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This article introduces the use of emerging augmented reality technology to enable the next generation of structural infrastructure inspection and awareness. This work is driven by the prevalence of visual structural inspection. It is known that current visual inspection techniques have multiple sources of variance that should be reduced in order to achieve less ambiguous visual inspections. Emerging augmented reality tools feature a variety of sensors, computation, and communication resources that can enable relevant structural inspection data to be collected at very high resolution in an unambiguous manner. This work shows how emerging augmented reality tools can be used to greatly enhance our ability to capture comprehensive, high-resolution, three-dimensional measurements of critical infrastructure. This work also provides detailed information on the software architecture for augmented reality structural inspection applications that helps meet the goals of the framework. The fact that the framework is designed to accommodate the considerations associated with high-consequence infrastructure implies that it is also comprehensive enough to be applied to less hazardous but still high-value infrastructure such as bridges, dams, and tunnels. Augmented reality has great potential to enable the next generation of smart infrastructure, and this work focuses on addressing how augmented reality can be leveraged to enable the next generation of structural awareness for high-consequence, long-lifespan structures.

The need for novel, minimally invasive diagnostic, prognostic, and therapeutic biomedical devices has garnered increased interest in recent years. Microneedle (MN) technology has stood out as a promising new method for drug delivery, as well as extraction of interstitial fluid (ISF). ISF comprises a large portion of the extracellular fluid in living organisms yet remains inadequately characterized for clinical applications. Current MN research has focused on the fabrication of needles with different materials like silicone, carbon, and metals. However, little effort has been put forth into improving MN holders and patches that can be used with low cost MNs, which could effectively change how MNs are attached to the human body. Here, we describe different 3D-printed MN holders, printed using an MJP Pro 2500 3D printer, and compare the ISF extraction efficiencies in CD Hairless rats. We varied design parameters that may affect the skin-holder interface, such as throat thickness, tip curvature, and throat diameter. MN arrays, with insertion depths of 1500 μm, had extraction efficiencies of 0.44 ± 0.35, 0.85 ± 0.64, 0.32 ± 0.21, or 0.44 ± 0.46 µl/min when designed with flat, concave, convex, or bevel profile geometries, respectively. Our results suggest ISF extraction is influenced by MN holder design parameters and that a concave tip design is optimal for extracting ISF from animals. The future direction of this research aims to enable a paradigm in MN design that maximizes its efficiency and engineering performance in terms of volume, pressure, and wearability, thereby automatizing usage and reducing patient intervention to ultimately benefit remote telemedicine.

Fatigue constitutes a critical limit state affecting the safety of civil infrastructure. Under repetitive loading, structural members are susceptible to fatigue cracking under stresses much lower than the yield strength of the material. In this study, computer vision-based fatigue crack detection using a short video stream taken from a nonstationary camera is presented. Videos taken from a hand-held camera, or an unmanned aerial vehicle (UAV) contain two types of movements: 1) true object movements, and 2) unwanted camera movement due to hand shaking or UAV hovering. In most vision-based structural health monitoring research, feature-based motion compensation techniques are used that require manual selection of fixed objects in the video for feature point selection. Feature point selection from true moving objects in the video could produce inaccuracy in video stabilization. In this study, we propose to use hierarchical model-based motion estimation for global motion compensation, which does not require manual selection of fixed objects. First, we construct a pyramid of target and reference images and then estimate motion from top to bottom of the pyramid while accumulating the geometric transformation, by which the camera movement can be removed. Then, we detect salient feature points in the region of interest and track the motion of feature points throughout the video using the Kanade-Lucas-Tomasi (KLT) feature tracking algorithm. Subsequently, a crack detection, and localization algorithm is applied to search for differential point movements caused by fatigue crack opening and closing. To evaluate effectiveness of the proposed method, a laboratory experiment was conducted on a C(T) specimen with an in-plane fatigue crack. Results show that proposed method was able to effectively compensate the camera motion and detect the presence of the fatigue crack.

