Driven by governments, all countries are enacting laws related to environmental improvements and establishing policies to reduce greenhouse gases (GHG). Because 26.9% of the reduction plans from a total of 30% of national GHG reduction targets is set in the building area, most countries are inducing green building expansion through the implementation of green building standards that reflect the countries' standards to achieve the 2020 target. To construct buildings that conform to the certification by satisfying the eco-friendliness of buildings, studies that consider this requirement should be performed from the initial design stage. However, there are several complicated work processes and problems in analysing items in detail, recognizing demand related to data, and applying data to work.Accordingly, the development of new applicable techniques is required that can support the information of detailed items in certification more efficiently to vitalize green buildings based on green building standards. From this perspective, this study seeks to propose a practical method to support the design of green buildings using a GBT, BIM-based green template, and to develop the supportive and evaluative environment for the demands of green building standards via GBT.

In architectural planning and initial designing process, it is critical for architects to recognise users' emotional responses toward design alternatives. Since Building Information Modelling and related technologies focuses on physical elements of the building, a model which suggests decision-makers' subjective affection is strongly required. In this regard, this paper proposes an electroencephalography (EEG)-based hybrid deep-learning model to recognise the emotional responses of users towards given architectural design. The hybrid model consists of generative adversarial networks (GANs) for EEG data augmentation and an EEGbased deep-learning classification model for EEG classification. In the field of architecture, a previous study has developed an EEG-based deep-learning classification model that can recognise the emotional responses of subjects towards design alternatives. This approach seems to suggest a possible method of evaluating design alternatives in a quantitative manner. However, because of the limitations of EEG data, it is difficult to train the model, which leads to the limited utilisation of the model. In this regard, this study constructs GANs, which consists of a generator and discriminator, for EEG data augmentation. The proposed hybrid model may provide a method of developing supportive and evaluative environments in planning, design, and post-occupancy evaluation for decision-makers.

In building information modelling (BIM), the amount of information increases and architectural design processes become more complex as projects expand. This is because while a collaboration environment is important for smooth communication among experts, this has not been realized because of unclassified file synchronization and permission settings among team members. Therefore, this study aims to support cooperation in BIM modelling projects by synchronizing BIM data from different computers and rendering BIM project management easier by providing a BIM model viewer and data through the Web. The proposed technology, which is a construction project-type, purpose-tailored browsing technology, provides BIM information related to construction environments and planning processes only to the relevant experts.

In the existing research on the carbon dioxide emission of buildings, the amount of construction materials in the construction phase is calculated based on the quantity computation sheet. This amount must be recalculated according to the construction material of the quantity computation sheet when changing the construction design; thus, the reliability and compatibility of the quantity calculation is difficult to achieve. If BIM-based standardized data are used, users can immediately apply the edited factors in the design stage. Moreover, since efficiency and compatibility increase, the accuracy of the analysis and computation of CO 2 emission from various building materials can be expected.The purpose of this paper is to present a BIM-based building carbon dioxide emission quantity assessment method to analyse the reduction of energy consumption and the CO 2 emission quantity objectively and quantitatively. The accuracy of BIM-based quantity estimation according to major construction materials is examined based on the BIM library and the modelling construction method, and guidelines are provided to the users.

In this study, we propose an electroencephalogram (EEG)-based long short-term memory networks model for recognizing user preferences toward architectural design images. An EEG is an approach that records the electrical activity in the brain, and EEG-based affection recognition is a technique used for quantitatively recognizing human emotion by analysing the recorded signals. Decision-makers’ subjective reactions toward architectural design alternatives may play a key role in the architectural planning and design stage. In this regard, the proposed model enables the quantitative recognition of their preferences and supports architects in the planning and design stages. The suggested model classifies the recorded data using a deep-learning technique. To build the model, an EEG recording experiment was conducted with 18 subjects, who were asked to select their most/least preferred images among eight images of small-housing design. Post recording, a positive and negative affect schedule questionnaire was distributed to the subjects to rate their affection. Google TensorFlow and Keras were used to structure the model. After training, precision, recall, and f1 score metrics were used to evaluate and validate the model. This model can help designers to evaluate design alternatives in terms of decision-making. Moreover, as this model uses biosignal data, which is universal to humans, architectural design processes for children, the elderly, etc., may be supported. Furthermore, a data-driven design database may be proposed in a future research for cross-validating with previous methods such as interviews and observations.