The curation design of cultural heritage sites, such as museums, influence the level of visitor satisfaction and the possibility of revisitation; therefore, an efficient exhibit layout is critical. The difficulty of determining the behavior of visitors and the layout of galleries means that exhibition layout is a knowledge-intensive, time-consuming process. The progressive development of machine learning provides a low-cost and highly flexible workflow in the management of museums, compared to traditional curation design. For example, the facility’s optimal layout, floor, and furniture arrangement can be obtained through the repeated adjustment of artificial intelligence algorithms within a relatively short time. In particular, an optimal planning method is indispensable for the immense and heavy trains in the railway museum. In this study, we created an innovative strategy to integrate the domain knowledge of exhibit displaying, spatial planning, and machine learning to establish a customized recommendation scheme. Guided by an interactive experience model and the morphology of point–line–plane–stereo, we obtained three aspects (visitors, objects, and space), 12 dimensions (orientation, visiting time, visual distance, centrality, main path, district, capacity, etc.), 30 physical principles, 24 suggestions, and five main procedures to implement layout patterns and templates to create an exhibit layout guide for the National Railway Museum of Taiwan, which is currently being transferred from the railway workshop for the sake of preserving the rail culture heritage. Our results are suitable and extendible to different museums by adjusting the criteria used to establish a new recommendation scheme.
Continual revisions and enhancements to the presentation in museum will allow visitors engagement to remain high interest and acquire visiting benefits when interaction within the display objects. The layout task of objects in exhibition gallery of museum is quite complex, high-cost, time-consuming, and laborious manual process. It is essential and necessary to establish a customized recommendation scheme of exhibition spatial layouts to provide museum crews the configuration frameworks of gallery to improve the efficient of exhibition layout. According to the interactive experience model in museums, we suggest three dimensions: the visitors' behavior, the role of objects, and the layout of space, will benefit to looking for affective and embodied procedures and physical principles of exhibition layout. On the other hand, the state-of-the-art machine learning of artificial intelligence has been widely applied in lots of professional fields (e.g. diagnosis, monitory, prediction, classification, interpretation, scheduling). According to the attributions of exhibition layout and the characteristic of machine learning methods, we suggest that machine learning is a great potential and powerful approach to build up a customized recommendation scheme of exhibition layouts based on the previous knowledge of layout, and it is worth to develop and implement in future research.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.