A proper understanding of the complex physics associated with nonlinear dynamics can improve the accuracy of predictive engineering models and provide a foundation for understanding nonlinear response during environmental testing. Several researchers and studies have previously shown how localized nonlinearities can influence the global vibration modes of a system. This current work builds upon the study of a demonstration aluminum aircraft with a mock pylon with an intentionally designed, localized nonlinearity. In an effort to simplify the identification of the localized nonlinearity, previous work has developed a simplified experimental setup to collect experimental data for the isolated pylon mounted to a stiff fixture. This study builds on these test results by correlating a multi-degree-of-freedom model of the pylon to identify the appropriate model form and parameters of the nonlinear element. The experimentally measured backbone curves are correlated with a nonlinear Hurty/Craig-Bampton (HCB) reduced order model (ROM) using the calculated nonlinear normal modes (NNMs). Following the calibration, the nonlinear HCB ROM of the pylon is attached to a linear HCB ROM of the wing to predict the NNMs of the next-level wing-pylon assembly as a pre-test analysis to better understand the significance of the localized nonlinearity on the global modes of the wing structure.

The quality assurance of constructing reinforced concrete (RC) structures in compliance with their design plays a key role in the durability, serviceability, and sustainability of the built RC elements. One area of concern in the quality control of constructing RC structures is examining the position and dimension of the rebars before pouring fresh concrete. Currently, this is accomplished by visual inspection and individually by hand with limited time available between construction stages. Over the past decades, structural health and monitoring during the construction period has applied remote sensing technologies. However, little research has focused on the use of such technologies to inspect and evaluate rebar placement prior to concrete pouring as quality control. In this study we develop an algorithm that facilitates inspecting the positions of rebars and the cover of concrete using a new-generation low-cost RGB-D sensor to find incorrect rebar placement. The proposed method is evaluated using a typical 5 × 5 two-layer rebar cage in the laboratory by comparing the proposed technique with traditional inspection methods. The results show that the RGB-D sensor can achieve cost-effective inspection for rebar spacing and clearance with an acceptable tolerance. The evaluation of rebar spacing results shows that the maximum standard deviation for rebar spacing is 0.34 inch (8.64 mm) between longitudinal rebar 2 and 3, which is the same as the rebar construction and traditional tape measurement results. The concrete cover estimation results show that the maximum standard deviation for rebar cage concrete cover is 0.19 inch (4.83 mm) for longitudinal rebar 3. The issues of new RGB-D sensor scan settings and the test results will be helpful for practitioners in improving construction quality.

Multi-Input-Multi-Output (MIMO) testing is challenging due to the need to estimate inputs required to achieve desired responses at multiple locations on the test article of interest. Errors in the estimation of inputs can come from either the forward process (measurement of the Transfer Function (TRF) matrix that captures the input-output relationship) or the inverse process requiring the inversion of a rectangular matrix using the Moore-Penrose Pseudo-Inverse method.

Crack occurrence and propagation are among critical factors that affect the performance and lifespan of civil infrastructures such as bridges. Consequently, numerous crack detection and measurement methods have been proposed and developed in the recent decades in the areas of Structural Health Monitoring and non-destructive testing. Many novel technologies have emerged with the potential to overcome the limitations of the presented techniques of crack detection and characterization. Crack detection and characterization method used in this research lies in supplementing human visual inspection capabilities in a systematic manner through an appropriate level of automation. The Augmented Reality (AR) tool developed in this project allows a user to perform tasks in a real-world environment while visually receiving supplementary 3D computer-generated information to support the tasks. More specifically, we developed a crack detection/characterization tool in this research and deployed it in Microsoft HoloLens smart glasses. This AR tool provides the user with automatic data collection capability through AR headset camera and is a means of hands-free data sharing for inspectors while conducting their normal inspection. We conducted several laboratory and field experiments by which we evaluated the effectiveness of the developed crack detection and measurement system. The result confirm that the AR tool devised in this project has the potential to help the inspection process in terms of time, comfort and accuracy., Tran-SET Project: 20STUNM33

Railroad bridge inspectors are interested in measuring the maximum total transverse displacement of railroad bridges under trains, but these values are generally not easy to obtain in the f...

The 4th industrial revolution started in 2016 and referred to a new phase in the industrial revolution. One of the most significant technological evolvements during the 4th industrial revolution is Augmented Reality (AR) technology. AR superimposes interactional virtual objects/images to real environments. Because of the interaction and see-through characteristics, AR is better applied to engineering than Virtual Reality (VR). The application of AR in civil infrastructure can avoid artificial mistakes, improve efficiency, and saves budget. This article reviews AR applications in civil infrastructure, focusing on research studies in the latest five years (2016–2020) and their milestone developments. More than half of the AR research and implementation studies have focused on the construction domain in the last five years. Researchers deploy AR technologies in on-site construction to assist in discrepancy checking, collaborative communication, and safety checking. AR also uses building information models (BIMs) to produce detailed 3D structural information for visualization. Additionally, AR has been studied for structural health monitoring (SHM), routine and damage detection, energy performance assessment, crack inspection, excavation, and underground utility maintenance. Finally, AR has also been applied for architecture design, city plan, and disaster prediction as an essential part of smart city service. This article discusses the challenges of AR implementation in civil infrastructure and recommends future applications.

