Harnessing AutoCAD designs with machine learning for smart building optimization

One intriguing approach to enhancing building architecture, increasing energy efficiency, and automating predictive maintenance is to integrate AutoCAD designs with machine learning (ML). This study investigates the ways in which CAD data and AI methods might improve design procedures and advance sustainability. Among the most important uses are automated design optimization methods, such as genetic algorithms and reinforcement learning, which improve natural lighting, ventilation, and thermal insulation while lowering energy use. Additionally, by examining architectural elements taken from CAD files, machine learning models like Random forest and Classification Models may mimic energy performance and allow for data-driven design modifications.
In order to simplify feature recognition and analysis, the study also explores the use of computer vision models, such as ANN, to extract geometric characteristics from AutoCAD drawings. Still, issues including insufficient datasets, high processing costs, and incompatibility with conventional design tools must be resolved. This study lays the groundwork for using AI to promote smart construction practices and sustainable urban planning by analyzing recent research and pointing out new trends