim was to explore if
structural deformation events in the quasi-static range can be measured and recorded using energy
harvesters with vibration energy. The set up in a testing frame is shown in Figure 18 (a), where a
polycarbonate strip with fixed supports was placed between rigid continuous bilateral walls. A
PVDF-based piezoelectric oscillating energy-harvester was attached at middle of the axially
loaded strip with the harvester perpendicular to the strip’s axis in a cantilever configuration.
Experimental tests were performed by gradually applying the axial shortening to the strip, resulting
in multi-stable post-buckled configurations as depicted in Figure 18 (b). Results demonstrated that
the proposed mechanism was able to self-power at mHz frequencies and that, unlike other self-
powered sensing technologies, it could self-power at much lower frequencies (
𝜇
HZ).
Figure 18. (a) Experimental setup with the bilaterally constrained column, (b) Different modes of the column acting
as a mechanical energy concentrator [108]
The applicability of harvesting mechanical energy and damage identification using post-buckling
response of nonprismatic beams was studied [100,101]. The goal was to demonstrate improved
control of the post-buckling response of nonuniform cross‐section beams under quasi-static axial
forces for the purpose of enhanced energy harvesting. A non-prismatic beam as that shown in
Figure 19 was used for experimental tests. The beam with fixed end supports was placed between
aluminum constraining walls. Incremental axial shortening was applied to the top of the beam

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using a universal testing frame (see Figure 19). Results showed that the beam’s optimized non-
prismatic geometry leads to controlled post-buckling response and improved efficiency of the
energy harvester, indicating the feasibility of energy harvesting through controlled elastic
instabilities in slender elements.
Figure 19. Experimental setup with mounted piezoelectric harvester [101]
5.3.2 Signal communication
Another challenge in the use of self-powered sensors is how to communicate the signals measured
at each sensing unit to the SHM processor or data logger, where information collected from all the
sensors is interpreted for structural health evaluation. Existing self-powered sensing systems
mainly use a Radio Frequency Identification (RFID) scanner to read the measured data.
Nevertheless, there are some technical challenges in terms of using RFID sensors. First, it is a
difficult task to power all the operations since the RF signal can be weakened by the ambient
materials under test, affecting the read/write range of the sensor. Second, RFID sensors can be
affected by environmental factors (e.g., high pressure, high temperature, humidity, etc.) in their
applications, thus notably influencing their reliability under harsh conditions [109]. To cope with
the noted challenges, novel data transmission protocols are being developed and used in SHM
sensing systems. A power-efficient data communication concept called pulse switching protocol
has been developed, in which the occurrence and location of a binary signal is communicated
across wireless sensor networks through the monitored material substrate [110,111]. Recently, a
through-substrate self-powered communication network incorporating pulse
switching/communication protocol and the PFG self-powered sensor was developed for energy-
lean SHM platforms (see Figure 20) [112] to address the problem of energy availability in such

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26
systems. The proposed energy-efficient SHM platform was developed to minimize the energy
communication demand in self-powered sensor networks, thereby reaching pervasiveness in
structural monitoring sensing systems. The applicability and effectiveness of the proposed
platform with integrated self-powered sensing and signal communication was demonstrated
through numerical tests for energy-lean health monitoring of civil and aerospace structures [113–
126]. However, the applicability of the PFG self-powered sensors outfitted with pulse
communication protocol is yet to be evaluated for monitoring large-scale civil
structures/infrastructure to fully demonstrate the advantages and unique abilities of the integrated
self-powered sensing system.
Figure 20. Through-substrate self-powered sensor network for energy-lean SHM
5.3.3 Signal interpretation
Although rapid progress in self-powered sensors enables civil structures/infrastructure to
wirelessly communicate their health status, the interpretation and analysis of the massive amount
of information/data collected from the sensor networks still poses a great challenge. A viable
solution to the noted problem is a smart SHM platform based on the integration of self-powered
sensing technology, intelligent data mining algorithms, IoT, and cloud computing. Artificial
intelligence methods (e.g., machine learning, deep learning, etc.) are being increasingly used as
promising tools in the context of SHM and condition assessment for the interpretation of large
amounts of data collected from smart sensors [120]. Furthermore, IoT and cloud computing

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platforms are being proposed for smart monitoring and health assessment in smart cities [127].
Therefore, an enhanced connected platform developed based on merging the noted
technologies/methods enables an intelligent data-driven decision making for smart SHM and
condition assessment employing self-powered sensing, resulting in enhanced use of such smart
sensing systems.
