The development of Building Information Modelling (BIM) has enabled new opportunities, such as standard data storage and collaborative building design. Moreover, there exist many Life Cycle Assessment (LCA) tools and Building Energy Performance (BEP) simulators that use the Industry Foundation Classes (IFC) exports of BIM platforms as input for further operational analysis. While the extracted IFC files contain numerical and tabular data from the BIM model, the visual data including floor plans and section drawings is often obtained directly from the original 3D software such as REVIT. In this study, we introduce an open-source solution, BatchPlan, for batch processing IFC files of medium- and high-rise building projects, leading to floor plan extraction on a large scale. Furthermore, we have designed a user-friendly graphical interface that allows users to select floors manually. BatchPlan is based on open-source Python packages; thus users can easily edit and adapt it to their specific requirements. The presented solution enables a scalable data generation pipeline for downstream tasks that require extensive quantitative analysis, such as machine learning models to perform material detection, volume estimation, and environmental impact prediction.@en

Optimizing the layout of residential buildings based on daylight performance and view quality is crucial to visual comfort and well-being of building occupants. Machine Learning (ML) methods offer valuable support for performance-based decision-making process at the early-stage building design. In this study, a novel workflow is introduced to integrate ML models into the architectural design process. With the designer’s input floor layout designs, the presented multimodal ML model predicts daylight provision and view quality, which are then translated into practical visual representations by a post-processing step. This approach allows input designs to be evaluated by the ML model, leading to enhanced design decisions while preserving the designer’s autonomy. Results for the best-performing model, implementing ResNet50 and a fully connected network, led to a Mean Square Error (MSE) of 0.0440 and 0.0478, and an R2 score of 0.7411 and 0.7815 for the daylight and view metrics, respectively. The results of the daylight and view predictive models are further interpreted according to different apartment categories and at various resolutions. These results indicate that the method could be viable for predicting daylight provision and view quality in early design tools, providing designers with faster feedback that supports informed decision-making during design iterations. Ultimately, the challenges of the study and further improvements are discussed.@en

As the current environmental crisis and depletion of our energy resources are pushing the Architecture, Engineering, and Construction (AEC) industry toward the design and construction of High-Performance (HP) buildings, new organizational and technological methods of practice, such as Integrated Design Process (IDP) and Building Information Modeling (BIM), have emerged to facilitate this transition. Consequently, Architecture schools are left with the duty of training practitioners with the required holistic vision and technical knowledge for designing HP buildings, technological abilities to work with new BIM tools, collaboration skills to work with cross-disciplinary team members, and theoretical knowledge to run the new processes. Scholars of architectural education are faced with a significant theoretical and practical knowledge gap on how to add all these new layers of knowledge and skills to what is an already saturated curriculum in architecture schools. To address this need, we developed a conceptual framework for teaching an integrated and BIM-based HP design studio for the MS program in Building Science. The experience was successful in creating an effective systematic method for integrating HP design elements in the students' projects, with all the teams achieving their project performance targets in six distinct HP categories of energy consumption, greenhouse gas emissions, health and wellbeing, water management, and resiliency, while meeting reasonable architectural qualities and economic criteria. The key elements of this pedagogical approach, including teamwork, a structured and iterative design process, decision-making mechanism with a high level of attention given to various performance metrics, the use of related BIM technologies, and the evaluation techniques, are introduced, discussed, and recommendations are proposed for future applications.

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Recent advancements in machine learning (ML) in architectural design led to new developments in automated generation of floor plans. However, critical evaluation of ML-based generated floor plans has not progressed proportionally due to the subjectivity and complexity of the assessment, particularly for large and more complex floor plans. Accordingly, a hybrid (quantitative and qualitative) floor plan evaluation scheme is introduced in this study, focusing on multiple architectural aspects. To verify the effectiveness of the proposed framework, the evaluation scheme is applied on the generated floor plans resulting from two baseline computer vision models. The models have been trained on a newly introduced large-scale floor plan dataset called Modified Swiss Dwellings (MSD). The results showed that despite the progression of computer vision models for floor plan generation, they still have difficulty capturing the more complex architectural qualities. In addition, the prospect of floor plan generation and evaluation and possible future developments are discussed.@en