Infrastructures such as aerial tramways carry unique traffic operations and have specific maintenance requirements that demand constant attention. It is common that old structures lack any type of automatization or monitoring systems, relying only on human judgment. Owners are interested in implementing techniques that assist them in making maintenance decisions, but are reluctant to invest in expensive and complex technology. In this study, researchers discussed with the owners different options and proposed a sustainable and cost-efficient solution to monitor the Sandia Peak Tramway operations with just two strategically located acceleration sensors. To maximize the success options researchers worked with the owners and developed a sensor that satisfied their needs. A Low-cost Efficient Wireless Intelligent Sensor 4—Outdoors (LEWIS 4) was developed, tested and validated during the experiment. Two solar-powered units were installed by the tramway staff and recorded data for three days. When retrieved, researchers analyzed the data recorded and concluded that with only two sensors, the acceleration data collected were sufficient to determine the position and location of the tramway cars. It was also found that the sensor on the tower provides data about the cable–tower interaction and the forces caused by the friction on the system, this being a critical maintenance factor. This work summarizes a methodology for infrastructure owners consisting of guidelines to design a sustainable and affordable monitoring approach that is based on the design, development and installation of low-cost sensors.

Emerging technologies (ETs) are increasingly becoming more accessible, and as they make their way into the field will be an integral part of the engineering community’s work that impacts fu...

Researchers conduct dynamic experiments to quantify changes in frequencies and mode shapes of interest for the safety and performance of structures. Even with sensor data and advanced computing techniques, valuable information of the experiment is always best observed with physical presence during the experiment. However, as of today, there exists no method for engineers to observe the dynamics experiments in the physical field and conduct dynamic analysis real-time. This paper describes a software application that conducts dynamic analysis in order to resolve this issue. The research consists of theoretical and experimental modal analyses of a cantilever beam using Augmented Reality (AR). The physical experiment involves a continuous aluminum beam attached to a shake table. First, a theoretical modal analysis is performed using Euler-Bernoulli equations in order to determine the modal parameters of this beam. A software application is built for the AR headset which contains a scaled hologram of the beam and depicts the first three theoretical mode shapes. This hologram is anchored to shake table to superimpose the physical beam. Using sinusoidal vibrations, the shake table can then excite the experimental mode shapes. The hologram and the actual beam are overlaid to compare real time structural data with theoretical mode shapes. By incorporating AR visualization of mode shapes, this prototype enables a new interface for structural analysis. Use of AR in the field of structural dynamics can expand human cognition by introducing a new interface for humans to interact with.

Multi-Input-Multi-Output (MIMO) testing enables more realistic replication of true environment in dynamic testing of structures. MIMO infers inputs from the desired responses at multiple locations on the test article of interest. Errors in the estimation of inputs include but are not limited to: output measurement errors and errors in the Transfer Function (TRF) matrix that captures the input-output relationship. A systematic study involving the measurement of acceleration, control system and system response were undertaken to investigate MIMO actuation of a fixed-end cantilever beam. The response of the beam was also modeled using closed-from equations and finite element method. Various actuation schemes were used to explore the effect of location of actuators in the context of the modal response of the beam. The MIMO input derivation was modeled using the Moore-Penrose Pseudo-Inverse of a rectangular Transfer Function matrix. The results of this study quantify the sources of uncertainty in MIMO vibration testing, specifically the errors due to equipment, sensor calibration, and actuator effects on the beam. The conclusions obtained from the cantilever beam used on this study can be extrapolated to more complex geometries.

Collisions between overheight semitrailer trucks with low-clearance railroad bridges are one of the leading causes for railroad traffic interruptions on bridges in the United States. Curren...

