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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.

In many countries, urban block layouts follow rigid and outdated design guidelines; however, these approaches fail to effectively address settlement construction, neighborhood planning, the design of old urban contexts and the rapid development of emerging cities, as seen in the new towns of Iran and various other regions worldwide. Revising these guidelines requires early-stage decision-making to evaluate building layouts based on their impact on the built environment. This study introduces a workflow for optimizing urban block layouts with constant density while considering design constraints, climate and environmental performance. This framework assists planners and policymakers in refining site density distribution and building massing.

A computational urban design method was developed utilizing a grasshopper-based algorithm and Python for data generation. It introduces an innovative approach by incorporating buildings’ placement coordinates as design variables alongside dimensions and orientation. The Wallacei tool optimizes layouts by considering energy use intensity (EUI), outdoor thermal comfort and facades’ received radiation employing Ladybug Tools. Spatial daylight autonomy (sDA) and urban heat island (UHI) effects are also integrated into the workflow. The framework is applied to a hypothetical residential district in Tehran, consisting of nine blocks, each containing nine plots.

The results for a set of optimal solutions demonstrate that beyond existing urban design policies, environmentally prioritized designs are achievable. Radiation differences between the optimal models and the conventional parallel layout range from 41.3 to 61.1%. However, changes in EUI and universal thermal climate index (UTCI) are minimal; significant improvements are observed in sDA, with 7, 8 or all 9 buildings meeting the acceptable threshold compared to none in the parallel layout. These findings highlight the effectiveness of the design method framework proposed in this research, which has the potential to significantly influence urban planning practices across various regions globally.

The methodology of this study provides a flexible decision-making framework for early design phases, enabling policymakers, architects and urban designers to address the gap between current urban design methods and environmentally driven approaches. Sensitivity analysis of optimal solutions further offers valuable insights into the correlation between design variables and evaluation metrics.