07 Dec K&C wins AFRL Grand Challenge Award for Machine Learning Prediction of Internal Building Structures
Karagozian & Case was the winning submission in the AFRL Grand Challenge 5 to provide a solution for Machine Learning Prediction of Internal Building Structures. K&C presented their solution, ASPEN (for Adversarial Structural Predictor and Extraction Network), to the Air Force Research Laboratory (AFRL) at the Pitch Day on November 12th, 2021. K&C is excited for this opportunity to use their combined expertise in Machine Learning and Building Structures to develop a new technology for AFRL. K&C was the winning solution out of 25 submitted. This challenge was awarded through the Wright Brothers Institute, National Security Innovation Network, and DEFENSEWERX.
“The goal of this challenge is to predict the internal configuration of structures such as buildings by only using external satellite photographs of the building taken from various angles. Using the shape, size, and external characteristics of the building (such as the number and spacing of windows), the desired methodology should be able to produce a model of the building to include basic structure system (frame vs. load-bearing systems), the construction materials and likely material properties, structural detailing, and any other feature responsible for the overall structural strength of the building. A machine learning model trained with a sufficient database of known buildings, along with constraints to represent current construction processes, could potentially yield a machine learning model that can use a minimal of external visual clues and then automatically produce a digital file of the most likely structural design of the building.” UNUM
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