Artificial intelligence for structural glass engineering applications — overview, case studies and future potentials

Abstract

’Big data’ and the use of ’Artificial Intelligence’ (AI) is currently advancing due to the increasing and even cheaper data collection and processing capabilities. Social and economical change is predicted by numerous company leaders, politicians and researchers. Machine and Deep Learning (ML/DL) are sub-types of AI, which are gaining high interest within the community of data scientists and engineers worldwide. Obviously, this global trend does not stop at structural glass engineering, so that, the first part of the present paper is concerned with introducing the basic theoretical frame of AI and its sub-classes of ML and DL while the specific needs and requirements for the application in a structural engineering context are highlighted. Then this paper explores potential applications of AI for different subjects within the design, verification and monitoring of façades and glass structures. Finally, the current status of research as well as successfully conducted industry projects by the authors are presented. The discussion of specific problems ranges from supervised ML in case of the material parameter identification of polymeric interlayers used in laminated glass or the prediction of cut-edge strength based on the process parameters of a glass cutting machine and prediction of fracture patterns of tempered glass to the application of computer vision DL methods to image classification of the Pummel test and the use of semantic segmentation for the detection of cracks at the cut edge of glass. In the summary and conclusion section, the main findings for the applicability and impact of AI for the presented structural glass research and industry problems are compiled. It can be seen that in many cases AI, data, software and computing resources are already available today to successfully implement AI projects in the glass industry, which is demonstrated by the many current examples mentioned. Future research directories however will need to concentrate on how to introduce further glass-specific theoretical and human expert knowledge in the AI training process on the one hand and on the other hand more pronunciation has to be laid on the thorough digitization of workflows associated with the structural glass problem at hand in order to foster the further use of AI within this domain in both research and industry.

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

“…The AI model relies on the input of grey-value images after pummeling the laminated glass. This example showed that an AI-approach can successfully be trained to standardize, automate and objectify human-based classification of pummel images into the Pummel categories during production control along with a statistically sound quantification of uncertainties of this process (further details can also be found in [5]). Since the training of the CNN within this example was based on a small amount of publicly available data, more theoretical justification for a potential Pummel class lumping along with a quantification of the improvement of the performance and robustness of the CNN and further investigations on alternative architectures or even alternative approaches such as clustering [35] has to build upon future studies with an increasing amount of ground-truth Pummel images.…”

“…This system uses adaptive elements within the facade (windows, shading elements, air conditioning) and sensors (humidity, wind speed, temperature, user feedback via smart watches) together with an AI controlling unit to process all data and learn over time the individual preferences of the occupants. Further details and explanations can be found in [5].…”

“…This last subsection highlights three main future potentials for the application of AI in design, verification and monitoring of glass structures in short, further details, discussions and examples in this context can be found in [5].…”

Predicting Nanobinder-Improved Unsaturated Soil Consistency Limits Using Genetic Programming and Artificial Neural Networks

“…Studies have shown that a combination of AI algorithms can boost the prediction efficiency [22]. Overall, the fields of statistics, numerics, and optimization are critical aspects that facilitate understanding the data, describing the properties of a dataset, finding relationships and patterns in the data, as well as choosing and implementing the suitable AI model.…”

Physik‐informierte Künstliche Intelligenz zur Berechnung und Bemessung im Stahlbau

“…Zu allen drei genannten Punkten existieren erste Publikationen zur Anwendung von KI oder einer Unterform. Eine glasbauzentrierte Diskussion von Potenzialen zum Einsatz von KI und eine Sammlung spezifischerer Details zu den drei genannten Bereichen ist in zu finden. Es ist allerdings insgesamt festzustellen, dass die heute existierenden Ansätze noch weitere Jahre der Forschung und Zusammenarbeit zwischen den verschiedenen Disziplinen und insbesondere der Praxis und Wissenschaft benötigen, um die benötigte Zuverlässigkeit und Qualität für einen gewinnbringenden Einsatz im Planer‐ und Baualltag zu erreichen.…”