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Virtual Nondestructive Evaluation Laboratory Simulates Bridge Conditions

A digital image of the underside of a segment of a bridge. The digital image captures the high-resolution details of its real-world counterpart, even including the graffiti on the bridge piers.
In the virtual NDE laboratory, this "digital twin" of a bridge is used to get a better understanding of the bridge's condition than traditional real-world inspections yield.

With assistance and funding from the Federal Highway Administration's (FHWA) EAR Program, researchers at Drexel University, Rutgers University, and Saint Joseph's University are developing an online laboratory where inspectors and engineers can try out diagnostic tools and emerging technologies to examine the condition of virtual bridges. The virtual bridges are "digital twins" of real-world bridges with the same problems or conditions as their real-world counterparts.

The virtual nondestructive evaluation (NDE) laboratory not only includes data from the real-world bridges but also controlled specimens of structural elements and technology used to assess bridge conditions. In the simulated website environment of the virtual NDE laboratory, engineers and inspectors can test new tools to diagnose and address problems affecting actual bridges and their components, and researchers can demonstrate these techniques in a realistic, shared environment.

A multi-color graph showing a three-dimensional image of a simulated impact test on a bridge. The resulting shape is raised on the left and right sides and sinks down in the center.
A simulation of a multiple-reference impact test in which sensor placement and performance impacts are indicated on a virtual bridge. This technique can obtain reliable spatial modal response data with limited onsite instrumentation and operation.

"Using this website will complement the visual inspections already being made by bridge inspectors," said Hoda Azari, program manager at FHWA's NDE Laboratory with the Office of Infrastructure Research and Development. "The tools and scenarios presented in the website will help inspectors develop a more comprehensive understanding of a bridge's condition."

The researchers plan to develop case studies or scenarios aimed at showing laboratory users how physical or nondestructive testing methods can help them develop a clearer picture of a bridge's health. The case studies could be used to illustrate best practice applications of various technologies and their integration; demonstrate how well-designed and executed technology applications can lead to generalizable conclusions about bridge performance; and present potential business cases for the application of assessment technologies for bridges.

For more information, see the fact sheet Virtual Nondestructive Evaluation Laboratory for Highway Structures or contact Hoda Azari at 202–493–3064 or

NRC Associates Advancing Cooperative Automation

FHWA's Office of Operations Research and Development is sponsoring three National Research Council (NRC) associates to advance research on cooperative automation. The researchers work in the Saxton Transportation Operations Laboratory (STOL) at the Turner-Fairbank Highway Research Center (TFHRC). They are developing algorithms on the Cooperative Automation Research Mobility Applications (CARMA) platform, an open source tool that FHWA released in October 2018 on GitHub.

Each of the associate's research areas seeks to improve infrastructure efficiency and increase safety of the surface transportation system by leveraging cutting-edge automated driving technologies.

  • Dr. Pavle Bujanovic is developing and advancing current vehicle platooning concepts on CARMA and using simulation tools to predict the impact of cooperation-enabled automated driving systems (ADS) on highways.
  • Dr. Mohammad Goli is developing the cooperative automated vehicle control and the vehicle platooning framework for a cooperation-enabled ADS to support CARMA.
  • Dr. Mehdi Zamanipour is developing optimization models for deploying cooperative automated driving technologies along signalized corridors for a cooperation-enabled ADS to support CARMA.

A black SUV with the word CARMA painted on its side is parked off the side of a road beneath the arm of a traffic signal and next to a metal control box with the door open. One researcher stands in front of the control box working on a laptop. The SUV's tailgate is open, and another researcher sits inside the back of the vehicle working on a laptop. A third researcher adjusts equipment mounted on the vehicle's roof.

NRC Associates (from left) Mehdi Zamanipour and Mohammad Goli prepare to test the CARMA vehicle at one of the smart intersections located at TFHRC while Pavle Bujanovic adjusts the rooftop-mounted LiDAR.

Dr. Taylor Lochrane and Dr. Govind Vadakpat advise the associates and are engaged in their day-to-day activities. One of the most valuable parts of the NRC Research Associateship Program (RAP) at TFHRC is that the associates have access to laboratory equipment to assist in their research. The team of associates was assigned one of the automated vehicles in the STOL and are upgrading it to support their research.

"By providing the team with the tools it needs to build its own vehicle, we get a better understanding of the automated vehicle functionality and constraints, which results in a better research product," said Lochrane.

Three men stand on the running boards of a stopped SUV with the doors open. The word CARMA is painted on the doors of the vehicle in white and yellow letters below a yellow stripe. A piece of equipment is mounted on the vehicle's roof.

From left: Mehdi Zamanipour, Mohammad Goli, and Pavle Bujanovic with their test vehicle.

The associates have equipped the vehicle with a light detection ranging sensor (LiDAR), two stereo cameras, two global positioning system receivers, and radar. They will install more radar and cameras for better coverage, and then fuse the outputs of the sensors for environmental perception, localization, and control. Using these sensors, the vehicle will be able to localize itself and detect things such as pedestrians and other vehicles by applying estimation theories and machine learning techniques. They also have developed and plan to implement an algorithm to detect lane marks and other vehicles from video.

More information about the NRC RAP is available here or in the EAR Program publication Research Associates Program 2018.

Updated: Thursday, June 6, 2019