Towards Self- Sufficient High-Rises
Performance Optimisation using Artificial Intelligence
Berk Ekici
All bindings and editions
-
€29.95
Paperback
2022
Towards Self- Sufficient High-Rises
Performance Optimisation using Artificial Intelligence
Berk Ekici
Paperback
|
English
€29.95
- From €15,- no shipping costs.
- 30 days to change your mind and return physical products
Description
Population growth and urbanisation trends bring many consequences related to the increase in global energy consumption, CO2 emissions and a decrease in arable land per person. High‑rises have been one of the inevitable buildings of metropoles to provide extra floor space since the early examples in the 19th century. Therefore, optimisation of high-rise buildings has been the focus of researchers because of significant performance enhancement, mainly in energy consumption and generation. Based on the facts of the 21st century, optimising high-rise buildings for multiple vital resources (such as energy, food, and water) is necessary for a sustainable future. This research suggests “self-sufficient high-rise buildings” that can generate and efficiently consume vital resources in addition to dense habitation for sustainable living in metropoles. The complexity of self-sufficient high-rise building optimisation is more challenging than optimising regular high-rises that have not been addressed in the literature. The main challenge behind the research is the integration of multiple performance aspects of self-sufficiency related to the vital resources of human beings (energy, food, and water) and consideration of large numbers of design parameters related to these multiple performance aspects. Therefore, the dissertation presents a framework for performance optimisation of self-sufficient high-rise buildings using artificial intelligence focusing on the conceptual phase of the design process. The output of this dissertation supports decision-makers to suggest well-performing high-rise buildings involving the aspects of self-sufficiency in a reasonable timeframe.