Novel sensing techniques for full-scale testing of civil structures

Abstract

Performing full-scale structural testing is an important methodology for researchers and engineers in the civil engineering industry. Full scale testing helps the researchers understand civil infrastructures’ loading scenarios, behaviors, and health conditions. It helps the engineers verify, polish, and simplify the structural design and analysis theories. To conduct a full-scale structural testing, sensors are used for data acquisitions. To help structural researchers and engineers get familiar with sensing technologies and select the most effective sensors, this study reviewed and categorized new sensing techniques for full-scale structural testing applications. The researchers of this study categorized sensors used for civil-infrastructure testing into traditional contact sensors and remote sensors based upon their application methodologies, and into cabled sensors and wireless sensors based upon their data communication strategies. The detailed descriptions of wireless sensors and remote sensing techniques and their on-site full-scale applications are presented.

Keywords

sensing technique / full-scale testing / wireless sensor / remote sensing / LiDAR / laser vibrometer

Introduction

Civil infrastructures, including bridges, buildings, dams, and pipelines, are complex engineered systems that ensure societies’ economic and industrial prosperity. To design structures that are safe for public use, standardized building codes and design methodologies have been created. Unfortunately, structure failures and structural deteriorations still exist. Most of those failures and deteriorations are the results of i) a poor understanding of the structural properties and behaviors, ii) harsh loading scenarios and severe environmental conditions not anticipated during design, iii) inferior construction materials not able to withstand the loads, and iv) employment of unskilled labor on construction work.

Full-scale structural testing may help researchers and engineers in the civil engineering industry i) understand the behavior of those civil infrastructures under practical loading cases, ii) characterize the harsh loading scenarios and severe environmental conditions on these infrastructures, iii) detect the damages and monitor the health conditions of constructed civil structures, and iv) verify, polish, and simplify the structural design and analysis theories.

To conduct a full-scale structural testing, sensors are used for data acquisitions. Structural researchers and engineers use sensors to record all kinds of physical parameters of civil infrastructures and to convert these parameters to analog or digital signals. The physical parameters most often used to depict the property, behavior, or health condition of civil infrastructures include accelerations, velocities, displacements, positions, strains, and forces. In addition, temperatures, pressures, sound, flow rates, viscosities, optical radiations, and electromagnetic fields have been used as well. Based upon their application methodologies, sensors used for civil-infrastructure testing could be categorized into traditional contact sensors and remote (non-contact) sensors. Based upon their data communication strategies, sensors used for civil-structure testing could be divided into traditional cabled sensors and wireless sensors.

This study reviewed and categorized new sensing techniques for full-scale structural testing applications. It is expected to help structural researchers and engineers to get familiar with these sensing technologies and select the most effective testing tools.

Remote sensing technologies and wireless sensors

Commonly used sensors in structural engineering include accelerometers, displacement sensors, strain gauges, load cells, and thermal sensors. The data acquired by these sensors are traditionally transmitted to the users through cables. However, during a full-scale civil-infrastructure testing, cable set-up is costly and time consuming. Therefore, wireless communication has been introduced in civil engineering sensing to facilitate field tests. In addition, engineers often come across structures that prevent sensor installation; and global measurements are often of more interest than the typical individual measurements that traditional sensors provide. Hence, remote sensing technologies find their application potentials in structural testing. A detailed discussion on remote and wireless sensing technologies appears in the following sections.

Remote sensing technologies

Remote (non-contact) sensing technologies, such as photogrammetrics and laser optics, which do not rely on physical contact with a subject material, have evolved to complement traditional contact sensing methods [1-3]. The ability to apply remote sensing techniques to civil structure health monitoring practices has great potential. Currently, these remote sensing techniques are primarily used i) to detect surface damages, ii) to detect subsurface damages, and iii) to measure distances and vibrations.

Surface damage imaging

One facet of remote sensing technologies employs the manipulation of optics and photography to show the surface damage images of a structure. High definition cameras and imaging technologies are commonly used in these types of sensing technologies. The related technologies include ground-based photography or photogrammetry, interferometry, and satellite and aerial imaging.

Photography is the most popular technology to show the surface damage of a structure. It creates images by recording light through an image-forming device. Digital cameras are now conventional tools in structural assessment, replacing traditional visual inspections. Lee et al. [4] proposed image processing methods to evaluate rust defects for steel bridge coating. Digital color images were processed instead of gray-scale images commonly used in the photography techniques [4]. Photogrammetry is a technology that can provide depth and height information that cannot otherwise be obtained from an individual image. It determines the geometric properties of a structure; and based on this geometric information, civil engineers develop algorithms to obtain results within their interests, such as structural deformations. Hutt and Cawley [5] utilized correlating images of a loaded and unloaded surface to acquire displacement and strain fields. This process utilized an exhaustive synthetic pattern to track points. Koschitzki et al [6]. used photogrammetric techniques, coupled with acoustic emission analysis, to rapidly detect and classify material cracking. Ye et al. [7] developed a close-range digital photogrammetric system for full-field, spatially intensive measurement for structural applications.

