Immersive virtual reality for extending the potential of building information modeling in architecture,engineering,and construction sector:systematic review

The field of architecture,engineering,and construction(AEC)is continually striving to use resources efficiently and manage complex processes.Now more than ever,there is a strong need for digital transformation in AEC.The improvement in the accessibility of consumer-based head-mounted displays(HMD)is attracting different entertainment and research fields to immersive virtual reality(VR)applications.Building Information Modeling(BIM)is known as a promising technology in AEC.The full potential of BIM is not yet employed to empower this field,however,and this could be a result of some barriers still to be surmounted by BIM in both technological and management perspectives.One of these barriers is the communication and collaboration between design,construction,operation,and maintenance phases.VR can fill this gap by providing additional capabilities for BIM which either were not available before or were not possible to employ in practical ways.In this paper,we systematically review recent research around the application of VR in BIM and discuss the results using the PRISMA flowchart.We discuss the most commonly used technologies,software,and evaluation methods and the various applications of VR in the reviewed papers.Finally,we extend the discussion by summarizing the potential future work in this area.

Interactive visual labelling versus active learning:an experimental comparison

Methods from supervised machine learning allow the classification of new data automatically and are tremendously helpful for data analysis.The quality of supervised maching learning depends not only on the type of algorithm used,but also on the quality of the labelled dataset used to train the classifier.Labelling instances in a training dataset is often done manually relying on selections and annotations by expert analysts,and is often a tedious and time-consuming process.Active learning algorithms can automatically determine a subset of data instances for which labels would provide useful input to the learning process.Interactive visual labelling techniques are a promising alternative,providing effective visual overviews from which an analyst can simultaneously explore data records and select items to a label.By putting the analyst in the loop,higher accuracy can be achieved in the resulting classifier.While initial results of interactive visual labelling techniques are promising in the sense that user labelling can improve supervised learning,many aspects of these techniques are still largely unexplored.This paper presents a study conducted using the mVis tool to compare three interactive visualisations,similarity map,scatterplot matrix(SPLOM),and parallel coordinates,with each other and with active learning for the purpose of labelling a multivariate dataset.The results show that all three interactive visual labelling techniques surpass active learning algorithms in terms of classifier accuracy,and that users subjectively prefer the similarity map over SPLOM and parallel coordinates for labelling.Users also employ different labelling strategies depending on the visualisation used.