Turning the existing building stock into a resource mine: Proposal for a new method to develop building stock models

Abstract

The construction sector is facing an important challenge to reduce its resource consumption. A promising strategy is to reduce the need of virgin resources by using the existing building stock as a resource mine. Various insights are needed to enable this. It should be clear how many materials are in the stock, when these will become available and to what extent these can be reclaimed in an environmentally and economically viable way. For this purpose a spatio-temporal building stock model is being developed and tested on the city of Leuven, Belgium. In a next step it will be assessed how these flows can be reclaimed in an environmentally and economically viable way. This paper provides a review on the methods used for building stock modelling and proposes improvements on the bottom-up archetypes scaling method. Building parameters relevant to material reuse and are introduced and a new methodology for upscaling is presented, using two data analysis techniques: a clustering algorithm and an artificial neural network.