Interferometry is a technique using the effects of wavelength interference to measure structural characteristics and reactions. It uses a light beam with a known wavelength, splits it, externally influences it(altering the wavelength), and recombines it before doing an analysis. Jacquot and Facchini [8] introduced image interferometry in civil engineering. They reported that interferometric imaging is an effective method for deformation measurement. Both laboratory and field applications of interferometric imaging were described in the paper, such as micro-deformation analysis of granular media and soils, measurement of differential shrinkage of concrete sample, and flatness check of highway bridge box girders by fringe projection [8]. Krajewski [9] validated the use of interferometric technology for bridge inspection applications. Interactions of applied wavelengths were used to map contour images of a structure and to determine the characteristics of deformation.

Remote sensing technologies with satellite imaging are traditionally used for agriculture, forestry, and regional planning. Alhaddad et al. [10] determined building densities using satellite imaging technology. The satellite imagery was used to assess temporal construction patterns and mark locations as well. With the improvement of image accuracy, these technologies come to the structural testing applications. However, most of the applications remain for large-scale damage evaluations [11]. Similar to the satellite imaging technologies, aerial photography is traditionally used for monitoring the dynamics of vegetation, forestry, and hydrology [12].Now, aerial photography has been developed as a sensing technology for surface imaging of structures [13-15]. Chen et al. [14] aerially photographed several highway bridges to visualize large cracking and temporal joint behavior. This research promoted an economic technology for modernizing bridge and highway surface inspection and imaging. Rathinam et al. [15] used an autonomous unmanned aerial vehicle equipped with an onboard video to help with inspection of linear structures such as roads.

Subsurface damage detection

A critical application of remote sensing technologies is the ability to measure defects beneath the surface of a subject. Subsurface defect sensing technologies employ many different sensing principles such as thermology, radiography, ultrasonics, acoustics, elastic waves, etc.

Thermal infrared (IR) cameras utilize ambient environmental conditions to induce thermal gradients that reveal the subsurface features of a subject. Washer et al. [16] found that the thermal IR imaging technology (IR thermography) can effectively show the subsurface delamination in reinforced concrete members as deep as five inches. The American Society for Testing and Materials method D4788 (ASTM D4788) [17] presents a way to utilize thermal IR imaging technology to identify delamination in bridge decks, with an accuracy of±5 percent.

Normally, the penetrating radar needs to contact the target surface but it can obtain information beneath the surface. The penetrating radar measures the pulsed electromagnetic energy transduced and reflected from materials with different dielectric properties. It is commonly used for the measurements of material strength, moisture condition, temperature, chemical content, and material frequency for the cement and aggregate type of materials [18]. The ground penetrating radar (GPR) utilizes the multi-frequency diffraction tomography technology to map a high resolution spatial image. Hing and Halabe [19] studied the effectiveness of applying the GPR to evaluate conditions of glass fiber-reinforced polymer (GFRP) bridge decks. The GPR was found to be able to effectively detect water-filled defects with the minimum size of 5 cm × 5 cm. It may be possible to be used for detecting the bottom flange defects of a GFRP bridge deck module at 10 cm deep.

Piezoelectric lead zirconatetitanate (PZT)-basedhealth monitoring has been used in structural subsurface damage detection applications recently. The PZT-based technology primarily depends on the special property of piezoelectric material, which produces electric charge when subjected to a stress/strain, or generates stress/strain when subjected to an electric field in its poled direction. Nair and Cai [20] discussed an acoustic emission method with PZT-based sensors for bridge monitoring and case studies in Louisiana, USA. Xu et al. [21] successfully detected debonding between concrete and steel for typical concrete-filled steel tube structural members using PZT patch based smart aggregate actuators and PZT patch sensors. Park et al. [22] proposed a wireless scheme to circumvent problems associated with using electrical wiring for PZT sensors. However, thePZT-based technologies mentioned above need to install PZT-based sensors and/or actuators on the target structure. More recently, air-coupled sensors, among which PZT based microphones is one type for high amplitude pressure range, are used for structural damage detection. Kee and Zhu [23] developed a method to estimate the concrete crack depth based on air-couple sensors. The non-contact or remote sensing approach based on air-coupled sensors eliminates coupling problems induced by sensor mounting and enables quick test setting-up.

