Applied Machine Learning in Structural Glass Design

Machine learning, a type of artificial intelligence, is becoming increasingly prevalent in everyday life. Email spam filters, autonomous cars, and speech recognition all rely on machine learning algorithms to function accurately and efficiently. Such algorithms allow computers to learn without being explicitly programmed, allowing them to grow increasingly accurate with more data.

This paper will explore the potential of this technology to assist in the field of structural glass design. To demonstrate this, machine learning algorithms will be trained on a database of analytical and computational structural glass solutions. Once trained, the algorithm’s accuracy will be assessed and used to predict glass build-ups for a variety of geometries and applied loads.

When used in combination with experienced structural engineers, such intelligent predictors have significant benefits in early stage design, allowing rapid and accurate assessment of glass.