6. CONCLUDING REMARKS
This survey paper presented the importance of self-powered sensors in the civil engineering
domain, while highlighting their field applications over the last decade. A content analysis
approach was followed to choose the reviewed literature. The aim was to ensure that the
publications included in this review paper were selected from the well-accepted and prominent
academic databases. As part of the content analysis, quantitative and qualitative analyses were
performed, which led to the identification of a variety of self-powered sensors and their
applications in civil engineering. As a result, the paper highlighted self-powered sensing
technologies being used in diverse civil engineering applications, including SHM and damage
assessment, transportation infrastructures, structural vibration control using dampers, and
corrosion prediction in reinforced concrete structures. It was revealed that among various noted
applications, the most use of self-powered sensing over the last decade has been for SHM and
damage prognosis. The survey highlighted that in contrast with traditional wireless sensor
networks, the advantages offered by using self-powered sensors are ease of installation, cost-
effectiveness, and a sustainable energy resource to empower wireless sensor networks for
innovative civil engineering applications. It was also found that self-powered sensors are
increasingly establishing themselves as a promising alternative to conventional sensing systems
for numerous civil engineering applications, e.g., SHM, transportation monitoring, corrosion
monitoring, etc. Finally, the literature survey revealed that among the numerous types of self-
powered sensors that have been developed, piezoelectric sensors are being widely used for SHM
and condition assessment due to their low-power and low-cost attributes.
Potential research avenues and future directions for self-powered sensors in civil engineering
(including smart cities, data-driven SHM and condition assessment) were also discussed in detail

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as part of the review. It is expected that self-powered sensors can be effectively used in the domain
of smart cities due to their unique abilities. When incorporated with the novel concept of IoT, self-
powered sensors can be employed to address complex problems in smart cites. Based on the
conducted survey, self-powered sensing technology can also be applied to the domain of data-
driven SHM, with the aim of addressing the important issue of energy availability. Further,
enabling technologies/methods in self-powered sensing with regards to energy harvesting,
communication of measured signals, and analysis of the received signals at the SHM processor
were showcased. These are essential to effectively enhance utilization of self-powered sensors and
fully take advantage of their unique features for future civil engineering applications. Finally, it is
anticipated that efficiency and reliability of many existing civil and structural engineering
applications can be notably increased through development of pioneering self-sustained and
multifunctional monitoring platforms employing self-powered sensing.
REFERENCES
[1] Lynch JP, Loh KJ. A summary review of wireless sensors and sensor networks for structural health
monitoring. Shock and Vibration Digest 2006;38:91–130.
[2] Yun C-B, Min J. Smart sensing, monitoring, and damage detection for civil infrastructures. KSCE
Journal of Civil Engineering 2011;15:1–14.
[3] Lynch JP, Sundararajan A, Law KH, Kiremidjian AS, Kenny T, Carryer E. Embedment of structural
monitoring algorithms in a wireless sensing unit. Structural Engineering and Mechanics
2003;15:285–97.
[4] SOHN H. A review of structural health monitoring literature: 1996-2001. Los Alamos National
Laboratory Report 2004.
[5] Farrar CR, Park G, Allen DW, Todd MD. Sensor network paradigms for structural health
monitoring. Structural Control and Health Monitoring: The Official Journal of the International
Association for Structural Control and Monitoring and of the European Association for the Control
of Structures 2006;13:210–25.
[6] Yi T, Li H, Gu M. Optimal sensor placement for structural health monitoring based on multiple
optimization strategies. The Structural Design of Tall and Special Buildings 2011;20:881–900.
[7] Yi T-H, Li H-N, Gu M. A new method for optimal selection of sensor location on a high-rise building
using simplified finite element model. Structural Engineering and Mechanics 2011;37:671–84.
[8] Yi T-H, Li H-N, Zhang X-D. A modified monkey algorithm for optimal sensor placement in
structural health monitoring. Smart Materials and Structures 2012;21:105033.
[9] Zhang Y, Kurata M, Lynch JP. Long-term modal analysis of wireless structural monitoring data
from a suspension bridge under varying environmental and operational conditions: System design
and automated modal analysis. Journal of Engineering Mechanics 2017;143:04016124.
[10] O’Connor SM, Zhang Y, Lynch JP, Ettouney MM, Jansson PO. Long-term performance assessment
of the Telegraph Road Bridge using a permanent wireless monitoring system and automated
statistical process control analytics. Structure and Infrastructure Engineering 2017;13:604–24.