Distancing and vibration sensing

Distancing technology, an important aspect of remote sensing technologies, has been experiencing rapid developments. Applications of distancing technologies, incorporating laser-light and radar, include detections of surface roughness, qualifications of material defects, and descriptions of spatial images [24,25].

Far-field radar systems have been developed to detect distances and damages regarding structures and structural materials [26]. The fundamentals of this technique are related to radar range and cross-range synthesis and phase interferometry. Pieraccini et al. [27] described a coherent radar system based on a stepped-frequency continuous wave in Ku band and its application on measuring deformations of a pedestrian bridge under live loads. Pieraccini et al. [28] discussed this radar system application on measuring vibration modes of a 200 m-long bridge. The radar system developed can generate and transmit microwaves, and the antennas on the radar can receive the coherent radar waves backscattered by the natural reflectors. It can simultaneously measure the dynamic response of several points of a structure. The static and dynamic responses of the structure can then be analyzed based on the received signal. The radar system has been applied in several other bridge tests as well [29,30].

Laser-based remote sensing technologies are getting the attention of civil engineers and researchers nowadays because of their measurement accuracy and instrumentation conveniences. Researchers have employed the laser measurement method to conduct on-site full scale testing on civil structures. The basics of the laser-based technology and its applications will be discussed in detail in a separate section of this paper.

Wireless sensing technologies

Sensors with wireless communication features

Wireless sensing technologies employ wireless communications. They can be wireless accelerometers, wireless strain sensors, wireless thermal sensors, and many other types of sensors with wireless communication functions. Compared to cabled sensors, wireless sensors can potentially significantly reduce implementation time and costs [31], thus facilitating deployment of a dense network of sensors for structural health monitoring (SHM) [32].

Early researches on wireless sensing technologies in structural testing started with Westermo and Thompson [33]. Their wireless sensor network consisted of three stain gauges which, along with a digital junction, were connected to a cellular modem that was set to receive incoming calls from a PC for data downloading or reprogramming. After their research, many other researchers were involved in the development of this new technology [34-45].

Wireless smart sensors

The wireless smart sensor network (WSSN) is the most used wireless sensing technology nowadays. The essential difference between a smart sensor and a standard integrated sensor is its intelligence capabilities, i.e., the on-board microprocessor. The microprocessor is typically used for digital processing, analog-to-digital or frequency-to-code conversions, calculations, and interfacing functions, which can facilitate self-diagnostics, self-identification, or self-adaptation (decision making) functions. Lynch and Loh [46] concluded that all wireless smart sensor hardwires generally had their designs delineated into four functional subsystems: sensing interface, computational core, wireless transceiver, and, for some, an actuation interface. In Lynch and Loh [46], WSSNs were categorized into academic prototypes and commercial platforms.

The first WSSN academic prototype was introduced by Straser and Kiremidjian [47] who proposed the design of a low-cost wireless modular monitoring system (WiMMS) for civil structures (Fig. 1(a)). After completing the design of the prototype, Straser and Kiremidjian used the Alamosa Canyon Bridge (New Mexico) to validate its performance.

Lynch et al. [48-50] proposed a WSSN prototype that emphasized the design of a powerful computational core (Fig. 1(b)). The completed wireless sensor was compact (10 cm × 10 cm × 5 cm) and relatively low power. To validate the performance of this prototype unit, it was used to measure the acceleration response of a five-story aluminum test structure mounted to a shaking table. Lynch et al. [51-53] updated their low energy WSSN prototype by proposing a dual-processor computational core design. Using the same bridge as Straser and Kiremidjian [47], the performance of this wireless smart sensing prototype was validated in the field. Finally, Lynch et al. [54] installed 14 of their wireless smart sensing unit prototypes to monitor the forced vibration response of the Geumdang Bridge in Korea. Through comparing the recorded time histories of the bridge using both monitoring systems (wireless and cable-based), the accuracy of the wireless sensing units was confirmed.

Beside Lynch, other researchers proposed WSSN prototypes during the same period [55-70]. In more recent years, most academic and industrial researchers in structural engineering have begun to explore WSSNs using commercial platforms. Rice and Spencer [71] divided the commercial WSSN platforms into two categories of products: 1) the fully-integrated wireless sensors, such as MicroStrain Technologies and 2) the open source hardware and software platforms, such as Crossbow Technologies (WeC, Rene2, MICA, MICA2, MICAz), Tmote Technologies, and Intel Technologies (Imote).

MicroStrain assembled its first WSSN from off-the-shelf electrical components resulting in a functionally rich platform [72]. When fully assembled, the wireless node is 9 cm × 6.5 cm × 2.5 cm and is powered by two 1.5 V lithium-ion batteries. Galbreath et al. [73] demonstrated the use of a MicroStrain wireless sensor network to monitor the performance of a steel girder composite deck highway bridge spanning the LaPlatte Riverin Shelburne, Vermont. Arms et al. [74] reported an improvement on the original wireless sensor and demonstrated the capabilities of their MicroStrain wireless strain and temperature modules with the installation of a network of sensors on the Ben Franklin Bridge in Philadelphia, Pennsylvania. Right now, MicroStrain offers a wide variety of wireless sensors including strain sensors, accelerometers, and generic analog input nodes.

