Abstract
Machine learning (ML) is a major subfield of artificial intelligence (AI) that provides systems, the ability to automatically learn and improve from experience without being explicitly programmed. With its ability to capture complex behaviour of structures and systems, ML has been proposed as a solution to overcome the limitations of conventional methods in Structural Engineering. This paper is an insight in to a few of such applications, based on neural networks, Support Vector Machines and Nearest Neighbours, projecting their accuracy in performance.
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