Developing an Automated Design Decision Support Software for Productive Indoor Environment (ADSPE)


The BIM process is providing an opportunity for design teams to unlock new methods of collaboration and bring together data rich digital models during all stages of the design and construction process. Digital BIM based technologies are allowing architects and designers to fully prototype a buildings performance through the implementation of software tools to ‘model’ the fabric of the building and subsequently use high level toolkits to simulate the environmental and energy performance of the building over its lifecycle. Occupation and pedestrian movement around the building can also now be modelled to understand how design changes may affect the logistics of human movement. All these emerging processes are allowing a full range of what-if scenarios to be undertaken on design strategies and materials to provide better quality buildings. The development of sensor-based technologies has provided the ability to measure the telemetry of the building during operation once completed, however, these are focused on post completion and post occupancy. Whilst implementing these new digital workflows has provided step change in how buildings are designed and monitored during operation, the analysis has hitherto been confined to the noted functional elements and has given no consideration to the operational metrics of those who inhabit or work in the building.


this project aims to develop a novel platform to allow designers to model and simulate the productivity of the occupants of office buildings during the design stage. The previous study generated trained Neural Network models that can predict occupant productivity based on indoor environmental parameters like Temperature, Carbon Dioxide, Volatile Organic Compound (VOC), Sound level, Lux level, Office Layout. This innovation significantly enhanced the Global Sustainability Assessment System (GSAS) by incorporating elements of occupant productivity, health and well-being and behaviour change making it the pioneer among green building guidelines globally. This new project will advance this work even further by integrating the Artificial Neural Network (ANN) with existing design tools within a BIM framework. This will revolutionize the design process by providing a new range of feedback data to the design team allowing them to design buildings to ensure maximum productivity of the occupants.


Building on successful work undertaken in NPRP No: 7 – 344 – 2- 146 “Developing Green Building Guidelines for Occupant Productivity, Wellbeing and Behavioural Change: A BIM based Design Planning Approach”  , the project is divided into four phases . A literature review was done on Artificial Neural Network and decision support software development and then followed by development of information exchange and flow process to map the data exchange between proposed decision support software and various architectural design software/tools. This would help the next phase of prototype decision support software development, its testing and validation. The final phase would be dissemination of the research findings and application of newly developed decision support software. These phases are described in depth in the work packages.


The outcome of the project will be a novel decision support software built on scientific artificial neural networks (ANN) that can plug into the existing BIM workflow and integrate with existing BIM software design tools including BIM environmental simulation packages. In the same way that current BIM technologies are used to support the design process and ensure optimized building performance, it is anticipated that this new tool.