Laurent Deschamps

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Most current software support the Industry Foundation Classes (IFC) transfer of Building Information Models (BIMs) to applications enabling Virtual Reality (VR) navigation (BuildingSMART, 2020; Kiviniemi, Tarandi, Karlshøj, Bell, and Karud, 2008; Poljanšek, 2017). However, a review of current industry practice and scientific literature shows a rupture between the information concerning the materiality of objects stored in the IFC model data and the texture mapping image-source determining the objects' appearance in VR environments. This document presents an application based on the IfcOpenShell library (IfcOpenShell, 2020), which provides a mapping table allowing to align the retrieved relevant IFC model data to any chosen set of image or pattern files. It produces a DAE file and a linked repertoire defining texture mapping onto model objects visualised in VR. The application also allows the automatic updating after modification of the original model.

2021

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Professionals involved in building construction and monitoring increasingly implement new practices made available by Building Information Modelling (BIM), Virtual Reality (VR) and the Internet of Things (IoT). In spite of numerous efforts, most methods integrating BIM and IoT rely on Application Programming Interfaces (APIs) related to Closed Source Software (CSS), which practically exclude the basic BIM Industry Foundation Classes (IFC) standard. Within the fewer methods actively using the IFC data model to VR-enhanced interfaces, we can observe the loss of crucial IFC information. This article describes a workflow of a BIM-IFC to VR-enabled data management definition, articulating a Building Management Systems' (BMS) database or, as an alternative, an IoT system. This workflow is presented through four phases (model making, device inventory, protocol setting, and real-time VR interaction), which correspond to the actual sections of the information flow that we have successfully tested.

2021

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Rule-based control logics have proven to be efficient for automation of shading and lighting systems. However, successful commissioning of these systems for multiple stores non-residential buildings, regardless of the control logic, requires time-consuming programing and fine-tuning of numerous parameters. In this article, a control approach is suggested to overcome the limitations of the rule-based control systems: i) necessity for extensive information on the office room, ii) costly adaptation of control parameters to a new environment, and iii) no feedback to the control system in the case of mal-function. A novel self-commissioning approach is proposed: a set of open-loop geometry-based rules are enhanced with a supervised learning module for fine-tuning seven tunable parameters. This approach was validated through an in-situ experiment for 22 days in a daylight testbed equipped with internal venetian blinds, dimmable electric lighting system, a miniaturized accurate High Dynamic Range vision sensor to evaluate Daylight Glare Probability (DGP), horizontal illuminance meters and a pyranometer. The goal was to command the shading and electric lighting system to keep the DGP and horizontal illuminance in a predefined visual comfort zone. A novel visualization method is proposed to demonstrate the performance of the automatic system in leading the indoor environment to the visual comfort zone. After 11 days, the learning module reached the convergence state. Afterwards, the controller was capable of successfully confining the indoor illumination conditions to the visual comfort zone for 96% of the working hours while actuating the shading system efficiently; on average 2.54 times a day.

2020