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Showing posts with label Streets. Show all posts
Showing posts with label Streets. Show all posts

Monday, March 27, 2023

Towards Talking Concrete

Novel infrastructure idea.

'Talking' Concrete Could Help Prevent Traffic Jams, Cut Carbon Emissions

By Purdue University News

March 16, 2023

From beneath a concrete pour, this black circular sensor transmits data about the concrete's strength levels through a cord plugged into an above-ground handheld device called a data logger.

Credit: Rebecca McElhoe/Purdue University

U.S. interstates are preparing to test sensors developed by Purdue University researchers that could help prevent congestion and lower carbon emissions.

The sensors enable concrete pavement to relay data about its strength and repair requirements to engineers, forgoing the need to test samples; this should reduce construction time and repair frequency while cutting emissions from vehicles waiting to bypass construction sites.

The devices communicate to engineers through a smartphone application when the pavement is sufficiently strong to tolerate heavy traffic.

More than half of U.S. states with concrete interstate pavement will deploy the sensors as part of a Federal Highway Administration study, with Indiana and Texas already testing them in highway paving projects.

The WaveLogix company will manufacture the technology for commercialization as the REBEL Concrete Strength Sensing System.

From Purdue University News

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Friday, August 05, 2022

Redesigning Streets with AI

Worked with  civil engineers early in my career who worked with this kind of designing, fascinating approach

Think Your Street Needs a Redesign? Ask an AI

Bloomberg, Marie Patino, July 30, 2022

Brooklyn-based artist and former activist Zach Katz is using artificial intelligence (AI) laboratory OpenAI's DALL-E 2 system to redesign streets to be more friendly to pedestrians and bikes. DALL-E 2, the second iteration of the DALL-E neural network, can convert text into visual features and display how these features could relate to one another, thanks to a massive training dataset. Users can input text queries and request specific styles and features from the AI, although results can be biased against women and people of color due to a lack of diversity in its training data. Katz posts images of DALL-E-reimagined streets on Twitter, and has a backlog of requests for such reimaginings from around the world.  ....