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Structural Health Monitoring (SHM) is the automation of the condition assessment process of an engineered system. When applied to geometrically large components or structures, such as those found in civil and aerospace infrastructure and systems, a critical challenge is in designing the sensing solution that could yield actionable information. This is a difficult task to conduct cost-effectively, because of the large surfaces under consideration and the localized nature of typical defects and damages. There has been significant research efforts in empowering conventional measurement technologies for applications to SHM in order to improve performance of the condition assessment process. Yet, the field implementation of these SHM solutions is still in its infancy, attributable to various economic and technical challenges. The objective of this Roadmap publication is to discuss modern measurement technologies that were developed for SHM purposes, along with their associated challenges and opportunities, and to provide a path to research and development efforts that could yield impactful field applications.

Imposing a boundary condition on a structure can significantly alter its dynamic properties. However, sometimes the specifics of the new boundary conditions are not known. When the effects of a boundary condition are uncertain or there is not enough information, engineers need to excite the complex structure to obtain these modified properties. In order to experimentally obtain the new properties, engineers need multiple experiments and many outputs for interpolation in order to sufficiently represent the entire structure. The researchers attached a stinger to a cantilever beam, acting as a new transverse restraint of unknown properties. This paper presents a conversion expression that predicts the dynamic behavior of any point in the system with the new boundary condition. This expression relies only on one impact hammer experiment with one output and the model of the stinger-free cantilever beam, referred to as the simple structure. Researchers estimated the Transfer Function (TRF) of the beam and compared it with an experimentally measured TRF to validate the method. The mean absolute error of the estimated TRF compared to the experimental TRF is 1.99 dB. This demonstrates the use of the proposed method for estimating unmeasured TRFs in a system with an uncertain boundary condition using a single input, single output (SISO) test and a model of the simple structure.

Bridge construction inspections require quantitative measurements and location information. The conventional approach is visual inspection, which in general, is rather time-consuming, expensive due to traffic closure, subjective, and needs special access. Therefore an automated rebar layout detection algorithm was developed to quickly extract quantitative rebar layout information from the LiDAR data. This systematic method can automatically cluster the bridge elements from a 3D point cloud by using LiDAR-equipped UAS data collection and unsupervised machine learning techniques. A new automated inspection system using a LIDAR-equipped UAS can eventually if developed and tested be more reliable as well as less expensive. In the future, if it can be automatized, it can be implemented to simplify the complexity of inspections. The authors developed a platform to mount the camera, sonar laser, and DAQ on the UAS and remotely controlled the data collection operation. Additionally, an algorithm was developed which can automatically obtain the geometric information of the rebar. The proposed automated RGBD-equipped UAS system was developed, fabricated, and tested in the Balloon Fiesta Park on a simulated bridge deck at different heights and with different UAS motions to obtain the best distance, speed, and motion for real construction field. The authors also conducted an outdoor experiment in a construction field at White Rock to validate the capability of the proposed system on the real site with vertical rebar and the challenges of the real construction site. The result confirmed that the LIDAR-equipped UAS system has the potential to help the inspection process in terms of time, accuracy, safety, and generating a permanent record of the inspection. Bridge construction information collected by LiDAR-equipped UAS technology can eventually provide bridge managers with transparent condition assessment and one-step decision-making support through quantitative measurement combined with 3D visualization to facilitate repair planning that can greatly facilitate maintenance., Tran-SET Project: 21STUNM04

Railroad bridge inspection manuals recommend measuring bridge displacements under train crossing events. Traditional displacement measurement methods require humans climbing the infrastruc...

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.

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.

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.

Rockfalls are a hazard for the safety of infrastructure as well as people. Identifying loose rocks by inspection of slopes adjacent to roadways and other infrastructure and removing them in advance can be an effective way to prevent unexpected rockfall incidents. This paper proposes a system towards an automated inspection for potential rockfalls. A robot is used to repeatedly strike or tap on the rock surface. The sound from the tapping is collected by the robot and subsequently classified with the intent of identifying rocks that are broken and prone to fall. Principal Component Analysis (PCA) of the collected acoustic data is used to recognize patterns associated with rocks of various conditions, including intact as well as rock with different types and locations of cracks. The PCA classification was first demonstrated simulating sounds of different characteristics that were automatically trained and tested. Secondly, a laboratory test was conducted tapping rock specimens with three different levels of discontinuity in depth and shape. A real microphone mounted on the robot recorded the sound and the data were classified in three clusters within 2D space. A model was created using the training data to classify the reminder of the data (the test data). The performance of the method is evaluated with a confusion matrix.

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.

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.

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.