Reincarnate continues advancing data-driven approaches for circular construction. The newly published Deliverable 1.3 – Probabilistic methods for lifecycle assessment and prediction of buildings and construction products provides the foundation for more reliable and realistic evaluation of reuse potential and material longevity.
The report outlines how IoT sensors, Markov Chains, and Bayesian Networks can be combined to model degradation, reuse potential, and maintenance intervals over time. This approach enhances the Circular Potential Information Model (CP-IM) by adding predictive power for lifecycle extension and reuse planning — a crucial step toward data-centric circular construction.
Probabilistic methods help transform lifecycle assessment from static evaluation to a dynamic process that captures the true behaviour of buildings and materials over time!
The deliverable was a joint effort led by Benjamí Moreno Torres, with contributions from TUB, BAM, DMO, PlanB, and HUST, and reviewed by Timo Hartmann, Samaneh Rezvani, Angela Greco and Ghezal Ahmad Zia.