Machine Learning and Optimization techniques for Steel Connections

Main Projects, Uncategorized

Presented at IASS 2018 at MIT

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Most part of their time, engineers around the globe are asked to design over and over again the same or very similar structures, without just taking what has already been done or learning from the past.


Arguably the same structural elements with given acting forces and constraints have been designed thousands of times and the same can be said for architectural finishing, MEP, and in principle to entire buildings which could be informed from previous similar ones. Also, traditional methods for optimizing structures require a large number of resources and time, whereas an AI-based approach can draw from previous knowledge. The more such an approach were used, the more the data points collected and therefore the more efficient it would become. This paper tries to take a step further in the realm of structural optimization by having a fresh look at machine learning strategies to address one of the above-mentioned challenges: the automatic design of steel end-plates.

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