According to railroad managers, displacement of railroad bridges under service loads is an important parameter in the condition assessment and performance evaluation. However, measuring bridge responses in the field is often costly and labor-intensive. This paper proposes a low-cost, efficient wireless intelligent sensor (LEWIS) platform that can compute in real-time the dynamic transverse displacements of railroad bridges under service loads. This sensing platform drives on an open-source Arduino ecosystem and combines low-cost microcontrollers with affordable accelerometers and wireless transmission modules. The proposed LEWIS system is designed to reconstruct dynamic displacements from acceleration measurements onboard, eliminating the need for offline post-processing, and to transmit the data in real-time to a base station where the inspector at the bridge can see the displacements while the train is crossing, or to a remote office if so desired by internet. Researchers validated the effectiveness of the new LEWIS by conducting a series of laboratory experiments. A shake table setup simulated transverse bridge displacements measured on the field and excited the proposed platform, a commercially available wired expensive accelerometer, and reference LVDT displacement sensor. The responses obtained from the wireless system were compared to the displacements reconstructed from commercial accelerometer readings and the reference LVDT. The results of the laboratory experiments demonstrate that the proposed system is capable of reconstructing transverse displacements of railroad bridges under revenue service traffic accurately and transmitting the data in real-time wirelessly. In conclusion, the platform presented in this paper can be used in the performance assessment of railroad bridge network cost-effectively and accurately. Future work includes collecting real-time reference-free displacements of one railroad bridge in Colorado under train crossings to further prove LEWIS’ suitability for engineering applications.

When constructing Reinforced Concrete (RC) components, rebar spacing must be inspected in the field before pouring concrete. The traditional inspection of rebar spacing: (1) depends on the experience of the individual inspector; (2) is limited to a pass/fail outcome; and (3) is in general conducted with limited time. This paper presents a recognition algorithm that automatically identifies the rebar position using LiDAR. The result is an automatic Rebar Layout Quality Index (RLQI). Researchers collected LiDAR data from a real rebar mat, compared it with the design drawings, and obtained the RLQI for both top and bottom rebar mats. The authors also quantified the quality of the structural construction with a new flexural relative moment strength index. This methodology has the potential to reduce inspection time and increase the reliability of inspections through an objective assessment of rebar spacing, by creating an as-built record of actual rebar location in the structure.

Infrastructure spending is a major component of railroad annual budgets and therefore must be prioritized to ensure both safety and line capacity at all times. Current inspection priorities for railroads include early detection of structural deterioration, including concrete and timber, caused by the recent increase of car load capacities. Railroad bridge inspectors conduct tap testing to detect the deterioration of concrete and timber acoustically. However, measuring railroad bridge condition in the field is a challenging task and, in general, is solely based on the inspector’s experience. The results of this research describe the development, validation, and testing of a remote tap testing device that can be deployed by an aerial robot. The new tap testing device can remotely impact the surface, record the sounds of those impacts, and post-process the data to perform replacement prioritization. Tapping acoustics were analyzed both in the time and frequency domains. Principal component analysis of the data enabled the clustering of the different sets of data collected from different concrete and timber surfaces. The results quantify for the first time structural tap testing data collected with a tap testing mechanism built for deployment by an aerial robot. The goal of this work is to enable cost-effective, safer and sustainable upgrade prioritization of railroad bridge inventories.

Displacements of railroad bridges under service loads are important parameters in assessing bridge conditions and risk of train derailment, according to railroad bridge managers. Measuring bridge responses in the field is often expensive and challenging due to the high costs of sensing equipment. Consequently, railroad bridge managers typically rent or subcontract field measurements to others or choose not to collect dynamic data in the field and make visual inspections. This article studies the use of a low-cost data acquisition platform to measure reference-free dynamic displacements of railroad bridges by combining low-cost microcontrollers and accelerometers. Researchers used off-the-shelf systems to measure accelerations and reconstructed reference-free displacements from several railroad bridge crossing events by running trains with different levels of serviceability in the laboratory. The results obtained from the proposed low-cost sensors were compared with those of commercial sensing equipment. The results show that low-cost sensors and commercial sensing systems have comparable accuracy. The results of this study show that the proposed platform estimates reference-free displacements with a peak error between 20% and 30% and a root mean square error between 10% and 20%, which is similar to commercial structural health monitoring systems. The proposed low-cost system is approximately 300 times less expensive than the commercial sensing equipment. The ultimate goal of this research is to increase the intelligent assessment of bridges by training owners and inspectors to collect dynamic data of their interest with their own resources.

