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Sensors have recently become valuable tools in engineering, providing real-time data for monitoring structures and the environment. They are also emerging as new tools in education and training, offering learners real-time information to reinforce their understanding of engineering concepts. However, sensing technology’s complexity, costs, fabrication and implementation challenges often hinder engineers’ exploration. Simplifying these aspects could make sensors more accessible to engineering students. In this study, the researcher developed, fabricated, and tested an efficient low-cost wireless intelligent sensor aimed at education and research, named LEWIS1. This paper describes the hardware and software architecture of the first prototype and their use, as well as the proposed new versions, LEWIS1-β and LEWIS1-γ, which simplify both hardware and software. The capabilities of the proposed sensor are compared with those of an accurate commercial PCB sensor. This paper also demonstrates examples of outreach efforts and suggests the adoption of the newer versions of LEWIS1 as tools for education and research. The authors also investigated the number of activities and sensor-building workshops that have been conducted since 2015 using the LEWIS sensor, showing an increasing trend in the excitement of people from various professions to participate and learn sensor fabrication.

A significant percentage of bridges in the United States are serving beyond their 50-year design life, and many of them are in poor condition, making them vulnerable to fatigue cracks that can result in catastrophic failure. However, current fatigue crack inspection practice based on human vision is time-consuming, labor intensive, and prone to error. We present a novel human-centered bridge inspection methodology to enhance the efficiency and accuracy of fatigue crack detection by employing advanced technologies including computer vision and augmented reality (AR). In particular, a computer vision-based algorithm is developed to enable near-real-time fatigue crack detection by analyzing structural surface motion in a short video recorded by a moving camera of the AR headset. The approach monitors structural surfaces by tracking feature points and measuring variations in distances between feature point pairs to recognize the motion pattern associated with the crack opening and closing. Measuring distance changes between feature points, as opposed to their displacement changes before this improvement, eliminates the need of camera motion compensation and enables reliable and computationally efficient fatigue crack detection using the nonstationary AR headset. In addition, an AR environment is created and integrated with the computer vision algorithm. The crack detection results are transmitted to the AR headset worn by the bridge inspector, where they are converted into holograms and anchored on the bridge surface in the 3D real-world environment. The AR environment also provides virtual menus to support human-in-the-loop decision-making to determine optimal crack detection parameters. This human-centered approach with improved visualization and human–machine collaboration aids the inspector in making well-informed decisions in the field in a near-real-time fashion. The proposed crack detection method is comprehensively assessed using two laboratory test setups for both in-plane and out-of-plane fatigue cracks. Finally, using the integrated AR environment, a human-centered bridge inspection is conducted to demonstrate the efficacy and potential of the proposed methodology.

Abstract Augmented reality (AR) is a technology superimposes interactional virtual objects onto a real environment. Since the beginning of the millennium, AR technologies have rapidly grown, with significant research publications in engineering and science. However, the civil infrastructure community has minimally implemented AR technologies to date. Civil engineers face one of the challenges when understanding and using AR is the lack of a classification of AR in the context of capabilities for civil infrastructure applications. Like most engineering fields, practitioners in civil infrastructure prioritize understanding a new technology's maturity level before considering its adoption and field implementation. This article compares the capabilities of 16 AR head‐mounted devices (HMDs) available in the market since 2017, ranking them in terms of performance for civil infrastructure implementations. Finally, the authors recommend a development framework for functional AR interfaces with civil infrastructure and operations.

In the future, sensors mounted on uncrewed aerial systems (UASs) will play a critical role in increasing both the speed and safety of structural inspections. Environmental and safety concerns make structural inspections and maintenance challenging when conducted using traditional methods, especially for large structures. The methods developed and tested in the laboratory need to be tested in the field on real-size structures to identify their potential for full implementation. This paper presents results from a full-scale field implementation of a novel sensor equipped with UAS to measure non-contact transverse displacement from a pedestrian bridge. To this end, the authors modified and upgraded a low-cost system that previously showed promise in laboratory and small-scale outdoor settings so that it could be tested on an in-service bridge. The upgraded UAS system uses a commodity drone platform, low-cost sensors including a laser range-finder, and a computer vision-based algorithm with the aim of measuring bridge displacements under load indicative of structural problems. The aim of this research is to alleviate the costs and challenges associated with sensor attachment in bridge inspections and deliver the first prototype of a UAS-based non-contact out-of-plane displacement measurement. This work helps to define the capabilities and limitations of the proposed low-cost system in obtaining non-contact transverse displacement in outdoor experiments.

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.

