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

摘要

Green building information modeling (Green BIM) is focused on a project using BIM as a basic tool from the beginning of the design stage and employs building performance analysis (BPA) in the design-analysis decision-making cycle to obtain an optimized design proposal. However, there are inevitable discrepancies between the simulated performance data and the data obtained from the actual environment. Neural network learning can be used in conjunction with training to obtain a predictive ability, and the resulting predictive values are more representative of actual performance than simulation values. In this study, it is proposed that a predictive value be used instead of a simulation value in judging whether design goals have been met. To construct an adaptive building envelope based on daylight simulation, this project plans to carry out the following six steps in a two-stage process: Stage 1: Data collection and learning: (1) BIM modeling, (2) BPA performance simulation, (3) production of an actual structure and illuminance measurement, and (4) collection of sample data to perform training in supervised neural network learning. Stage 2: After obtaining a predictive ability: (5) setting targets to find an optimized adaptation plan and (6) implementation of script-oriented automatic control.