Enhancing Validity of Green Building Information Modeling with Artificial-neural-network-supervised Learning - Taking Construction of Adaptive Building Envelope Based on Daylight Simulation as an Example

Shang-Yuan Chen

doi:10.18494/sam.2019.2147

Abstract

Enhancing Validity of Green Building Information Modeling with Artificial-neural-network-supervised Learning - Taking Construction of Adaptive Building Envelope Based on Daylight Simulation as an Example

Highlights

Integrated design and analysis procedures based on green building information modeling (Green BIM) have become an important tool for architects and design teams wishing to select and improve design proposals
This study addresses the subjects of Green BIM and artificial neural network (ANN)supervised learning
In accordance with the theory and method outlined in this paper, a six-step, two-stage process was employed to verify the optimization strategy in a virtual environment, construct an adaptive mechanism based on the light environment in a physical environment, and perform script-oriented automated control

Summary

Introduction

Integrated design and analysis procedures based on green building information modeling (Green BIM) have become an important tool for architects and design teams wishing to select and improve design proposals. When using building performance analysis (BPA) software to predict building performance in actual environments, there are inevitable discrepancies σs between simulation data obtained from the software and measurements in the actual environment (Fig. 1), which have caused the validity of the software’s simulation performance to be questioned.

Literature Review

Theory and Method

Stage 2

Conclusions and Recommendations