Introduction The construction industry is one of the world's largest carbon emitters,
accounting for around 40% of total emissions. Therefore, reducing carbon emissions from …

Convolutional and adversarial networks are found in various fields of knowledge and
activities. One such field is building design, a multi-disciplinary and multi-task process …

This study introduces a novel framework that leverages artificial intelligence (AI), specifically
deep learning and reinforcement learning, to enhance energy efficiency in architectural …

The new construction that is projected to take place between 2020 and 2040 plays a critical
role in embodied carbon emissions. The change in material selection is inversely …

Designing energy-efficient buildings is an essential necessity since buildings are
responsible for a significant proportion of energy consumption globally. This concern is even …

Accurate prediction dramatically enhances the effectiveness of carbon emission control,
making it a valuable tool for achieving decarbonization goals. In this work, we propose a …

The consumption of energy in buildings has elicited the occurrence of many environmental
problems such as air pollution. Building energy consumption prediction is fundamental for …

Reducing carbon emissions from buildings is crucial to achieving global carbon neutrality
targets. However, the building sector faces various challenges, such as low accuracy in …

The analyzed research issue provides a model for Carbon Footprint estimation at an early
design stage. In the context of climate neutrality, it is important to introduce regenerative …

The ever-changing data science landscape is fueling innovation in the built environment
context by providing new and more effective means of converting large raw data sets into …