Concrete 3D printing reduces both time and cost by eliminating traditional formwork, the temporary mold for casting. Yet most of today’s systems rely on extrusion-based methods, which deposit material very close to a nozzle layer by layer. This makes it impossible to print around reinforcement bars (rebars) without risk of collision, limiting both design flexibility and structural integrity of builds.

Kenji Shimada and researchers in his Carnegie Mellon University’s Computational Engineering and Robotics Laboratory (CERLAB), are breaking through that limitation with a new simulation tool for spray-based concrete 3D printing.

“Spray-based concrete 3D printing is a new process with complicated physical phenomena,” said Shimada, a professor of mechanical engineering. “In this method, a modified shotcrete mixture is sprayed from a nozzle to build up on a surface, even around rebar.”

The ability to print freely around reinforcement is especially important in places like Japan and California, where earthquakes are an imminent threat and structural strength is critical.

“To make this technology viable, we must be able to predict exactly how the concrete will spray and dry into the final shape,” Shimada explained. “That’s why we developed a simulator for concrete spray 3D printing.”

The new simulator can model the viscoelastic behaviors of shotcrete mixtures, including drip, particle rebound, spread, and solidification time. This way, contractors can assess multiple printing paths based on a CAD design with the simulator to evaluate whether spray 3D printing is a feasible fabrication technique for their structure.

The team traveled to Tokyo, Japan, where Shimizu Corporation already operates spray 3D printing robots to validate their model. In the first test, the team focused on the simulator’s ability to predict shape based on the speed of the nozzle’s movement. With 90.75% accuracy, the simulator could predict the height of the sprayed concrete. The second test showed that the simulator could predict printing over rebar with 92.3% and 97.9% accuracy for width and thickness, respectively.

According to Soji Yamakawa, a research scientist in Shimada’s lab and the lead author of the team’s research paper published in IEEE Robotics and Automation Letters, a simulation of this kind would typically take hours, if not days, to run.

“By making wild assumptions, we were able to successfully simplify a super complex physics simulation into a combination of efficient algorithms and data structures and still achieved highly realistic output,” Yamakawa said.

Future work will aim to increase accuracy by identifying environmental parameters like humidity, optimizing performance, and adding plastering simulation to create smoother finished products.

“There are still so many applications and technologies that we can develop with robotics,” said Kyshalee Vazquez-Santiago, a co-author of the paper and a mechanical engineering Ph.D. candidate leading the Mobile Manipulators research group within CERLAB.

“Even in concrete 3D printing, we are working with an entirely new type of application and approach that has so many advantages but leaves so much room for further development.”

Thirty-eight years later, The Running Man is back on our screens, playing to a world that seems to have caught up with the original’s idiocy. This new one features a considerably less bulky, but no less watchable star in Glen Powell, playing runner Ben Richards. Fired from various jobs for insubordination, and tending to a sick toddler, he’s press-ganged into joining America’s favorite kill-or-be-killed game show, after a producer identifies him as “quantifiably the angriest man to ever audition.”

The show’s premise has been tweaked a bit, too. Instead of navigating a series of video-game-like levels for the length of a TV broadcast, Richards must now survive in the real world for 30 days, surveilled by hovering network TV camera droids, pursued by armed-to-the-teeth “hunters,” private police goons, and a general public who spot and film runners using a proprietary app on their smartphones. The longer he lasts, and the more pursuers he can kill, the more money he makes. He’s cheered (and booed) by a massive audience of brain-dead oafs called Running Fans, glued to their screens 24/7. Like Schwarzenegger’s Richard before him, Powell makes the transition from onscreen villain to beloved folk hero, mugging for the cameras as his antics drive the ratings.

If it sounds familiar, it’s because this new version of The Running Man, which is cowritten and directed by Edgar Wright (Hot Fuzz, Scott Pilgrim vs. the World), draws as much from the original film and Stephen King’s source novel as it does from present-day reality. A modern-day America overseen by a game show president, where ICE squads team up with Dr. Phil McGraw to turn deportation raids into reality television, would seem ripe for a Running Man remake. But that’s the problem. Satire relies on caricature. And the new version is barely exaggerative. Does the very idea of a lethal game show seem that far off, in a world where the success of Netflix’s South Korean thriller series Squid Game (itself a variation on the The Running Man format) spawned an actual, licensed Squid Game-style competitive reality TV show? Or when a grinning zillennial YouTuber named “MrBeast” baits contestants with ten grand to sit in a bathtub full of snakes? A few weeks ago I watched live as rookie New York Giants’ running back Cam Skattebo’s ankle twisted 45-degrees, as if cranked by some invisible wrench, while a bar-full of rival fans cheered.

