Building feature‐based machine learning regression to quantify urban material stocks: A Hong Kong study

Abstract Urban material stock (UMS) represents elegant thinking by perceiving cities as a repository of construction materials that can be reused in the future, rather than a burdensome generator of construction and demolition waste. Many studies have attempted to quantify UMS but they often fall short in accuracy, primarily owing to the lack of proper quantification methods or good data available at a micro level. This research aims to develop a simple but satisfactory model for UMS quantification by focusing on individual buildings. Generally, it is a “bottom‐up” approach that uses building features to proximate the material stocks of individual buildings. The research benefits from a set of valuable, “post‐mortem” ground truth data related to 71 buildings that have been demolished in Hong Kong. By comparing a series of machine learning‐based models, a multiple linear regression model with six building features, namely building type, building year, height, perimeter, total floor area, and total floor number, is found to yield a satisfactory estimate of building material stocks with a mean absolute percentage error of 9.1%, root‐mean‐square error of 474.13, and R ‐square of 0.93. The major contribution of this research is to predict a building's material stock based on several easy‐to‐obtain building features. The methodology of machine learning regression is novel. The model provides a useful reference for quantifying UMS in other regions. Future explorations are recommended to calibrate the model when data in these regions is available.