2021

Jack Green, Purdue University
John Mott (jhmott@purdue.edu), Purdue University

Unmanned Aerial Systems (UAS) continue to grow in both popularity and utility within the national airspace system. The use of commercial UAS for civil inspection, specifically that of bridge structures, is becoming commonplace among practitioners and academics alike. The development of an integrated bridge-inspection hazard model provides a way for UAS operators to prepare for and respond to changing environmental conditions that could otherwise prevent a successful UAS flight. The interaction of wind-induced airflow with bridge surfaces creates an aerodynamic wake that can result in hazardous conditions for a UAS platform operating in close proximity. An analysis of this airflow surrounding an overpass bridge using computational fluid dynamic (CFD) models shows where these hazardous areas exist, based on initial wind conditions, existing functionality of UAS platforms, and mitigating systems that exist within current UAS technology. Altering the initial conditions by modifying wind velocities allows for a baseline understanding of which areas surrounding an overpass bridge structure are not suitable for successful UAS flight. Hazard zones range from severe, moderate, and mild, to no appreciable hazard. Moderate to extreme hazard zones are located in areas where high levels of wind shear, vorticity, or general turbulence exist, making UAS operations difficult. This study develops a practical model that may be utilized by UAS operators to better understand and potentially avoid unsafe flight environments during bridge structure inspections.

Michael Plotnikov, University of Massachusetts, Amherst
John Collura, University of Massachusetts, Amherst

This paper presents the results of research conducted by a team of researchers at the University of Massachusetts Transportation Center on the current state of the practice of small unmanned aircraft systems (UASs) for highway bridge inspections done by state Departments of Transportation (DOTs). The results of recent studies indicate that UASs can serve as a useful tool in some of highway bridge inspection procedures, while significantly reducing costs and time and improving safety. The major factors that affect the success of integrating UASs into the bridge inspection process relate to selection of the proper types of UAS platforms and avionics, data collection sensors and processing software, as well as conduct of task specific pilot training. The paper provides an examination of current standard bridge inspection procedures and protocols currently carried out by state DOTs; an evaluation of state DOT experiences regarding the integration of UAS technology into bridge inspections; and an assessment of the issues and challenges associated with this technology. It is expected that this paper will be of interest to the Federal Aviation Administration (FAA) and state DOT officials; FAA UAS Test Site staff and their industry partners; local public works directors, highway superintendents, and traffic engineers; Federal Highway Administration (FHWA) field personnel and other UAS stakeholders including flight instructors, consultants, UAS manufacturers and vendors, and university researchers .

Thiago Rodrigues (tarodrig@andrew.cmu.edu), Carnegie Mellon University
Jay Patrikar, Carnegie Mellon University
Natalia Oliveira, Carnegie Mellon University
H. Scott Matthews, Carnegie Mellon University
Sebastian Scherer, Carnegie Mellon University
Constantine Samaras, Carnegie Mellon University

The adoption of Unmanned Aerial Vehicles (UAV) for last-mile deliveries will affect the energy productivity of package delivery and requires new methods to understand the associated energy consumption and greenhouse gas (GHG) emissions. There are limited open empirical datasets of drone energy use, and here we combine empirical testing with first principles analysis to develop a usable energy model for drone package delivery. Using our experimental protocol, we collected and analyzed empirical data from 187 drone flights and developed two energy models to estimate the energy required to power a quadcopter. We validate both methods using the data collected and compared the predictive power of the two models to a flexible nonlinear algorithm, XGBoost. We show that both models were successful in predicting the energy consumption of a quadcopter drone with an average relative error of 3.3% and 10%. Moreover, the operational parameters can greatly affect the drone’s range, reducing it by 50% in same cases.  Our empirical analysis found a small electric quadcopter drone with a payload of 1 kg would consume between 0.10 and 0.20 MJ/km. A Monte Carlo simulation is then performed to compare the energy consumption of small quadcopter drones to other last-mile delivery transportation modes.