Autonomous vehicles (AVs) have been tested as taxis for decades in San Francisco, Pittsburgh and around the world, and trucking companies have enormous incentives to adopt them.

But AV companies rarely share the crash- and safety-related data that is crucial to improving the safety of their vehicles—mostly because they have little incentive to do so.

Is AV safety data an auto company’s intellectual asset or a public good? It can be both—with a little tweaking, according to a team of Cornell researchers.

A new data-sharing roadmap

The team has created a roadmap outlining the barriers and opportunities to encourage AV companies to share the data to make AVs safer, from untangling public versus private data knowledge, to regulations to creating incentive programs.

“The core of AV market competition involves who has that crash data, because once you have that data, it’s much easier for you to train your AI to not make that error. The hope is to first make this data transparent and then use it for the public good, and not just profit,” said Hauke Sandhaus, M.S. ’24, a doctoral candidate at Cornell Tech and co-author of “My Precious Crash Data,” presented Oct. 16 at the ACM on Human-Computer Interaction.

His co-authors are Qian Yang, assistant professor at the Cornell Ann S. Bowers College of Computing and Information Science; Wendy Ju, associate professor of information science and design tech at Cornell Tech, the Cornell Ann S. Bowers College of Computing and Information Science and the Jacobs Technion-Cornell Institute; and Angel Hsing-Chi Hwang, a former postdoctoral associate at Cornell and now assistant professor of communication at the University of Southern California, Annenberg.

Barriers to sharing AV safety data

The team interviewed 12 AV company employees who work on safety in AV design and deployment, to understand how they currently manage and share safety data, the data sharing challenges and concerns they face, and their ideal data-sharing practices.

The interviews revealed the AV companies have a surprising diversity of approaches, Sandhaus said. “Everyone really has some niche, homegrown data set, and there’s really not a lot of shared knowledge between these companies,” he said. “I expected they would be much more commonality.”

The research team discovered two key barriers to sharing data—both underscoring a lack of incentives. First, crash and safety data includes information about the machine-learning models and infrastructure that the company uses to improve safety.

“Data sharing, even within a company, is political and fraught,” the team wrote in the paper. Second, the interviewees believed AV safety knowledge is private and brings their company a competitive edge.

“This perspective leads them to view safety knowledge embedded in data as a contested space rather than public knowledge for social good,” the team wrote.

And U.S. and European regulations are not helping. They require only information such as the month when the crash occurred, the manufacturer and whether there were injuries. That doesn’t capture the underlying unexpected factors that often cause accidents, such as a person suddenly running onto the street, drivers violating traffic rules, extreme weather conditions or lost cargo blocking the road.

Potential solutions for safer autonomous vehicles

To encourage more data-sharing, it’s crucial to untangle safety knowledge from proprietary data, the researchers said. For example, AV companies could share information about the accident, but not raw video footage that would reveal the company’s technical infrastructure.

Companies could also come up with “exam questions” that AVs would have to pass in order to take the road. “If you have pedestrians coming from one side and vehicles from the other side, then you can use that as a test case that other AVs also have to pass,” Sandhaus said.

Academic institutions could act as data intermediaries with which AV companies could leverage strategic collaborations. Independent research institutions and other civic organizations have set precedents working with industry partners’ public knowledge. “There are arrangements, collaboration, patterns for higher ed to contribute to this without necessarily making the entire data set public,” Qian said.

The team also proposes standardizing AV safety assessment via more effective government regulations. For example, a federal policymaking agency could create a virtual city as a testing ground, with busy traffic intersections and pedestrian-heavy roads that every AV algorithm would have to be able to navigate, she said.

Federal regulators could encourage car companies to contribute scenarios to the testing environment. “The AV companies might say, ‘I want to put my test cases there, because my car probably has passed those tests.’ That can be a mechanism for encouraging safer vehicle development,” Yang said. “Proposing policy changes always feels a little bit distant, but I do think there are near-future policy solutions in this space.”