The Crossbow Technologies have been under development since late 1990s by the University of California, Berkeley with the first prototype, called WeC, produced in 1999 and commercialized as the Rene Mote by Crossbow. In 2001, the WeC wireless smart sensor was modified to produce the Rene2 platform. Tanner et al. [75,76] presented the adoption of the Crossbow Rene2 Mote in a SHM project and discovered that two sensing channels could not be sampled simultaneously. Glaser [77] also identified some issues with the hardware design of the Rene2 Mote. To provide more program and data storage and to improve the flexibility of the wireless communication channel, Crossbow released the MICA Mote wireless smart sensing platform in early 2002 as the successor to the Rene2. Studies were performed for the MICA with different sensor boards, but some problems were found [78,79]. The Crossbow MICA2 platform (shown in Fig. 1(c)) was introduced in 2003 with a radio offering greater reliability. In 2004, the MICA2 adopted a 2.4 GHz IEEE802.15.4 compliant wireless transceiver and was called the MICAz. A number of researchers adopted the MICA2 and MICAz Mote in their researches [80-88].

Telos Mote (Tmote) Technologies were introduced in 2004 by researchers at Berkeley in an attempt to achieve a smart sensor module with lower power consumption [89]. Using this platform, researchers from Clarkson University developed a complete wireless sensing module called the wireless sensing solution (WSS) specifically for bridge monitoring using static and vibration-based approaches [90,91].

Imote technologies are the next-generation WSSN platforms resulting from a close collaboration between the University of California, Berkeley, and the Intel Research Laboratory [92]. The hardware design of the Imote was different from those of the MICA, MICA2, MICAz, and Telos Motes. It was designed with only a computational core and a wireless transceiver. Spencer et al. [93] reported that the availability of the IntelImote platform would potentially serve as a powerful tool for future wireless SHM systems. The second generation of Intel Mote, the Imote2, was introduced in 2005 [94,95]. The Imote2, shown in Fig. 1(d), is a high-performance wireless smart sensor platform. It possesses the features required for the demands of data intensive SHM applications [96]. Based upon the Imote2 platform, the Illinois Structural Health Monitoring Project (ISHMP) developed a complete WSSN package for civil engineering SHM applications. The Illinois WSSN package includes the hardware (sensor boards: SHM-A, SHM-W, and SHM-S), the middleware (embedded data processing strategies and algorisms), and the key software (ISHMP tool suite). Details of the package and its on-site applications are discussed in the following section.

Application of Illinois WSSN Package in Field Testing of Civil Structures

The Illinois Structural Health Monitoring Project (ISHMP) developed a complete WSSN package for the continuous and reliable monitoring of civil infrastructures using a dense network of smart sensors. The project has developed key sensor hardware (SHM-A, SHM-W, SHM-S sensor boards, etc.) interfaced with the Imote2 smart sensor platform and released a key software tool suite containing a library of services for SHM applications. Scalable communication configuration and data processing strategies have been embedded in the tool suite, which includes centralized, independent, and decentralized topologies [32].

Illinois WSSN package

The WSSN package employs two hardware configurations: a gateway node attached to the base station PC for sending commands and receiving wireless data from the network and battery-operated sensor nodes at remote locations to the base station. The gateway node consists of an Imote2 stacked on an IIB2400 interface board connected to the base station PC via a USB/UART port. Each sensor node consists of an Imote2, an IBB2400CA battery board, and a sensor board. To increase the communication range, both nodes are equipped with an Antenovagiga Nova Titanis 2.4 GHz external antenna. The sensor nodes are typically placed in environmentally hardened enclosures to endure harsh environments [97]. Three types of sensor boards can be employed: an SHM-A sensor board to measure vibration accelerations, an SHM-W sensor board to measure wind signals from an anemometer, and an SHM-S sensor board to measure member strains. The SHM-A sensor board integrates a tri-axial accelerometer, which has a range of±2 g. The noise levels of the accelerometer are 0.3 mg for the x- and y-axes and 0.7 mg for the z-axis. The SHM-A board also contains temperature, humidity, and light sensors [97]. The SHM-W board is developed by modifying the SHM-A board to have three external 0-5 V input channels and one acceleration channel. The wind speed (channel 1) and horizontal and vertical wind directions (channels 2 and 3) are measured through analog input interface connectors on the SHM-W board. The SHM-W board has also been adjusted so that the full range of the 0-5 V input is utilized, resulting in better resolutions for the wind data. The SHM-S sensor board measures member strains of the structure using a 350 ohm foil type 3-wires strain gauge. The positive wire (usually red) of the 3-wires strain gauge is connected to pin2, and the other two negative wires (usually white or black) are connected to pin3 and pin4. The SHM-S sensor board, which has no ADC, is designed for combined use with the SHM-A board through the basic connectors. The conditioned signal is linked to the 4th channel of SHM-A board.

