Connecting Artificial Intelligence and Structural Glass Engineering – Overview, Potentials and Case Studies

Abstract

This paper introduces the Artificial Intelligence (AI) technology to structural glass engineering and glass industry audience. The first part of the paper is concerned with lying nomenclature and theory foundation of AI and its subclasses of Machine and Deep Learning (ML/DL), elaborating the specific needs and requirements for the application in a structural glass context. A subsequent section explores applications of AI for different subjects within the production and quality assessment of glass products as well as the design, verification and monitoring of facades and glass structures. This paper presents successfully conducted industry projects by the authors, which are: supervised ML for material parameter identification of polymeric interlayers used in laminated glass, the prediction of sound insulation properties of insulation glass units and glass laminates and the application of computer vision DL methods to image classification of the Pummel test. A visionary outlook highlights how to use AI for future generative design and verification of glass structures for rapid collaborative prototyping. The summary and conclusion section wraps up the main findings for the applicability and impact of AI for the presented structural glass research and industry problems. This paper shows, that already by today in many cases AI, data, software and computing resources are already in place to successfully implement and conduct AI projects in the glass industry and structural glass engineering practice.