Mostafa Namian, East Carolina University
Mohammad Khalid, East Carolina University
George Wang, East Carolina University
Yelda Turkan, Oregon State University

Unmanned Aerial Vehicle (UAVs) (also known as drones) have recently gained their prevalent recognition in the construction industry due to their exceptional advantages. Despite the increasing use of UAVs in construction and their remarkable benefits, there are serious potential safety risks associated with their utilization that have been overlooked. As UAV proliferation continues, drones’ operational complexities and risky situations are increasing. Construction is one of the most hazardous industries in the United States. In addition to the ordinary hazards naturally implemented in dynamic construction workplaces, UAVs can expose workers to a wider range of never-before-seen safety risks that must be recognized and controlled. The industry is not equipped with safety measures to prevent potential accidents due to scarce research investigating drone-associated hazards and risks. The aim of this research is to (1) investigate and identify the UAV-associated hazards in construction that may expose personnel and property to potential harm, and (2) study the relative impact of each hazard and the associated safety risks. This study was completed in two phases. In Phase I, the researchers conducted an extensive literature review and consulted with a local industry advisor with extensive experience of UAV operations on construction projects. In Phase II, the researchers obtained data from fifty-four construction experts validating and evaluating the identified hazards and risks. A post-hoc case study was also investigated and presented in this article. The results revealed that adopting UAVs can expose personnel and properties to a variety of hazards that the industry is not familiar with.

Sattar Dorafshan, University of North Dakota
Leslie Campbell, U.S. Army Engineer Research and Development Center
Marc Maguire, University of Nebraska, Lincoln
Rob Connor, Purdue University

Inspection agencies have been increasingly implementing Unmanned Aerial Systems (UAS) for bridge inspections. Currently, UAS are typically used for long-range monitoring and surveillance tasks, but bridge managers are hopeful that they may be utilized for detailed inspection, such as condition assessments and the inspection of fracture critical members (FCM) in the near future. As an assistive tool for visual inspections, the accuracy of UAS-assisted inspections is unknown. This study investigates the influence individual inspectors on a set of performance metrics associated with UAS-assisted FCM inspections for the first time. Four bridge inspectors used a UAS to inspect a series of full-sized bridge specimens with known fatigue cracks. The inspection videos were later shared with 19 bridge inspectors for desk review. The performance of each inspector was evaluated and compared to the results of 30 hands-on inspections of the same specimens. The results showed inspector’s past experience with UAS, licensure, and academic degree could have a significant influence on one or more of the three defined performance metrics. However, specimen’s orientation affected all three-performance metrics. Additionally, individual performance metrics had the highest correlation with inspectors’ experience and standard vision test score. The comparison between the results of the UAS-assisted inspections and the hand-on inspections revealed that detected cracks in both UAS-assisted and hands-on inspection were comparable. However, the hands-on inspections overall were more accurate.

Ruifeng She, University of Illinois, Urbana-Champaign
Yanfeng Ouyang (yfouyang@illinois.edu), University of Illinois, Urbana-Champaign

Emerging unmanned aerial vehicle (UAV) technologies have motivated logistics carriers to seek last-mile parcel delivery through the air so as to benefit from its convenience and flexibility. However, UAV-based delivery services are limited by several binding factors, such as low battery capacities and short delivery range, which in turn require simultaneous use of a large fleet for commercial scale operations. In such cases, congestion in low-altitude air will inevitably arise. This paper investigates self-organized UAV traffic flow in low altitude 3D airspace, and formulates the user equilibrium condition as a set of partial differential equations. We propose a finite element scheme to numerically solve the traffic equilibrium and compute system performance. Two specific test scenarios for last-mile freight delivery systems are studied, including one with a conventional ground-based distribution facility, and the other with a novel concept of airborne fulfillment center. We evaluate the operational cost and energy consumption of these systems under a variety of system configurations. The results provide insights that could be useful for logistic carriers and policy makers to achieve efficiency and sustainability for last-mile delivery.