Human induced dynamic forces on structures are of interest in the area of human-environment interfaces. The research community is interested in characterizing human decisions and providing information on the consequences of human actions to control those human forces more effectively. Dynamic structures can vibrate under human motion. In the context of human–structure interactions (HSI), dance induced vibrations can be quantified with sensors. This data can provide a unique opportunity for dancers to understand the quality of their dance with objective metrics. Previous work in capturing dance moves required wearable sensors attached to the dancer’s body. Often an intrusive process, this method is not scalable if dancers are not familiar with technology and it limits their participation without access to special studios or facilities. If simple, deployable technology could be available to dancers, they could monitor their dance without engineers. This research integrates dancers’ interest in qualifying dance motion and engineering curiosity to study human induced vibrations. As a part of the framework, researchers used two indexes to differentiate between a well synchronized group dance from asynchronous moves. The two indexes are the Harmony Index and the Coordination Index, respectively, and are validated against the Visual Index, a qualitative index obtained from an expert who judged dance moves based on one video capture. The indexes were derived from measurements of the movement of the structure dynamically excited by the dancers, hence quantifying dance coordination. These two indexes are based on time history data obtained from sensors installed on a wooden bridge where dancers performed at different levels of proficiency. The results of this research show that the two indexes sort effectively the quality of the dancers, when validated with the Visual Index. As a result, this research proposes using Low-cost efficient wireless intelligent sensor (LEWIS) to objectively sort different levels of dance quality which could be expanded to study the HSI for design and assessment of the structural systems used for dancing, such as performance halls and ballrooms.

Transportation infrastructure is an integral part of the world’s overall functionality; however, current transportation infrastructure has aged since it was first developed and implemented. Consequently, given its condition, preservation has become a main priority for transportation agencies. Billions of dollars annually are required to maintain the United States’ transportation system; however, with limited budgets the prioritization of maintenance and repairs is key. Structural Health Monitoring (SHM) methods can efficiently inform the prioritization of preservation efforts. This paper presents an acoustic monitoring SHM method, deemed tap testing, which is used to detect signs of deterioration in structural/mechanical surfaces through nondestructive means. This method is proposed as a tool to assist bridge inspectors, who already utilize a costly form of SHM methodology when conducting inspections in the field. Challenges arise when it comes to this method of testing, especially when SHM device deployment is done by hand, and when the results are based solely upon a given inspector’s abilities. This type of monitoring solution is also, in general, only available to experts, and is associated with special cases that justify their cost. With the creation of a low-cost, cyber–physical system that interrogates and classifies the mechanical health of given surfaces, we lower the cost of SHM, decrease the challenges faced when conducting such tests, and enable communities with a revolutionary solution that is adaptable to their needs. The authors of this paper created and tested a low-cost, interrogating robot that informs users of structural/mechanical defects. This research describes the further development, validation of, and experimentation with, a tap testing device that utilizes remote technology.

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.

A system’s response to disturbances in an internal or external driving signal can be characterized as performing an implicit computation, where the dynamics of the system are a manifestation of its new state holding some memory about those disturbances. Identifying small disturbances in the response signal requires detailed information about the dynamics of the inputs, which can be challenging. This paper presents a new method called the Information Impulse Function (IIF) for detecting and time-localizing small disturbances in system response data. The novelty of IIF is its ability to measure relative information content without using Boltzmann’s equation by modeling signal transmission as a series of dissipative steps. Since a detailed expression of the informational structure in the signal is achieved with IIF, it is ideal for detecting disturbances in the response signal, i.e., the system dynamics. Those findings are based on numerical studies of the topological structure of the dynamics of a nonlinear system due to perturbated driving signals. The IIF is compared to both the Permutation entropy and Shannon entropy to demonstrate its entropy-like relationship with system state and its degree of sensitivity to perturbations in a driving signal.

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’ decisions, so it is of vital necessity to improve the inspectors’ 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.

Governments and professional groups related to civil engineering write and publish standards and codes to protect the safety of critical infrastructure. In recent decades, countries have developed codes and standards for structural health monitoring (SHM). During this same period, rapid growth in the Chinese economy has led to massive development of civil engineering infrastructure design and construction projects. In 2016, the Ministry of Transportation of the People’s Republic of China published a new design code for SHM systems for large highway bridges. This document is the first technical SHM code by a national government that enforces sensor installation on highway bridges. This paper summarizes the existing international technical SHM codes for various countries and compares them with the new SHM code required by the Chinese Ministry of Transportation. This paper outlines the contents of the new Chinese SHM code and explains its relevance for the safety and management of large bridges in China, introducing key definitions of the Chinese–United States SHM vocabulary and their technical significance. Finally, this paper discusses the implications for the design and implementation of a future SHM codes, with suggestions for similar efforts in United States and other countries.

Currently, over half of the U.S.’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.