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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.

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.

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.

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.

University students spend a considerable amount of time in dorm rooms, where their environmental condition affects residents' health, well-being, sleep quality and the associated performance. Accordingly, this study aims to run an initial assessment of the environmental quality of two dormitory buildings in Tehran, using field studies and computational simulation, and then provide feasible optimized improvement strategies. The possible correlation between architectural elements and the environmental quality and the impact of proposed solutions on the annual energy use of these spaces are also discussed.

Field studies and computational simulation.

Results indicate that applied strategies, including shadings, reflectors, thermal and acoustic insulations, inlet vents and ceiling fans, can boost different aspects of the thermal condition, ventilation, acoustics and visual comfort by 21.77, 55.96, 20.69 and 50.37%, respectively. Accordingly, an acceptable comfort level can simply be achieved at a low cost by installing or replacing a few construction elements in dorm rooms. Nevertheless, a systematic architectural design can offer healthy spaces. For instance, south-facing rooms with large windows provide a higher level of thermal comfort and daylight quality.

This study shows that an acceptable level of IEQ can be achieved in dorm rooms by applying simple retrofit strategies. Moreover, energy consumption of dormitories can be significantly reduced using these solutions. However, the efficiency of the strategies in comparison to their economic aspects should be discussed, and results need to be further validated in real conditions. It is also recommended that a more extensive range of dormitory room typologies be studied in future studies. The results of this study are limited to the study context and so they can only be applied in case studies with similar use and climatic condition.

While many studies have explored the environmental quality of dormitories in different climatic conditions, no significant work has been found in Iran, Tehran investigating feasible optimized improvement strategies responding to all IEQ aspects of acoustics, thermal comfort, air and visual quality. Accordingly, this study makes an initial assessment of IEQ factors in a typical dormitory complex, and then develops practical retrofit strategies to bring the environmental condition of these spaces close to the suggested standards.