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Unmanned aerial vehicles (UAVs) have transitioned from a futuristic research concept to becoming a reality for practical, safe, cost-effective bridge inspections. Several studies have used UAVs to capture images from bridges and infrastructure to assess their condition. However, measuring the dynamic responses of bridges using a UAV involves the integration of UAV, sensors, and the use of dynamic equations. Measurement of dynamic transverse displacement especially is a difficult task in the field given the actual constraints of bridges, flights, and sensing under loading events. If transverse displacements could be measured easily, bridge owners could prioritize maintenance operations more cost-effectively by selecting to repair those bridges that move the most under trains. This paper discusses new requirements and solutions for fabricating an enhanced UAV to obtain dynamic transverse displacement benefiting from experiences gained from several field tests. This work follows the regulations of railroad bridge inspection guidelines and considers the aspects for an implementable system in its development. The paper first introduces the preliminary system that has been developed to this end and discusses potential improvements to this system that are identified through multiple field tests. The preliminary UAV system was developed using an algorithm combining the signals from sensors mounted on the UAV to measure dynamic displacements. This paper explains the step-by-step improvement of the existing system which resulted in a successful field test on a real bridge. Subsequently, some modifications and enhancements for the algorithm are proposed which are compatible with the new system data.

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

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...

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.

Today, the railroad industry carries 40% of the United States’ total freight ton-miles over 100,000 bridges spanning over 225,000 km (140,000 mi) of rail tracks and the demand for railroads...

Railroads carry approximately 40% of the ton-miles of the freight in the United States. On the average, a bridge occurs every 2.25 km (1.4 mi) of track, making them critical elements. The primary load on the railroad bridges is the train, resulting in numerous models being developed to understand the dynamic response of bridges under train loads. However, because the problem is time-dependent and coupled, developing adequate models is challenging. Most of the proposed models fail to provide a simple yet flexible representation of the train, bridge, and track. This paper proposes a new hybrid model that is effective for solving the track–bridge interaction problem under moving trains. The main approach is to couple the finite-element model of the bridge with a continuous beam model of the track using the assumed modes method. Both single-track and multitrack bridges are considered. The hybrid model is validated against field measurements for a double-track bridge. This model is then used to predict...

Current railroad bridge inspection and rating practices include observing bridge movement under live loads to help assess bridge conditions. Recent research has shown that transverse displacements of timber trestle bridges can capture critical changes in bridge serviceability (the ability to safely carry out railroad operations) as a function of railroad loading, speed, and direction. Measuring bridge movement under trains in the field is difficult and expensive because a fixed reference point is not normally available, thus creating the need to erect independent scaffolding to create good reference points near a timber bridge. This research demonstrates the potential of using reference-free accelerations collected with wireless smart sensors to estimate railroad bridge transverse displacements under live train loads. Focus is placed on timber trestle bridges, which comprise approximately 24% of the total inventory length of railroad bridges in the United States. The results show that wireless smart sensors can estimate transverse displacements of timber railroad trestles and could become an effective tool for campaign monitoring of railroad bridges (with applications toward helping overall bridge assessment).

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 d...

We present a novel, compliant mechanism that provides the capability to navigate extremely confined spaces for the purpose of infrastructure inspection. Extremely confined spaces are commonly encountered during infrastructure inspection. Examples of such spaces can include pipes, conduits, and ventilation ducts. Often these infrastructure features go uninspected simply because there is no viable way to access their interior. In addition, it is not uncommon for extremely confined spaces to possess a maze-like architecture that must be selectively navigated in order to properly perform an inspection. Efforts by the imaging sensor community have resulted in the development of imaging sensors on the millimeter length scale. Due to their compact size, they are able to inspect many extremely confined spaces of interest, however, the means to deliver these sensors to the proper location to obtain the desired images are lacking. To address this problem, we draw inspiration from the field of endoscopic surgery. Specifically we consider the work that has already been done to create long flexible needles that are capable of being steered through the human body. These devices are typically referred to as 'steerable needles.' Steerable needle technology is not directly applicable to the problem of navigating maze-like arrangements of extremely confined spaces, but it does provide guidance on how this problem should be approached. Specifically, the super-elastic nitinol tubing material that allows steerable needles to operate is also appropriate for the problem of navigating maze-like arrangements of extremely confined spaces. Furthermore, the portion of the mechanism that enters the extremely confined space is completely mechanical in nature. The mechanical nature of the device is an advantage when the extremely confined space features environmental hazards such as radiation that could degrade an electromechanically operated mechanism. Here, we present a compliant mechanism developed to navigate maze-like arrangements of extremely confined spaces. The mechanism is shown to be able to selectively navigate past three 90° bends. The ability to selectively navigate extremely confined spaces opens up new possibilities to use emerging miniature imaging technology for infrastructure inspection.