This is a preprint draft. The published article can be found at: https://doi.org/10.1016/j.engstruct.2021.111963
Please cite this paper as: Salehi, H., Burgueño, R., Chakrabartty, S., Lajnef, N. and Alavi, A.H., 2021. A
comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research
trends. Engineering Structures. https://doi.org/10.1016/j.engstruct.2021.111963
29
[11] Sundaram BA, Ravisankar K, Senthil R, Parivallal S. Wireless sensors for structural health
monitoring and damage detection techniques. Current Science 2013:1496–505.
[12] Lynch J. Overview of wireless sensors for real-time health monitoring of civil structures.
Proceedings of the 4th International Workshop on Structural Control, 2004, p. 189–94.
[13] Spencer Jr B, Ruiz-Sandoval ME, Kurata N. Smart sensing technology: opportunities and
challenges. Structural Control and Health Monitoring 2004;11:349–68.
[14] Lynch JP. Design of a wireless active sensing unit for localized structural health monitoring.
Structural Control and Health Monitoring: The Official Journal of the International Association for
Structural Control and Monitoring and of the European Association for the Control of Structures
2005;12:405–23.
[15] Brincker R, Ventura C. Introduction to operational modal analysis. John Wiley & Sons; 2015.
[16] Elvin NG, Elvin AA, Spector M. A self-powered mechanical strain energy sensor. Smart Materials
and Structures 2001;10:293.
[17] Sirohi J, Chopra I. Fundamental understanding of piezoelectric strain sensors. Journal of Intelligent
Material Systems and Structures 2000;11:246–57.
[18] Rahimi M, Shah H, Sukhatme GS, Heideman J, Estrin D. Studying the feasibility of energy
harvesting in a mobile sensor network. 2003 IEEE International Conference on Robotics and
Automation (Cat. No. 03CH37422), vol. 1, IEEE; 2003, p. 19–24.
[19] Elvin N, Elvin A, Choi DH. A self-powered damage detection sensor. The Journal of Strain Analysis
for Engineering Design 2003;38:115–24.
[20] Miller TI, Spencer Jr BF, Li J, Jo H. Solar energy harvesting and software enhancements for
autonomous wireless smart sensor networks. Newmark Structural Engineering Laboratory.
University of Illinois at Urbana Champaign; 2010.
[21] Farinholt KM, Miller N, Sifuentes W, MacDonald J, Park G, Farrar CR. Energy harvesting and
wireless energy transmission for embedded SHM sensor nodes. Structural Health Monitoring
2010;9:269–80.
[22] Zhou G-D, Yi T-H. Recent developments on wireless sensor networks technology for bridge health
monitoring. Mathematical Problems in Engineering 2013;2013.
[23] Godínez-Azcuaga VF, Inman DJ, Ziehl PH, Giurgiutiu V, Nanni A. Recent advances in the
development of a self-powered wireless sensor network for structural health prognosis. vol. 7983,
International Society for Optics and Photonics; 2011, p. 798325.
[24] Lee Y, Blaauw D, Sylvester D. Ultralow power circuit design for wireless sensor nodes for structural
health monitoring. Proceedings of the IEEE 2016;104:1529–46.
[25] Fu TS, Ghosh A, Johnson EA, Krishnamachari B. Energy-efficient deployment strategies in
structural health monitoring using wireless sensor networks. Structural Control and Health
Monitoring 2013;20:971–86.
[26] Spencer BF, Jo H, Mechitov KA, Li J, Sim S-H, Kim RE, et al. Recent advances in wireless smart
sensors for multi-scale monitoring and control of civil infrastructure. Journal of Civil Structural
Health Monitoring 2016;6:17–41.
[27] Cha Y-J, Kim Y, You T. Advanced sensing and structural health monitoring. Journal of Sensors
2018;2018.
[28] Das S, Saha P. A review of some advanced sensors used for health diagnosis of civil engineering
structures. Measurement 2018;129:68–90.
[29] Krippendorff K. Content analysis: An introduction to its methodology. Sage; 2012.
[30] Huang C, Chakrabartty S. A hybrid energy scavenging sensor for long-term mechanical strain
monitoring, IEEE; 2011, p. 2473–6.

This is a preprint draft. The published article can be found at: https://doi.org/10.1016/j.engstruct.2021.111963
Please cite this paper as: Salehi, H., Burgueño, R., Chakrabartty, S., Lajnef, N. and Alavi, A.H., 2021. A
comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research
trends. Engineering Structures. https://doi.org/10.1016/j.engstruct.2021.111963
30
[31] Taware S, Deshmukh S. A review of energy harvesting from piezoelectric materials. IOSR Journal
of Mechanical and Civil Engineering (IOSR-JMCE) 2013:43–50.