The ISHMP services tool suite developed by Illinois SHM Project is the software that provides high-quality sensor data and transfers the data reliably to the base station via wireless communication across the sensor network. The tool suite components are categorized into four services packages and were discussed in detail in Rice et al. [71]. The four service packages are i) foundation services, ii) application services, iii) tools and utilities, and iv) continuous and autonomous monitoring services. In addition, a library of supporting numerical functions that are common to many SHM algorithms is provided, including fast Fourier transform (FFT), singular value decomposition (SVD), eigen value analysis, etc.

The foundation services implement the functionality required to support the application and other services. One of the primary purposes of the foundation services is to enable applications to acquire synchronized data from a network of sensors. The foundation services include network time synchronization (TimeSync), precise time-stamping of the data (UnifiedSensing), reliable communication of both short messages and long data records (ReliableComm), and a service that supports the reliable dissemination of network commands (RemoteCommand).

The application services provide the numerical algorithms necessary to implement system identification and SHM applications on the Imote2s and may also be used independently. For each application service, an application module to test the algorithm on both the PC and the Imote2 has been developed. The applications services include synchronized sensing (SyncSensing), correlation function estimation (CFE), the Eigensystem Realization Algorithm (ERA), Stochastic Subspace Identification (SSI), Frequency Domain Decomposition (FDD), and the Stochastic Damage Locating Vector method (SDLV).

The tools and utilities services support network maintenance and debugging. This category has essential services for full-scale monitoring as well as sensor maintenance. The key features of these services include a gateway node sensing tool (LocalSensing), a remote synchronized data measurement application (RemoteSensing), the data measurement and on-board computation to calculate the correlation functions for decentralized local groups (DecentralizedDataAggretation), automatic terminal tools to interact between the base station PC and a gateway node (imote2comm and autocomm), a command for checking a sensor node’s battery level (Vbat), a tool for assessing radio communication quality (TestRadio), a numerical service that simulates the identification of potential structural damage locations from injected acceleration data (TestServices), and many others.

The critical command of the continuous and autonomous monitoring service is AutoMonitor, an autonomous SHM network management application, combining RemoteSensing, ThresholdSentry, and SnoozeAlarm. SnoozeAlarm is a strategy that allows the network to sleep most of the time, thus improving energy efficiency and allowing long-term system deployment. To wake the network for an important event, the ThresholdSentry application defines a specified number of the sensor nodes as sentry nodes. Sentry nodes wake up at predefined times and measure short periods of acceleration or wind data. When the measured data exceeds a pre-defined threshold, sentry nodes send an alarm to the gateway node, which subsequently wakes the entire network for synchronized data measurement. In this way, AutoMonitor enables the automatic, continuous monitoring with reduced power consumption.

Full-scale applications

Rice et al. [98] conducted tests at the Stawamus Chief Pedestrian Bridge (Squamish, British Columbia, Canada)to demonstrate the functionality of the ISHMP tool suite and the performance of the SHM-A sensor board. The newly constructed Stawamus Chief Pedestrian Bridge, shown in Fig. 2(a), employed a striking arched suspension design that complimented the beauty of its surroundings over the Sea-to-Sky Highway in Vancouver, British Columbia. A series of tests were conducted to characterize the dynamic behavior of the arches with a specific focus on their susceptibility to wind-induced vibration. Four sensor nodes (Fig. 2(b)) and one gateway node were employed. The sensor nodes were installed at the top of the arches to measure the vibration along three axes. During the test, the arches were excited manually by pull-down tests and horizontal tug tests. In total, 22 60-s tests were conducted with a 50 Hz sample rate. In two cases, the tests did not complete as a result of incorrect user commands resulting in the freezing of one or more of the remote nodes, rendering them non-responsive to radio commands. In the other 20 cases, RemoteSensing with re-sampling was successful with excellent data quality and no data loss. The test results (Fig. 2(c)) showed very good time synchronization and good agreement in both lower and higher amplitude ranges. The measured fundamental frequencies matched closely those predicted by the finite element model. In all, including setup, sensor installation, damper engagement/disengagement, and teardown, the testing took approximately 6 h. These tests demonstrated that, in addition to long-term autonomous monitoring, the Illinois WSSN package could provide a quick and convenient method for conducting short-term structural testing.