Railroad bridges in North America are an integral but aging part of the railroad network and are typically only monitored using visual inspections. When quantitative information is required for assessment, railroads often monitor bridges using accelerometers. However without a sensor to directly measure displacements, it is difficult to interpret these results as they relate to bridge performance. Digital Image Correlation (DIC) is a non-contact sensor technology capable of directly measuring the displacement of any visible bridge component. In this research, a railroad bridge was monitored under load using DIC and accelerometers. DIC measurements are directly compared to serviceability limits and it is observed that the bridge is compliant. The accelerometer data is also used to calculate displacements which are compared to the DIC measurements to assess the accuracy of the accelerometer measurements. These measurements compared well for zero-mean lateral data, providing measurement redundancy and validation. The lateral displacements from both the accelerometers and DIC at the supports were then used to determine the source of lateral displacements within the support system.

Railroad network is the main freight transportation systems in the US, with 40 percent of the total freight traffic being transported by rail. More than 50% of the railroad bridges in North America are over 100 years old. Nowadays, train loadings on bridges are higher than what they were designed to be in the past. Railroad bridges have been capable to adapt to new loads and successfully ensure safe operations by proper maintenance and repair since their inception, including continuous monitoring to proactively prevent any disruption in the transportation. Railroad managers have indicated that measuring the transverse displacement of the bridges under train crossing events can be used to quantify their proper performance and to inform management or repair decisions with objective data. Traditionally, Linear variable differential transformer (LVDTs) and other types of sensors are used to obtain the displacement data of the train, but these methods need human forces for their installation. Additionally, some parts of the bridges are not accessible or easy to reach for these types of inspections. Thus, aerial and remote sensing have become more popular. However, current methods for using Unmanned Aerial Vehicles (UAVs) to monitoring railroad bridges rely on cameras or lasers which are either technologically complex or economically unaffordable at large scale. This paper explains the development of a new hybrid sensing method to obtain total dynamic displacement using UAV. Sensors collecting displacements include lasers, cameras, and dynamic 3D cloud data acquisition. Displacement of the bridge is collected with the use of a low-cost laser sensor on the UAS with a camera attached to it. Then, the new algorithm extracts the hovering of the UAS and correct the laser displacement to obtain the displacement of the bridge under load. The results are compared to LVDT displacement for validation. The paper also identifies the priorities of North American Railroad owners regarding how the proposed technology can be implemented for new cost-effective management of railroad bridges maintenance.

This work presents a novel, comprehensive framework that leverages emerging augmented reality headset technology to enable smart nuclear industrial infrastructure that a human can easily interact with to improve their performance in terms of safety, security, and productivity. Nuclear industrial operations require some of the most complicated infrastructure that must be managed today. Nuclear infrastructure and their associated industrial operations typically features stringent requirements associated with seismic, personnel management (e.g., access control, equipment access), safety (e.g., radiation, criticality, mechanical, electrical, spark, and chemical hazards), security (cyber/physical), and sometimes international treaties for nuclear non-proliferation. Furthermore, a wide variety of manufacturing and maintenance operations take place within these facilities further complicating their management. Nuclear facilities require very thorough and stringent documentation of the operations occurring within these facilities as well as maintaining a tight chain-of-custody for the materials being stored within the facility. The emergence of augmented reality and a variety of Internet of Things (IoT) devices offers a possible solution to help mitigate these challenges. This work provides a demonstration of a prototype smart nuclear infrastructure system that leverages augmented reality to illustrate the advantages of this system. It will also present example augmented reality tools that can be leveraged to create the next generation of smart nuclear infrastructure. The discussion will layout future directions of research for this class of work.

Currently, over half of the U.S.&rsquo, s railroad bridges are more than 100 years old. Railroad managers ensure that the proper Maintenance, Repair, and Replacement (MRR) of rail infrastructure is prioritized to safely adapt to the increasing traffic demand. By 2035, the demand for U.S. railroad transportation will increase by 88%, which indicates that considerable expenditure is necessary to upgrade rail infrastructure. Railroad bridge managers need to use their limited funds for bridge MRR to make informed decisions about safety. Consequently, they require economical and reliable methods to receive objective data about bridge displacements under service loads. Current methods of measuring displacements are often expensive. Wired sensors, such as Linear Variable Differential Transformers (LVDTs), require time-consuming installation and involve high labor and maintenance costs. Wireless sensors (WS) are easier to install and maintain but are in general technologically complex and costly. This paper summarizes the development and validation of LEWIS2, the second version of the real-time, low-cost, efficient wireless intelligent sensor (LEWIS) for measuring and autonomously storing reference-free total transverse displacements. The new features of LEWIS2 include portability, accuracy, cost-effectiveness, and readiness for field application. This research evaluates the effectiveness of LEWIS2 for measuring displacements through a series of laboratory experiments. The experiments demonstrate that LEWIS2 can accurately estimate reference-free total displacements, with a maximum error of only 11% in comparison with the LVDT, while it costs less than 5% of the average price of commercial wireless sensors.