[32] Chen J, Qiu Q, Han Y, Lau D. Piezoelectric materials for sustainable building structures:
Fundamentals and applications. Renewable and Sustainable Energy Reviews 2019;101:14–25.
[33] Lajnef N, Elvin NG, Chakrabartty S. A piezo-powered floating-gate sensor array for long-term
fatigue monitoring in biomechanical implants. IEEE Transactions on Biomedical Circuits and
Systems 2008;2:164–72.
[34] Huang C, Lajnef N, Chakrabartty S. Calibration and characterization of self-powered floating-gate
usage monitor with single electron per second operational limit. IEEE Transactions on Circuits and
Systems I: Regular Papers 2009;57:556–67.
[35] K Aono S. Self-powered sensors to facilitate infrastructural internet-of-things for smart structures.
The 13th International Workshop on Advanced Smart Materials and Smart Structures Technology,
2017.
[36] Ryu D, Loh KJ. Strain sensing using photocurrent generated by photoactive P3HT-based
nanocomposites. Smart Materials and Structures 2012;21:065016.
[37] Ryu D, Meyers FN, Loh KJ. Inkjet-printed, flexible, and photoactive thin film strain sensors. Journal
of Intelligent Material Systems and Structures 2015;26:1699–710.
[38] Pulliam E, Hoover G, Tiparti D, Ryu D. Development of self-powered strain sensor using mechano-
luminescent ZnS: Cu and mechano-optoelectronic P3HT. vol. 10168, International Society for
Optics and Photonics; 2017, p. 101680G.
[39] Olawale DO, Kliewer K, Okoye A, Dickens T, Uddin MJ, Okoli OI. Getting light through
cementitious composites with in situ triboluminescent damage sensor. Structural Health Monitoring
2014;13:177–89.
[40] Pulliam E, Hoover G, Ryu D. Multifunctional mechano-luminescent-optoelectronic composites for
self-powered strain sensing, American Society of Mechanical Engineers Digital Collection; 2017.
[41] Garcia C, Trendafilova I, del Rio JS. Detection and measurement of impacts in composite structures
using a self-powered triboelectric sensor. Nano Energy 2019;56:443–53.
[42] Liu H, Wang H, Lyu Y, He C, Liu Z. A novel triboelectric nanogenerator based on carbon fiber
reinforced composite lamina and as a self-powered displacement sensor. Microelectronic
Engineering 2020;224:111231.
[43] Aono K, Lajnef N, Faridazar F, Chakrabartty S. Infrastructural health monitoring using self-powered
Internet-of-Things. 2016 IEEE international symposium on circuits and systems (ISCAS), IEEE;
2016, p. 2058–61.
[44] Ryu D, Macias G. Self-powered strain sensor based on mechano-luminescence-optoelectronic
selfsensing composites, British Institute of Non-destructive Testing; 2018.
[45] Alavi AH, Hasni H, Lajnef N, Chatti K, Faridazar F. Damage detection using self-powered wireless
sensor data: an evolutionary approach. Measurement 2016;82:254–83.

This is a preprint draft. The published article can be found at: https://doi.org/10.1016/j.engstruct.2021.111963
Please cite this paper as: Salehi, H., Burgueño, R., Chakrabartty, S., Lajnef, N. and Alavi, A.H., 2021. A
comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research
trends. Engineering Structures. https://doi.org/10.1016/j.engstruct.2021.111963
31
[46] Alavi AH, Hasni H, Lajnef N, Chatti K, Faridazar F. An intelligent structural damage detection
approach based on self-powered wireless sensor data. Automation in Construction 2016;62:24–44.
[47] Alavi AH, Hasni H, Jiao P, Borchani W, Lajnef N. Fatigue cracking detection in steel bridge girders
through a self-powered sensing concept. Journal of Constructional Steel Research 2017;128:19–38.
[48] Alavi AH, Hasni H, Lajnef N, Chatti K. Damage growth detection in steel plates: Numerical and
experimental studies. Engineering Structures 2016;128:124–38.
[49] Hasni H, Jiao P, Alavi AH, Lajnef N, Masri SF. Structural health monitoring of steel frames using
a network of self-powered strain and acceleration sensors: A numerical study. Automation in
Construction 2018;85:344–57.
[50] Hasni H, Alavi AH, Jiao P, Lajnef N. Detection of fatigue cracking in steel bridge girders: a support
vector machine approach. Archives of Civil and Mechanical Engineering 2017;17:609–22.
