Most civil infrastructure worldwide is currently past its design life. This situation has precipitated the need for more systematic inspections and continual monitoring of infrastructure to assure structural integrity. With the advancement of sensing and computing technologies, rapid, remote, real-time, and robust structural health monitoring (SHM) techniques have been significantly developed. These smart SHM technologies often result in big data that require advanced data management, visualization, diagnostic, and prognostic techniques. Over the past few decades, researchers have developed numerous machine learning and artificial intelligence (AI)–based damage diagnostic and prognosis methods, which have been systematically reviewed in the recent state-of-the-art papers. In parallel, various data management and visualization techniques have been explored in SHM using building information modeling (BIM), virtual reality (VR), and augmented reality (AR). Both BIM and AR/VR present a unique opportunity to document, systematically interpret, and visualize SHM data in a three-dimensional (3D) environment and have shown significant promise in a wide range of infrastructure monitoring applications. Unlike diagnostics and prognostics methods of SHM, there has been a very limited systematic review of the latest visualization and data management techniques, which are both the objective and novelty of this review paper. Each subtopic reviews The recent data management and visualization technologies, their relevance to SHM, and their implementation challenges in broader structural engineering applications. Finally, this state-of-the-art review summarizes potential future research directions for BIM and VR/AR associated with SHM.

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 have 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. The Roadmap is organized into four sections: distributed embedded sensing systems, distributed surface sensing systems, multifunctional materials, and remote sensing. Recognizing that many measurement technologies may overlap between sections, we define distributed sensing solutions as those that involve or imply the utilization of numbers of sensors geometrically organized within (embedded) or over (surface) the monitored component or system. Multi-functional materials are sensing solutions that combine multiple capabilities, for example those also serving structural functions. Remote sensing are solutions that are contactless, for example cell phones, drones, and satellites. It also includes the notion of remotely controlled robots.

Infrastructure spending is such a large component of a railroad budget that it must be prioritized to meet the concurrent safety and line capacity requirements. Current bridge inspection and rating practices recommend observing bridge movements under a live load to help assess bridge conditions. However, measuring bridge movements under trains in the field is a challenging task. Even when they are measured, the relationships between bridge displacements and different loads/speeds are generally unknown. The research reported herein shows the effects of known train loadings, speeds, and traffic directions on the magnitude and frequency of displacements as measured on timber pile bents of a Class I railroad bridge. Researchers collected both vertical and transverse (lateral) displacements under revenue service traffic and work trains using LVDTs with a sampling frequency of 100 Hz. To investigate the effect of traffic on timber railroad bridges, displacements were measured under crossing events at different speeds and directions of a test train of known speed and weight provided by the railroad for the field experiment. The results indicate that bridge transverse displacements could help to capture critical changes in timber railroad bridge serviceability (i.e., ability to safely carry out railroad operations) as a function of railroad loading, speed, and direction. (C) 2014 American Society of Civil Engineersclose

Monitoring of bridge displacements is critical for safe and cost-effective railroad operations. One of the essential parameters for determining the serviceability of the bridges is the dynamic displacement of the bridge during train-crossing events. The traditional methods for bridge displacement measurement often utilize linear variable differential transducers (LVDTs). However, irregular terrain, remote and inaccessible locations, and the height of the railroad bridges make implementation of these sensors for displacement measurements inadequate, risky, and time-consuming, and sometimes not possible altogether. In recent years, the use of laser Doppler vibrometers (LDVs) in the field of bridge displacement measurement has drawn attention as an alternative. In these applications, the vibrometer is generally placed on a fixed-point reference close to the bridge. However, it is not always possible to locate a fixed reference perpendicular to the bridge span to measure transverse displacements, especially when the bridge spans over a large opening. Furthermore, LDV sensors require calibration for every unique, different setup and are cumbersome to implement across a variety of different bridges. This paper presents a novel concept for bridge displacement measurement that enables the use of noncontact and reference-free moving vibrometers in the field without the need for calibration. The concept discussed herein proposes a method of compensating for measurement errors due to the angular and linear movement of the vibrometer to obtain accurate transverse displacement measurements of bridges. The results of this study showed that the signal difference between the measured outputs of a moving LDV system and an LVDT was between 10% and 15% peak-to-peak and between 2% and 5% root-mean square (RMS), which are generally considered acceptable levels of accuracy by railroad managers for field applications. An outdoor test was conducted in which an LDV mounted on an unmanned aerial system (UAS) was used to collect displacement measurements of a moving target structure representing a railroad bridge train-crossing event. Researchers conducted three outdoor experiments by collecting LDV and LVDT measurements from the UAS as it was hovering. The results from this outdoor testing showed that the signal difference between the measured dynamic data from the moving LDV and the LVDT was less than 5% (peak) and 10% (RMS). The reference-free dynamic displacement is generally accepted by railroad managers as an important index related to bridge deterioration, so this application can inform railroads in the field of prioritization decisions without the need for installed sensors. The ultimate goal of this research was to validate the applicability of the proposed method by mounting one LDV on a UAS and measuring dynamic bridge displacements with minimal setup and calibration.

Railroads carry more than 40% of the freight, in terms of tons per mile transported in North America. A critical portion of the railroad infrastructure is the more than 100,000 bridges, which occur, on the average, every 1.4 miles of track. Railroads have a limited budget for capital investment. Therefore, decisions on which bridges to repair/replace become critical for both safety and economy. North American railroads regularly inspected bridges to ensure safe operation that can meet transport demands, using inspection reports to decide which bridges may need maintenance, replacement, or further investigation. Current bridge inspection practices recommend observing bridge responses under live load to help assess bridge condition. However, measuring bridge responses under train loads in the field is a challenging, expensive, and complex task. This research explores the potential of using wireless smart sensors (WSS) to measure bridge responses under revenue service traffic that can be used to inform bridge management decisions. Wireless strain gages installed on the rail measure real-time train loads. Wireless accelerometers and magnetic strain gages installed in the bridge measure associated bridge responses. The system is deployed and validated on a double-track steel truss bridge on the south side of Chicago, Illinois, owned by the Canadian National Railway. A calibrated finite element model of the bridge with known train input load estimated the responses of the bridge at arbitrary, unmeasured locations, showing the possibility of applying the system for decision making process. These results demonstrate the potential of WSS technology to assist with railroad bridge inspection and management practice. Copyright © 2016 John Wiley & Sons, Ltd.

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.