This paper investigates a novel approach for attitude control of two satellites acting as a virtual telescope. The Virtual Telescope for X-Ray Observations (VTXO) is a mission exploiting two 6U CubeSats operating in a precision formation. The goal of the VTXO project is to develop a space-based, X-ray imaging telescope with high angular resolution precision. In this scheme, one CubeSat carries a diffractive lens and the other one carries an imaging device to support a focal length of 100 m. In this mission, the attitude control algorithms are required to keep the two spacecraft in alignment with the Crab Nebula observations. To meet this goal, the attitude measurements from the gyros and the star trackers are used in an extended Kalman filter, for a robust hybrid controller, and the energy and accuracy of attitude control are optimized for this mission using neural networks and multi-objective genetic algorithm.

There is a need for rapid and responsive infrastructure repair and construction after natural disaster events such as hurricanes, wildfires, and tornadoes. These natural disasters often shut down basic infrastructure systems, including roads, bridges, water supply, and power supply, as experienced recently in several Region 6 states as well as in other states around the country. Accelerated construction practices are often used in these situations to speed up the traditional, and often slow, project delivery process. However, after a natural disaster, several and different types of transportation infrastructure components are in need of inspection, rehabilitation or reconstruction, and transportation agencies are challenged with the task of prioritizing these accelerated projects. This paper studied the current practices and institutional barriers to identify the critical decision criteria and to develop a conceptual model for prioritizing needs for accelerated construction after disaster events, specifically hurricanes and flooding which commonly affect Region 6.

Measurement of bridge displacements is important for ensuring the safe operation of railway bridges. Traditionally, contact sensors such as Linear Variable Displacement Transducers (LVDT) and accelerometers have been used to measure the displacement of the railway bridges. However, these sensors need significant effort in installation and maintenance. Therefore, railroad management agencies are interested in new means to measure bridge displacements. This research focuses on mounting Laser Doppler Vibrometer (LDV) on an Unmanned Aerial System (UAS) to enable contact-free transverse dynamic displacement of railroad bridges. Researchers conducted three field tests by flying the Unmanned Aerial Systems Laser Doppler Vibrometer (UAS-LDV) 1.5 m away from the ground and measured the displacement of a moving target at various distances. The accuracy of the UAS-LDV measurements was compared to the Linear Variable Differential Transducer (LVDT) measurements. The results of the three field tests showed that the proposed system could measure non-contact, reference-free dynamic displacement with an average peak and root mean square (RMS) error for the three experiments of 10% and 8% compared to LVDT, respectively. Such errors are acceptable for field measurements in railroads, as the interest prior to bridge monitoring implementation of a new approach is to demonstrate similar success for different flights, as reported in the three results. This study also identified barriers for industrial adoption of this technology and proposed operational development practices for both technical and cost-effective implementation.

Wireless sensor networks (WSN) are used by engineers to record the behavior of structures. The sensors provide data to be used by engineers to make informed choices and prioritize decisions concerning maintenance procedures, required repairs, and potential infrastructure replacements. However, reliable data collection in the field remains a challenge. The information obtained by the sensors in the field frequently needs further processing, either at the decision-making headquarters or in the office. Although WSN allows data collection and analysis, there is often a gap between WSN data analysis results and the way decisions are made in industry. The industry depends on inspectors&rsquo, decisions, so it is of vital necessity to improve the inspectors&rsquo, access in the field to data collected from sensors. This paper presents the results of an experiment that shows the way Augmented Reality (AR) may improve the availability of WSN data to inspectors. AR is a tool which overlays the known attributes of an object with the corresponding position on the headset screen. In this way, it allows the integration of reality with a virtual representation provided by a computer in real time. These additional synthetic overlays supply data that may be unavailable otherwise, but it may also display additional contextual information. The experiment reported in this paper involves the application of a smart Strain Gauge Platform, which automatically measures strain for different applications, using a wireless sensor. In this experiment, an AR headset was used to improve actionable data visualization. The results of the reported experiment indicate that since the AR headset makes it possible to visualize information collected from the sensors in a graphic form in real time, it enables automatic, effective, reliable, and instant communication from a smart low-cost sensor strain gauge to a database. Moreover, it allows inspectors to observe augmented data and compare it across time and space, which then leads to appropriate prioritization of infrastructure management decisions based on accurate observations.