Jang et al. [99] reported on the deployment and evaluation of the Illinois WSSN package on the Jindo Bridge (Fig. 3(a)), a cable-stayed bridge in South Korea with a 344 m main span and two 70 m side spans. The primary goals were to validate the performance of the SHM-A sensor board for full-scale testing, to validate the autonomous network operation software, to assess the time associated with network communication, and to investigate power consumption. In total, 70 Imote2 sensor nodes with SHM-A (or SHM-W) sensor boards, pictured in Fig. 3(b), were installed on the Jindo Bridge. To maintain the communication range and reduce the time to transmit the sensed data back to the base station, the network was divided into two sub-networks. The two sub-networks were set up to operate on two different radio channels, 20 and 25, to eliminate potential interference between them and to allow for simultaneous operation. The researchers reported that the hardware components, including the smart sensor nodes, the base stations, and an anemometer, had shown reliable performance during this deployment. The resolution of the SHM-A sensor board, 0.3 mg, was adequate to measure the bridge vibration, in the range of 5 to 30 mg. The wind speed and direction were successfully measured using the 3D ultra-sonic anemometer and the SHM-W sensor board at the mid span. The wind data were synchronized with vibration data measured by SHM-A sensor board. The two base stations had been functioning reliably for four months, enabling stable remote monitoring of the Jindo Bridge and communication with each gateway node. It was confirmed that the base station enclosure had successfully protected the computer from overheating and from the harsh environment. The enclosures for the smart sensor nodes had also performed well. The inside of the sensor enclosure was dry, and the temperature was acceptable. The magnet-based attachment proved to be an excellent solution for the Jindo Bridge because all sensor nodes remained attached firmly for four months. All software services were operating reliably as well. The AutoMonitor application had shown stable performance after appropriate optimization of the sensing and radio communication parameters. RemoteSensing and DecentralizedDataAggregation worked successfully with optimized communication parameters. One sample of recorded ambient vibration power spectral densities (PSD) is shown in Fig. 3(c). The PSD magnitude of two deck sensors at the mid span and the quarter span showed significant energy around 0.44, 0.66 and 1.03 Hz, implying the natural frequencies of the bridge, which agreed well with the previous measured data from the cabled sensors.

Huang conducted on-site full-scale system identification tests and FEM analyses on the historical Old Alton Bridge (a wrought iron truss bridge built in 1884 in Denton, Texas) using the Illinois WSSN package (not published yet). The goal of this research was to validate the WSSN package and to evaluate the effectiveness and accuracy of commonly used experimental system identification testing methods. The structure of the Old Alton Bridge consists of three spans: two are wrought iron beam approach spans, and the main span (shown in Fig. 4(a)) is a wrought iron Pratt truss with pinned connections at all joint locations. The truss is simply supported on four circular columns. The total length of the bridge is 145 feet. The dimensions of the wrought iron Pratt truss are 109 feet long in the east–west direction, 16 feet and 1 inch wide in the north–south direction, and 18 feet high vertically. The detailed truss members’ cross-section dimensions were taken directly from the on-site measurements by University of North Texas construction engineering senior students. Six sensor nodes (Fig. 4(b)) and one gateway were employed at the nodes along the bottom chords of the wrought iron truss (3 on each side of the deck) to record the vibration data of the bridge. An example of tested data in time domain appears in Fig. 4(c). The researcher of this study concluded that the Illinois WSSN package was a very powerful tool for on-site full-scaled civil structure testing. The sensitivity and resolution of the WSSN hardware are very good; it can even capture the small vibration of a single person jumping. Some further work on the software and documentation could be helpful for broader applications, such as more reliable Matlab version software, a separate data processing software package, and a more comprehensive user’s menu.

Applications of laser-based remote sensing for structural testing and inspection

Single point laser devices and scanning lasers are used for many structural sensing applications in SHM. Laser scanning devices are commonly used to measure spatial distance, motion, reflectivity, and other structural properties. Laser measurements are manipulated to form quantitative information about the condition, remaining lifetime, and effectiveness of structural elements. Light distancing and ranging (LiDAR) and scanning vibrometers are the laser-based testing and inspection tools that have been commonly used in civil engineering.

