AFRL Machine Learning Challenge

The use of machine learning techniques to enable modeling of complex problems is of significant interest to the Department of Defense (DoD). One goal that could benefit from machine learning is predicting the structural response of buildings, and a key input to this analysis is a good understanding of the building structure and the structural detailing. Often the Department of Defense does not have access to blue-prints or even information on internal layout of a building, but must nonetheless estimate the internal structure before doing an analysis of the structural response of the building due to various loads. The current approach used in developing a target structure for a structural response analysis is for a skilled person sit down and use specialized software to make representative models of buildings and other structures based on standard construction templates and structural design rules of thumb. This is a time consuming process that the Department of Defense would like to be largely automated.

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 minimum of external visual clues and then automatically produce a digital file of the most likely structural design of the building. The prediction of the number of configurations of non-load bearing partition walls would also be a benefit and should be regarded as an additional secondary challenge.

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