[51] Hasni H, Jiao P, Lajnef N, Alavi AH. Damage localization and quantification in gusset plates: A
battery-free sensing approach. Structural Control and Health Monitoring 2018;25:e2158.
[52] Hasni H, Alavi AH, Lajnef N, Abdelbarr M, Masri SF, Chakrabartty S. Self-powered piezo-floating-
gate sensors for health monitoring of steel plates. Engineering Structures 2017;148:584–601.
[53] Kurata M, Lynch JP, Galchev T, Flynn M, Hipley P, Jacob V, et al. A two-tiered self-powered
wireless monitoring system architecture for bridge health management. vol. 7649, International
Society for Optics and Photonics; 2010, p. 76490K.
[54] Zhou D, Kong N, Ha DS, Inman DJ. A self-powered wireless sensor node for structural health
monitoring. vol. 7650, International Society for Optics and Photonics; 2010, p. 765010.
[55] Mongare A, Ulibarri-Sanchez J, Misla A, Park Y, Zagrai A, Ryu D. Autonomous structural
composites for self-powered strain sensing-enabled damage detection. vol. 10970, International
Society for Optics and Photonics; 2019, p. 1097015.
[56] Aono K, Hasni H, Pochettino O, Lajnef N, Chakrabartty S. Quasi-self-powered infrastructural
internet of things: The mackinac bridge case study. Proceedings of the 2018 on Great Lakes
Symposium on VLSI, 2018, p. 335–40.
[57] Aono K, Hasni H, Pochettino O, Lajnef N, Chakrabartty S. Quasi-self-powered piezo-floating-gate
sensing technology for continuous monitoring of large-scale bridges. Frontiers in Built Environment
2019;5:29.
[58] Lallart M, Guyomar D. Low-power computation methods and self-powered systems for Structural
Health Monitoring techniques using Lamb waves for embedded sensing. Shock and Vibration
2012;19:867–77.
[59] Park S, Lee C, Kim H, Yun H-S, Kwon S. Development of piezoelectric energy harvesting modules
for impedance-based wireless structural health monitoring system. KSCE Journal of Civil
Engineering 2013;17:746–52.
[60] Jiang J, Claudel C. A high performance, low power computational platform for complex sensing
operations in smart cities. HardwareX 2017;1:22–37.
[61] Kexel C, Testoni N, Zonzini F, Moll J, De Marchi L. Low-Power MIMO Guided-Wave
Communication. IEEE Access 2020;8:217425–36.
[62] Wu F, Rüdiger C, Yuce MR. Real-time performance of a self-powered environmental IoT sensor
network system. Sensors 2017;17:282.
[63] Tzortzakis K, Papafotis K, Sotiriadis PP. Wireless self powered environmental monitoring system
for smart cities based on LoRa. 2017 Panhellenic Conference on Electronics and
Telecommunications (PACET), IEEE; 2017, p. 1–4.
[64] Kjellby R, Cenkeramaddi LR, Johnsrud TE, Jevne G, Løtveit SE, Lozano BB, et al. Design and
Prototype Implementation of Long-Range Self-powered Wireless IoT Devices. 2018 IEEE

This is a preprint draft. The published article can be found at: https://doi.org/10.1016/j.engstruct.2021.111963
Please cite this paper as: Salehi, H., Burgueño, R., Chakrabartty, S., Lajnef, N. and Alavi, A.H., 2021. A
comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research
trends. Engineering Structures. https://doi.org/10.1016/j.engstruct.2021.111963
32
International Symposium on Smart Electronic Systems (iSES)(Formerly iNiS), IEEE; 2018, p. 215–
8.
[65] Lin L, Hu Y, Xu C, Zhang Y, Zhang R, Wen X, et al. Transparent flexible nanogenerator as self-
powered sensor for transportation monitoring. Nano Energy 2013;2:75–81.
[66] Lajnef N, Rhimi M, Chatti K, Mhamdi L, Faridazar F. Toward an integrated smart sensing system
and data interpretation techniques for pavement fatigue monitoring. Computer-Aided Civil and
Infrastructure Engineering 2011;26:513–23.
[67] Wang L, Ye Z, Hou Y, Yang H, Tong X. Self-Powered Intelligent Monitoring System for
Transportation Infrastructures, American Society of Civil Engineers Reston, VA; 2018, p. 409–19.
[68] Li D, Wang X, Cai W, Zhu L, Guo C. Application of big data collection based on self-powered
technology in intelligent transportation system. Proceedings of the 2nd International Conference on
Big Data Research, 2018, p. 47–51.