LiDAR-based sensing method and its applications

The most important mechanism of LiDAR is the projection of a signal and the processing of the reflected, or scattered, responses [25].The LiDAR system can be scanning or non-scanning systems. The system can directly measure amplitude (incoherent system) or perform phase sensitive measurements (coherent system). A commercially available Faro^TM scanner shown in Fig. 5 is a typical coherent system based on phase shift technology. The working flow of a phase shift LiDAR is described in the following. First, a transmitted laser beam is split and polarized; then, the newly polarized laser beam is directed through a rotating scanning element, to the target. The laser beam returns from the target and passes through a laser beam de-rotator placed in the signal path between the reflected signal receiver and the signal detector. After passing through the de-rotator, the returning beam is split and passed through a lens, in conjunction with a local heterodyning beam, to detect the wavelength shift frequencies of the laser beam. The system can then calculate the surface distance as a function of phase shift. The effective range of the scanning system depends on the maximum modulation length of the selected waves. An alternate mechanism of LiDAR is the time-of-flight technique, which has a similar working flow as that of the phase shift LiDAR. The LiDAR system emits pulses of laser light; then, the pulses are reflected from a scanned object. A highly accurate sensor measures the time-of-flight of the reflected laser pulse and records the data for processing. The time-of-flight sensors have the ability to measure multiple arrival times for an emitted pulse. The first and the final arrival time may indicate different portions of a scanned subject. Theoretically, the measuring limitation (range) for the time-of-flight technology depends on the emitted wave energy of the laser beam and the scanner’s time-delay measurement accuracy requirement [100,101].

The LiDAR scanning system can generate spatial data, with X-Y-Z coordinates, and reflective intensities of the immediate surroundings relative to the scanned object. The X-Y-Z coordinates construct a three dimensional point cloud. The recorded reflective intensity values are greater for the objects that are closer to the scanner. Modern LiDAR scanners are capable of collecting hundreds of thousands of data points per second, under appropriate conditions. A standard LiDAR scan procedure can last less than one minute or over an hour, depending on the resolution settings. An important factor in determining the best location for a LiDAR scanner is the LiDAR unit’s line of sight because the LiDAR systems utilize laser technology to measure the surface distance, and the transmitted laser wavelengths cannot penetrate to the subsurface of materials.

LiDAR has been considered an effective sensor. Many engineering agencies incorporate LiDAR as a sensing tool for terrain and site mapping to preserve geological and hydrological data. LiDAR is also an excellent tool for site management because of its ability to repeatedly measure site conditions and construction progress without impacting operation or construction practices [102]. Pollyea and Fairlay [103] utilized LiDAR to remotely sense surface roughness of soil and structures. They created an algorithm to align planar coordinates that expose surface roughness. The study concluded that this methodology was best for applications that aid fractural network models in geostatistics, creating maps for geochemical investigations, and similar structural applications. Olsen and Donahue [104] used LiDAR to map scour damages, collapsed structures, damaged port facilities, and erosions from the 2009 Samoan tsunami.

LiDAR techniques have been developed for bridge applications as well. Fuchs et al. [105,106] introduced LiDAR system for bridge testing. The system can measure displacement with the installation of light reflective targets. The system has been shown to be a useful tool in measuring unprepared surface movements for load testing. Based on a terrestrial LiDAR scanner, Teza et al. [107] developed an algorithm for automatically recognizing mass loss of concrete structures. The proposed method was based on the mean and Gaussian curvature computation of the object surface at each point and on the analysis of the curvature distributions. This technique was applied on a concrete bridge to determine the mass losses of a column and a T-shaped beam. The authors proposed that this method could advantageously substitute a direct visual inspection [107]. Based on a commercially available LiDAR, Faro^TM LS 880HE, a research team at the University of North Carolina, Charlotte developed techniques for the field inspection of bridges [108-112]. The LiDAR system was used during a load testing of a steel girder bridge [111] to measure the girder deflections under truck loads. During the testing, a baseline scan was first performed without a vehicle on the bridge. The bridge was then loaded with vehicles and rescanned with the LiDAR. By comparing these two scan data, girder deflections were derived (Fig. 6). Algorithms were developed by the research team for applying this LiDAR unit to quantify the concrete mass loss [110] and to identify the blast-caused damages of bridge structures (Fig. 7) [112].

Laser vibrometer and its applications

A laser vibrometer utilizes the interferometric behavior of optical transducers to measure vibration amplitude and frequency of an object’s surface. The laser Doppler vibrometers (LDV) integrate the optical heterodyne interferometers, guided by multiple scanning mirrors, to extrapolate single point Doppler shift changes from reflective light scattering. The Doppler shift changes due to the motion of the surface of a subject are then manipulated to form vibration information about the subject. A typical simplified working flow of the LDV is described as the following. First, the helium-neon laser or a laser beam emitted by a diode with a specific wavelength is guided to a polarization beam splitter by a refracting lens. Then, a portion of the beam travels to the subject/target through the imaging component of the LDV. The scattered beam eventually returns through the imaging component and is reflected to a photo detection unit where it is combined with the originally emitted laser wavelength to form the Doppler signal. Finally, a signal processor converts the Doppler signal into the modal vibrational velocity information. The analog signal processor uses analog frequency demodulation to convert Doppler frequencies into voltages proportional to the velocity of the subject’s vibration. The classic digital processor relies on the “fringe counting” method, where a digital counter measures the number of times an interferometric fringe passes through the origin of a photo detector [101,113].

