Category: ARCH 692B: Building Science Thesis

Indoor environment quality (IEQ) is a significant component in people’s daily life because people spend most of their time to stay indoors. The IEQ has been thoroughly studied in previous research in daytime. However, the research on the relationship between IEQ and sleep quality is insufficient. It is essential to provide a meta-analysis of the bulk of research about IEQ, sleeping quality, and IoT to suggest a novel tendency of the development to improve sleeping quality through using IoT and wearable sensors to maintain indoor environment quality automatically.

This research was conducted to find out the relationship between humans’ bio-signals and electrochromic windows, which could be useful to create a possible mechanism of using bio-signals to control the windows. By using wearable sensors and remote sensors, subjects’ bio-signals like heart rate, skin temperature, and pupil sizes, and indoor environmental quality like temperature and humidity could be monitored and analyzed. At last, by utilizing machine learning and data analysis skills, the prediction model of human’s thermal and visual sensation could be developed.

Utilizing IES to conduct energy simulation to see if a net zero energy building is achievable.

Revit, 3ds Max, Unity 3D, and other software were used as experiments to model the elements in the wall based on the AR foundation and matching existing models with real-world architecture. Different from the tracking location method in other research, a start location was created to make it easier to match the digital with the real world. Then, users can click to choose the wall, the floor or the roof and hide it to show the inside components. And the components can also be moved by the button on device to realize the human-computer interaction. In addition to AR imaging, a demonstration animation was added to show the optimal disassembly method of a complex structure group or complex components in the wall.