[69] Gao M, Wang P, Wang Y, Yao L. Self-powered ZigBee wireless sensor nodes for railway condition
monitoring. IEEE Transactions on Intelligent Transportation Systems 2017;19:900–9.
[70] Chen Y, Zhang H, Quan L, Zhang Z, Lü C. Theoretical assessment on piezoelectric energy
harvesting in smart self-powered asphalt pavements. Journal of Vibration Engineering &
Technologies 2018;6:1–10.
[71] Balid W, Refai HH. On the development of self-powered iot sensor for real-time traffic monitoring
in smart cities. 2017 IEEE SENSORS, IEEE; 2017, p. 1–3.
[72] Alavi AH, Hasni H, Lajnef N, Chatti K. Continuous health monitoring of pavement systems using
smart sensing technology. Construction and Building Materials 2016;114:719–36.
[73] Hasni H, Alavi AH, Jiao P, Lajnef N, Chatti K, Aono K, et al. A new approach for damage detection
in asphalt concrete pavements using battery-free wireless sensors with non-constant injection rates.
Measurement 2017;110:217–29.
[74] Hasni H, Alavi AH, Chatti K, Lajnef N. A self-powered surface sensing approach for detection of
bottom-up cracking in asphalt concrete pavements: Theoretical/numerical modeling. Construction
and Building Materials 2017;144:728–46.
[75] Zhu S, Shen W, Li J. A Self-powered Vibration Monitoring and Control System for Stay Cables:
Numerical Study 2012.
[76] Chen C, Liao W-H. A self-powered, self-sensing magnetorheological damper. 2010 IEEE
International Conference on Mechatronics and Automation, IEEE; 2010, p. 1364–9.
[77] Chen C, Liao W-H. Design and analysis of a self-powered, self-sensing magnetorheological damper.
vol. 7977, International Society for Optics and Photonics; 2011, p. 797716.
[78] Sapiński B. Experimental study of a self-powered and sensing MR-damper-based vibration control
system. Smart Materials and Structures 2011;20:105007.
[79] Wang SQ, Yu M, Fu J, Choi S, Li P. A novel energy harvesting device for self-monitoring wireless
sensor node in fluid dampers. Smart Materials and Structures 2012;21:085027.
[80] Xinchun G, Yonghu H, Yi R, Hui L, Jinping O. A novel self-powered MR damper: theoretical and
experimental analysis. Smart Materials and Structures 2015;24:105033.
[81] Wang D-H, Bai X-X. A magnetorheological damper with an integrated self-powered displacement
sensor. Smart Materials and Structures 2013;22:075001.
[82] Shen W, Zhu S, Zhu H, Xu Y. Electromagnetic energy harvesting from structural vibrations during
earthquakes. Smart Struct Syst 2016;18:449–70.
[83] Shen W, Zhu S, Xu Y. An experimental study on self-powered vibration control and monitoring
system using electromagnetic TMD and wireless sensors. Sensors and Actuators A: Physical
2012;180:166–76.

This is a preprint draft. The published article can be found at: https://doi.org/10.1016/j.engstruct.2021.111963
Please cite this paper as: Salehi, H., Burgueño, R., Chakrabartty, S., Lajnef, N. and Alavi, A.H., 2021. A
comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research
trends. Engineering Structures. https://doi.org/10.1016/j.engstruct.2021.111963
33
[84] Zhao S, Fu H. A novel vibration sensor system for frequency measurement based on Bias Flip
technique. Measurement 2018;124:56–63.
[85] Tomicek D, Tham Y-H, Seah WK, Rayudu R. Vibration-powered wireless sensor for structural
monitoring during earthquakes. Proceedings of the 6th International Conference on Structural Health
Monitoring of Intelligent Infrastructure, 2013.
[86] Kim H, Lee S, Cho C, Kim JE, Youn BD, Kim YY. An experimental method to design piezoelectric
energy harvesting skin using operating deflection shapes and its application for self-powered
operation of a wireless sensor network. Journal of Intelligent Material Systems and Structures
2015;26:1128–37.
[87] Yu Y, Qiao G, Ou J. Self-powered wireless corrosion monitoring sensors and networks. IEEE
Sensors Journal 2010;10:1901–2.
[88] Qiao G, Sun G, Hong Y, Qiu Y, Ou J. Remote corrosion monitoring of the RC structures using the
electrochemical wireless energy-harvesting sensors and networks. NDT & E International
2011;44:583–8.
[89] Su D, Xia Y, Yuan R. Self-powered wireless sensor network for automated corrosion prediction of
steel reinforcement. Journal of Sensors 2018;2018.