Residential buildings are responsible for a considerable share of energy consumption and carbon emission. To decarbonize by 2050, as agreed in the Paris Climate Accord, immediate action for lowering the environmental impact of the building sector is needed. Environmental building design is a promising path, particularly during the early-stage design when design decisions are more impactful and long-lasting. One of the initial steps in the building design process is site assessment, during which the building context and environmental factors are to be evaluated. The surrounding environment plays a critical role in the building's energy performance and the thermal, visual, and acoustic comfort of its occupants. We choose quantitative approaches to study the complexity of the environmental design with respect to the building context by analyzing environmental cues embedded in architectural drawings that have been given less attention in previous studies. Nevertheless, disclosing site-specific geolocation data of buildings, more specifically residential type, is often challenging due to privacy issues. Therefore, there is a lack of context-related metadata in the current architectural datasets. Whereas simulation data are more available and provide a wealth of contextual information, however, it is less appealing for architects to interpret design patterns from extensive simulation figures. This research focuses on developing an interpretable visualization of the building’s micro-climate context from environmental simulation data without direct access to the geolocation of the site. The environmental context visualization is created from daylight, view, and noise from 3088 multifamily housing presented in the Swiss Buildings data set, merely based on available simulation data. The presented pipeline in this study facilitates the employment of existing simulation data in the built environment datasets while circumventing the concerns associated with geolocation data exposure. Further, the generated visualizations may be used to develop computer vision models for environmental assessments of building layout design.

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In shared office spaces, occupants' comfort criteria are limited to locally controlled zones while ambient features of the environment and the potential negative impacts of others' behavior require a well-designed control system, especially over adaptive façade elements. This means setting up control strategies for a wider spectrum of varying comfort perceptions from person to person dictates an approach towards personalizing adaptive facades. Thereby, this research coupled a simulation-based methodology with fuzzy logic and a genetic algorithm to personalize façade modules based on the visual discomfort conditions of the occupants. Results confirmed that increasing the control freedom by personalization accounting for multi-objective criteria including glare, daylight, and view could satisfy occupants from 83% to 100%. Moreover, the proposed façade personalization framework could enhance visual comfort compared with two typical automated Venetian blind controls, significantly. This study provides novel insights for designers and operators to decentralize facades' elements by accepting occupants’ feedback as part of their control loops.

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Rapid urbanization and global warming have increased heat stress in urban areas. This in turn makes using indoor space more compelling and leads to more energy consumption. Therefore, paying attention to outdoor spaces design with thermal comfort in mind becomes more important since outdoor spaces can host a variety of activities. This research aims to introduce a machine learning-based framework to predict the effects of different urban configurations (i.e. different greening configurations and types, different façade materials, and different urban geometry) on outdoor thermal comfort through training a pix2pix Convolutional generative adversarial network (cGAN) model. For the training of the machine learning model, a dataset consisting of 208 coupled pictures of input and output has been created. The simulation of this data has been carried out by ENVI-met. The resulting machine learning model had a Structural Similarity Index (SSIM) of 96% on the test dataset with the highest SSIM of 97.08 and lowest of 94.43 which shows the high accuracy of the model and it could have reached an answer in 3 s compared to the 30-min average time for ENVI-met simulation. The resulting model shows great promise for assisting researchers and urban designers in studying existing urban contexts or planning new developments. HIGHLIGHTS Machine learning use in outdoor thermal comfort assessment has been investigated. Vegetation, urban geometry, surface albedo, and water bodies have been studied parameters. Vegetation and street orientation have the highest and water bodies have the least impact on outdoor thermal comfort. Pix2pix algorithm implementation could create thermal comfort maps with 96% SSIM.

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Purpose: In this study, a novel framework based on deep learning models is presented to assess energy and environmental performance of a given building space layout, facilitating the decision-making process at the early-stage design. Design/methodology/approach: A methodology using an image-based deep learning model called pix2pix is proposed to predict the overall daylight, energy and ventilation performance of a given residential building space layout. The proposed methodology is then evaluated by being applied to 300 sample apartment units in Tehran, Iran. Four pix2pix models were trained to predict illuminance, spatial daylight autonomy (sDA), primary energy intensity and ventilation maps. The simulation results were considered ground truth. Findings: The results showed an average structural similarity index measure (SSIM) of 0.86 and 0.81 for the predicted illuminance and sDA maps, respectively, and an average score of 88% for the predicted primary energy intensity and ventilation representative maps, each of which is outputted within three seconds. Originality/value: The proposed framework in this study helps upskilling the design professionals involved with the architecture, engineering and construction (AEC) industry through engaging artificial intelligence in human–computer interactions. The specific novelties of this research are: first, evaluating indoor environmental metrics (daylight and ventilation) alongside the energy performance of space layouts using pix2pix model, second, widening the assessment scope to a group of spaces forming an apartment layout at five different floors and third, incorporating the impact of building context on the intended objectives.

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