Journal article Open Access
Züst, Simon; Züst, Rainer; Züst, Viturin; West, Shaun; Stoll, Oliver; Minonne, Clemente
The construction industry implies significant resource demands in two contexts: The supply of primary materials, as well as the conditioning and/or disposing of the materials resulting from excavation and demolition processes. Primary materials are limited and resource intensive in procurement, as well as in distribution. Landfill on the other hand requires brownfield sites and implies the risk of inert contamination of the soil. Manufacturing, re-use and recycling of these materials are thus an important step towards a circular economy and government agencies are interested in steering these material flows. From an economic point of view, these systems can be very volatile: Prices, availabilities and process capacities are fluctuating, while the locations of the construction sites change on a monthly basis. This paper presents a new approach using a digital twin of such a system to identify effective steering inputs and assist government agencies to understand the system and predict the influence of any potential measures. Based on a reduced graph representing the transportation network of the region, statistical information about the price structure of materials and services in the region as well as the decisions of different players within the systems are modelled. Using the Monte Carlo method, different material flows are quantified based on a set of statistical location parameters. The application of this model in the identification of steering inputs, as well as in the evaluation of potential scenarios is successfully demonstrated on two practical examples.