After the results of the LDV measurement are collected, it is easy to interpret them in the format of velocity (m/s) in time domain. A two-column data sheet with one column as recording time in second and another column as velocity in meter per second is a common data format and it is not difficult for post processing for engineers. One of the processing for structural testing is to identify modal parameters of a structure based on Fourier transformation algorithms. Noise problems of the LDV associated with the phase displacement and/or velocity recordings were discussed in Castellini and Paone [114]. Misalignment of internal reflective components, scattering inaccuracy, and small vibrations within the vibrometer unit itself are the culprits of this malign phase displacement. Johansmann et al. [115] reported that the velocity noise increased proportionally to the frequency, whereas displacement noise was constant.

The LDV have many advantages compared to contacted accelerometer sensors. It can measure a target that is difficult to get access for sensor installation; and it is useful for measurement of a subject that is too hot to attach a physical transducer. The LDV unit does not burden the test subject with any mass, which allows the unit to measure the functional properties of exceedingly small elements. A typical LDV measures the surface vibrations and does not detect or penetrate any subsurface material layers [116]. Structural applications of LDV run the gamut of testing and inspection. Ghoshal et al. [117] discussed using a scanning vibrometer to quantify the vibration response and structural defects of wind turbine blades. Martarelli et al. [118] studied problems and uncertainties associated with using the LDV in experimental modal testing. Siringoringo and Fujino [119] reported a study applying a LDV for system identification and structural damage detection with ambient vibration tests. Bolted lap joint plates were tested in the laboratory to verify the proposed method of modal identification and modal-based damage detection. Two laser systems, one for reference and one scanning laser, were used in the laboratory testing of bolted plates. Lee and Shinozuka [120] developed a vision-based system for measure bridge displacement; during field verification tests, the LDV was used as a comparative measurement to justify steel box girder bridge deflection values obtained by analyzing digital video camera imaging. Sohn et al. [121] created an automated method for detecting façade delamination and debonding using wavelength images from a LDV. In conjunction with a piezoelectric transducer to verify results, the researchers found that using different wave filters helped differentiate the vibrometer results between the delamination and the debonding. Nassif et al. [122] performed field tests on a bridge in order to compare LDV measurements with traditional contact measurements from the linear variable differential transducer and geophone sensors. They concluded that the LDV can effectively measure bridge deflections and vibrations.

The commercial LDV (Ometron 8330) has been used to study a high performance skew steel girder bridge and to construct a base line finite element model for the bridge [111]. Vibrations of the bridge under normal traffic were measured. Natural frequencies of the bridge were obtained from those test data. The finite element model of the bridge was verified partially through natural frequency comparison. Using another commercial product, PDV-100 from the Polytech^TM, structural vibration measurements were performed in the newly constructed structural laboratory on the Tongji University campus, Shanghai, China (Fig. 8). During the test, one continuous reinforced concrete girder was excited by crane traveling on its track. The LDV was positioned to point to the bottom of the reinforced girder. Vibrations of the girder were recorded for structural dynamic analysis. Vibrations of a large-scale prestressed concrete reaction wall were measured during the test as well. In addition to this laboratory test application, the PDV-100 has been employed in a vibration monitoring of a typical city viaduct (Fig. 9). The LDV was found to be easily used in all those full scale tests (not published yet).

Conclusions

Field testing and full-scale experiments are commonly conducted by engineers and researchers to study civil structures. Accelerometers, strain gages, displacement transducers, etc. are commonly used sensors in structural testing. This paper introduces newly developed sensing techniques and their applications in civil engineering through a state-of-the-art review. It is expected to help engineers and researchers with sensor selections during structural testing by getting familiar with these sensing technologies.

With technological advancement, novel sensors such as wireless sensors and remote sensing methods have shown advantages in full-scale structural tests. Therefore, this study primarily focused on the background and applications of the wireless sensors and remote sensors. Wireless smart sensors can not only communicate and transmit data wirelessly but also have on-board intelligence capabilities. The history of the wireless sensors development was reviewed in the paper; and a representative of the most advanced wireless smart sensing technology, the Illinois WSSN package, was described. From three bridge case studies, it is obvious that the package is a very powerful tool for the on-site full-scaled civil structure testing. Remote sensing techniques such as LiDAR and laser Doppler vibrometer have been proved as effective technologies in structural testing for scenarios when it is hard to get access to the structure in field. The theoretical backgrounds and civil engineering applications were presented in detail in the paper.

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