Development of Post Occupancy Evaluation Method for Climate Adaptive Building Shells: In Case of User Satisfaction Assessment of CABS

Building development is one of the human activities with the greatest impact on sustainability. Many alternatives to address sustainability in architecture have been proposed in recent years and Climate Adaptive Building Shell (CABS) are one of the most promising solutions to date. CABS provide a response to this problem due to their adaptability and modifiability. Although CABS are promising solution for reducing energy consumption of the building, they have reputation for decreasing the occupant’s satisfaction. The examination of the CABS literature review using PRISMA methodology reveals that most of the research attention in this subject has been focused on economic and technological factors, while the occupant-centric study has been neglected. As a result, the fundamental purpose of this thesis is to conduct an occupant-centric study. The CABS literature and database were investigated in the first phase of this thesis. As a result, CABS office buildings were evaluated based on user-facade interaction types and scenarios to achieve the study's occupant-centric purpose. Finally, a new taxonomy for CABS office buildings that covers a wide range of topics was introduced. In the other hand, the literature review highlights the lack of research on occupants’ satisfaction, productivity and needs for this system. POE has traditionally been an effective approach for assessing user satisfaction. However, in its current state, this technique cannot fulfill the CABS's complexity and multi-domain properties. Thus, in second phase of study, after analyzing POE in general and identifying comfort characteristics, an attempt was made to design a unique POE model that can fulfill CABS requirements. As a result, a dynamic cloud-based POE was created within three main stages. In the first stage the model has been conceptualized with help of Human-Computer Interaction and Internet of Things Principles. The occupant-centered model tries to pinpoint the precise moment of discomfort. In the second stage, application of the model has been studied. For the Machine Learning of the model an active surrogate model with Artificial Neural Network architecture method has been applied. Lastly the relational data management system of the model has been studied. In last step the implementation of the model has been discussed. Finally, data assessment approaches for multi-criteria analysis and decision-making for CABS has been presented. The proposed model fills a gap in the literature of CABS studies linked to POE; nevertheless, due to a lack of recorded case studies on CABS, creating practical research on CABS for POE using proposed model is required.