Noncontact Sensing Technologies for Bridge Structural Health Assessment

The special collection on Noncontact Sensing Technologies for Bridge Structural Health Assessment is available in the ASCE Library (https://ascelibrary.org/jbenf2/non_contact_sensing_bridge_assessment).

The last decade has witnessed unprecedented growth in bridge structural health assessment using noncontact sensors such as cameras, lasers, and radar. Unlike conventional sensors such as accelerometers and strain gauges, these noncontact sensors enable measurements of relevant quantities such as displacement fields without direct physical contact with the object of interest. When employed with the aid of mobile platforms such as unmanned aerial vehicles (UAVs), such noncontact technologies—especially cameras—have shown to enable rapid assessment of the surface condition of bridge components, even in otherwise hard-to-access areas of the structure. Other noncontact technologies such as radar and infrasound have shown to be capable of remote monitoring over very large distances. While the technology underpinning most of the noncontact sensors such as cameras or radar has matured considerably during the last decade, the algorithms and decision support tools which can enable the extraction of pertinent information in the context of bridge structure condition assessment are still in development. Many specific questions regarding their applicability specific to the domain of bridge monitoring arise such as noise robustness, resolution, scalability, and accuracy need to be fully studied before such noncontact technologies can be proven as viable alternatives to conventional contact sensors. This special issue aims to synthesize the state-of-the-art in several noncontact sensing modalities with particular emphasis on studies that demonstrate their applicability to the common tasks employed in the domain of bridge health assessment.

A total of 15 papers are included in this initial special collection and more slated to be added to this collection in the coming months. The taxonomy used in this introduction consists of three broad categories: (1) papers focused on demonstrating aerial- and ground-based robotic platforms; (2) papers focused on imaging and laser sensing techniques; and (3) papers focused on other noncontact technologies, not covered by (1) and (2). Based on this classification, four papers belonged to (1), seven to (2), and the remaining to (3). Brief highlights for each of these papers are provided in the following.

Papers on the first topic include both land and aerial platforms, and topics of interest range from evaluating the quality of inspection data acquired from these platforms to collision avoidance and inspection plan execution. In their paper on three-dimensional (3D) reconstructions using structure from motion (SfM) obtained from images acquired using UAVs, Chen et al. (2019) show that their approach can offer significant benefits in terms of cost, surveying time, point distribution, and coverage, while identifying challenges in their technology, e.g., noise, accuracy, and time for processing. Dorafshan et al. (2018) present a study of UAV obtained inspection results in both laboratory and field settings to monitor for fatigue cracks by varying parameters such as the level of illumination and stand-off distance. Their results highlight some of the challenges in obtaining clear images where such cracks can be detected, especially in GPS denied and windy environments. Tomiczek et al. (2019) demonstrate the development and use of an obstacle avoidance system in a small UAV, which is particularly relevant for inspecting the underside elements of bridges where global positioning from satellites cannot be guaranteed. In their paper on automating bridge inspections, Phillips and Narasimhan (2019) present an autonomy framework and execute a prespecified inspection plan using a ground-based robot and generate 3D point cloud reconstructions of a concrete bridge using a 3D Lidar sensor. This is the first paper of its kind where 3D mapping and inspection plan execution are demonstrated using a ground robot for the purposes of conducting bridge inspections.

Papers focused on the application of image and laser sensors touch upon a broad range of topics including—but not limited to—digital image correlation (DIC) and the use of laser Doppler vibrometers mounted on a moving base, e.g., UAV. Papers focused on DIC include studies using two-dimensional (2D) DIC on the precision of strains on surfaces measured using a wide-angle camera by Halding et al. (2019), strain fields on masonry rail bridges by Dhanasekar et al. (2019), and deflection measurements in bridges by Alipour et al. (2019). A hybrid system consisting of a consumer-grade camera and an accelerometer was presented by Xu et al. (2019) to overcome sensing errors due to camera shake and poor resolution when tracking low-contrast natural patterns in pursuit of bridge displacement measurements. Chen and Chang (2019) present an enhanced digital sampling moiré method using the phase distribution of moiré fringes in successive images to measure both in-plane translation and rotation. Garg et al. (2019) present results of bridge vibration displacement measurements using a laser Doppler vibrometer from a UAV and algorithms to correct the resulting motion-induced errors. In the paper on measurements of bridge response using He–Ne-modulated lasers, Attanayake and Aktan (2019) present a noncontact technology to enable accurate measurements, including details on the equipment and field evaluation results.

Two papers focus on the use of infrasound (below the human audible range of 20 Hz) for the measurement of bridge dynamic properties. Whitlow et al. (2019) demonstrate the feasibility of using infrasound to detect natural modes of a bridge from a distance of 2.6 km, and the authors verify their results with data collected using accelerometers. In the second paper on the use of infrasound, Lobo-Aguilar et al. (2019) uses a microphone located directly beneath an in-service highway bridge to measure the modal frequencies of a bridge located in Connecticut. Hoppe et al. (2019) present monitoring results using synthetic aperture radar in the form of displacement time series spanning over a period of 1.3 years for two post-tensioned bridges in Virginia. Finally, Tang et al. (2019) present a novel monitoring technique using magnet(s) embedded in concrete to monitor bridge scour depth, along with results from a field study at a highway bridge pier.

This special issue covers a broad spectrum of noncontact sensing technologies available in the market, with special emphasis on their applicability to bridge condition monitoring and assessment. While by no means an exhaustive treatment of all noncontact sensing modalities, the Guest Editors hope that this special collection captures key capabilities and challenges within the individual sensing methods and will spur interest among researchers and practitioners to use this technology and overcome crucial technical gaps which currently prevent them from widespread adoption. The Guest Editors thank Professor Anil Agrawal, Chief Editor, for providing them with the opportunity to develop this special issue. This special issue could not have been possible without the support of over 50 reviewers in providing timely and thorough comments necessary to successfully carry out the peer review process.