[90] Lajnef N, Burgueño R, Chakrabartty S, Borchani W. Self-Powered Sensing System for the
Monitoring of Quasi-Static Structural Response, American Society of Mechanical Engineers Digital
Collection; 2013.
[91] Lajnef N, Chakrabartty S, Burgueño R, Borchani W. Quasi-static self-powered sensing and data
logging. vol. 9061, International Society for Optics and Photonics; 2014, p. 90611V.
[92] Islam T, Mukhopadhyay SC, Suryadevara NK. Smart sensors and internet of things: a postgraduate
paper. IEEE Sensors Journal 2016;17:577–84.
[93] Kordonsky W. Elements and devices based on magnetorheological effect. Journal of Intelligent
Material Systems and Structures 1993;4:65–9.
[94] Salehi H, Taghikhany T. Application of robust-optimum algorithms in semi-active control strategy
for seismic protection of equipment. 15th World Conference on Earthquake Engineering, Sep, 2012,
p. 24–8.
[95] Salehi H, Taghikhany T, Yeganeh Fallah A. Seismic protection of vulnerable equipment with semi-
active control by employing robust and clipped-optimal algorithms. International Journal of Civil
Engineering 2014;12:413–28.
[96] Nehl TW, Betts JA, Mihalko LS. An integrated relative velocity sensor for real-time damping
applications. IEEE Transactions on Industry Applications 1996;32:873–81.
[97] Cho S-W, Jung H-J, Lee I-W. Smart passive system based on magnetorheological damper. Smart
Materials and Structures 2005;14:707.
[98] Choi K-M, Jung H-J, Cho S-W, Lee I-W. Application of smart passive damping system using MR
damper to highway bridge structure. Journal of Mechanical Science and Technology 2007;21:870–
4.
[99] Ahmad S. Reinforcement corrosion in concrete structures, its monitoring and service life prediction–
–a review. Cement and Concrete Composites 2003;25:459–71.
[100] Liu S, Burgueño R. Controlled elastic postbuckling of bilaterally constrained non-prismatic
columns: application to enhanced quasi-static energy harvesters. Smart Materials and Structures
2016;25:125010.
[101] Jiao P, Borchani W, Alavi AH, Hasni H, Lajnef N. An energy harvesting and damage sensing
solution based on postbuckling response of nonuniform cross-section beams. Structural Control and
Health Monitoring 2018;25:e2052.

This is a preprint draft. The published article can be found at: https://doi.org/10.1016/j.engstruct.2021.111963
Please cite this paper as: Salehi, H., Burgueño, R., Chakrabartty, S., Lajnef, N. and Alavi, A.H., 2021. A
comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research
trends. Engineering Structures. https://doi.org/10.1016/j.engstruct.2021.111963
34
[102] Liu S, Azad AI, Burgueño R. Energy harvesting from quasi-static deformations via bilaterally
constrained strips. Journal of Intelligent Material Systems and Structures 2018;29:3572–81.
[103] Gammaitoni L, Neri I, Vocca H. Nonlinear oscillators for vibration energy harvesting. Applied
Physics Letters 2009;94:164102.
[104] Alikhassi M, Nili-Ahmadabadi M, Tikani R, Karimi MH. A Novel Approach for Energy Harvesting
from Feedback Fluidic Oscillator. International Journal of Precision Engineering and
Manufacturing-Green Technology 2019;6:769–78.
[105] Sodano HA, Inman DJ, Park G. A review of power harvesting from vibration using piezoelectric
materials. Shock and Vibration Digest 2004;36:197–206.
[106] Hajati A, Kim S-G. Ultra-wide bandwidth piezoelectric energy harvesting. Applied Physics Letters
2011;99:083105.
[107] Lajnef N, Burgueño R, Borchani W, Sun Y. A concept for energy harvesting from quasi-static
structural deformations through axially loaded bilaterally constrained columns with multiple
bifurcation points. Smart Materials and Structures 2014;23:055005.
[108] Lajnef N, Borchani W, Burgueno R, Chakrabartty S. Self-powered piezo-floating-gate smart-gauges
based on quasi-static mechanical energy concentrators and triggers. IEEE Sensors Journal
2014;15:676–83.
[109] Cui L, Zhang Z, Gao N, Meng Z, Li Z. Radio frequency identification and sensing techniques and
their applications—A review of the state-of-the-art. Sensors 2019;19:4012.
[110] Huo Q, Rao J, Biswas S. Pulse switching: Toward a packet-less protocol paradigm for event sensing.
