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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.

The UNESCO recommendation under the historic urban landscapes (HUL) title and Operational Guidelines (OPG) were used to create dynamic protective boundaries to maintain the integrity and authenticity of Isfahan's heritage waterways. Accordingly, by using GIS and Isfahan urban layers, three protective zones were proposed and evaluated; the central zone, the functional zone and the visual zone.

Heritage waterways in historic cities are not adequately protected against the negative impacts of urban development, and there is a lack of a dynamic protective system to protect their integrity and authenticity. The problem can be observed in Isfahan, a historic Iranian city, where the boundaries of urban heritage waterways (Madi canals) are usually rigid and arbitrary. This study aims to develop a practicable paradigm for determining protection boundaries for Isfahan's Jolfa Madi, an urban heritage waterway.

Compared to the current protective boundaries, the authors found that proposed protective boundaries create a greater amount of protection space, which makes a strong connection among the ecological, historical and socio-economic characteristics of the urban context. Furthermore, the protective zones based on the HUL approach give Isfahan's urban planning policy the opportunity to consider participatory tools, financial tools and regulatory systems.

Many studies have emphasized a fixed-width buffer or an arbitrary distance from the urban waterway's axis (urban heritage) or its banks. Although these protections include technical conservation or setting restrictions on the adjacent buildings and blocks, studying crucial concepts such as urban dynamic, urban heritage context and producing a particular technique for protected boundaries has not been investigated. In this article three dynamic boundaries are delineated with various functions in order to provide urban heritage with dynamic preservation and sustainable development for the historic urban landscapes.