IEEE Transactions on Mobile Computing 2011;12:35–50.
[111] Huo Q, Dong B, Biswas S. A pulse switching paradigm for ultra low power cellular sensor networks.
Pervasive and Mobile Computing 2014;13:221–45.
[112] Das S, Salehi H, Shi Y, Chakrabartty S, Burgueno R, Biswas S. Towards packet-less ultrasonic
sensor networks for energy-harvesting structures. Computer Communications 2017;101:94–105.
[113] Salehi H, Das S, Chakrabartty S, Biswas S, Burgueño R. Structural assessment and damage
identification algorithms using binary data. ASME 2015 conference on smart materials, adaptive
structures and intelligent systems, American Society of Mechanical Engineers Digital Collection;
2015.
[114] Salehi H, Burgueño R, Das S, Biswas S, Chakrabartty S. Structural health monitoring from discrete
binary data through pattern recognition. Insights and Innovations in Structural Engineering,
Mechanics and Computation 2016:1840–5.
[115] Salehi H, Das S, Chakrabartty S, Biswas S, Burgueño R. A machine-learning approach for damage
detection in aircraft structures using self-powered sensor data. Sensors and Smart Structures
Technologies for Civil, Mechanical, and Aerospace Systems 2017, vol. 10168, International Society
for Optics and Photonics; 2017, p. 101680X.
[116] Salehi H, Burgueño R, Das S, Biswas S, Chakrabartty S. Localized damage identification of plate-
like structures with time-delayed binary data from a self-powered sensor network. ASME 2017
Conference on Smart Materials, Adaptive Structures and Intelligent Systems, American Society of
Mechanical Engineers Digital Collection; 2017.
[117] Salehi H, Das S, Chakrabartty S, Biswas S, Burgueno R. A methodology for structural health
diagnosis and assessment using machine learning with noisy and incomplete data from self-powered
wireless sensors. Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace
Systems 2018, vol. 10598, International Society for Optics and Photonics; 2018, p. 105980X.
[118] Salehi H, Das S, Chakrabartty S, Biswas S, Burgueño R. Structural damage identification using
image-based pattern recognition on event-based binary data generated from self-powered sensor
networks. Structural Control and Health Monitoring 2018;25:e2135.

This is a preprint draft. The published article can be found at: https://doi.org/10.1016/j.engstruct.2021.111963
Please cite this paper as: Salehi, H., Burgueño, R., Chakrabartty, S., Lajnef, N. and Alavi, A.H., 2021. A
comprehensive review of self-powered sensors in civil infrastructure: State-of-the-art and future research
trends. Engineering Structures. https://doi.org/10.1016/j.engstruct.2021.111963
35
[119] Salehi H, Das S, Chakrabartty S, Biswas S, Burgueño R. Damage identification in aircraft structures
with self-powered sensing technology: A machine learning approach. Structural Control and Health
Monitoring 2018;25:e2262.
[120] Salehi H, Burgueno R. Emerging artificial intelligence methods in structural engineering.
Engineering Structures 2018;171:170–89.
[121] Salehi H. Data interpretation frameworks employing machine learning for energy-lean data-driven
sructural health monitoring with novel self-powered sensing technology. Michigan State University;
2019.
[122] Salehi H, Das S, Chakrabartty S, Biswas S, Burgueño R. An algorithmic framework for
reconstruction of time-delayed and incomplete binary signals from an energy-lean structural health
monitoring system. Engineering Structures 2019;180:603–20.
[123] Salehi H, Chakrabartty S, Biswas S, Burgueño R. Localized damage identification in plate-like
structures using self-powered sensor data: A pattern recognition strategy. Measurement
2019;135:23–38.
[124] Salehi H, Burgueño R. Pattern recognition framework using asynchronous discrete binary data for
condition and damage assessment in plate-like structures. Journal of Intelligent Material Systems
and Structures 2019;30:1200–15.
[125] Salehi H, Das S, Biswas S, Burgueño R. Data mining methodology employing artificial intelligence
and a probabilistic approach for energy-efficient structural health monitoring with noisy and delayed
signals. Expert Systems with Applications 2019;135:259–72.
[126] Salehi H, Biswas S, Burgueño R. Data interpretation framework integrating machine learning and
pattern recognition for self-powered data-driven damage identification with harvested energy
variations. Engineering Applications of Artificial Intelligence 2019;86:136–53.
[127] Alavi AH, Jiao P, Buttlar WG, Lajnef N. Internet of Things-enabled smart cities: State-of-the-art
and future trends. Measurement 2018;129:589–606.