Maria Sivers
Expertise
#architectural design #urban design #history of architecture #sustainability #reconstruction #sacral architecture #circular economy #lca #architectural heritage #architectural graphics #theory of architecture #reuse #computational design #management #startups #innovations
Mission
Passionate about sustainability, circular economy goals and reuse in real estate and construction, combined with ML/ AI innovations.
Fellow of the EPFLinnovators Programme. The project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 754354.
Fellow of the EPFLinnovators Programme. The project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 754354.
Current work
"The Reuse Viability Index (RVI): Classification, Assessment and Prediction of the Sustainability and Cost-effectiveness of Building Component Reuse for Real Estate Due Diligence"
Circularity and sustainability assessments aligned with Global Sustainable Goals (SDGs), Circular Economy (CE), and Environmental, Social, and Governance (ESG) metrics are fast-paced requirements in real estate due diligence. Enhancing sustainability benchmarks involves extending building component lifespans to reduce CO2 emissions and waste. Integrating components into a closed loop demands efficient reuse potential evaluation within dynamic market trends, policy shifts, and technological advancements. Traditional reuse assessments rely on scarce, slowly trained experts, and there is a lack of scalable digital means to extend their expertise application.
The main research question in this thesis is whether it is possible to develop a Reuse Viability Index (RVI), an AI-driven decision-making tool to measure and predict the sustainable viability of building component reuse, classifying components by their reusability drawing upon resource assessments, open data, market value, and cost-effectiveness without continuous expert interaction.
Key words: circularity metric, composite index, ESG, graph database, multicriteria assessment, reusable building components, reuse, reuse assessment, reuse classification, reuse index, reuse market, reuse potential, reverse logistics, SDG, sustainability, sustainability benchmark, sustainability metrics
Circularity and sustainability assessments aligned with Global Sustainable Goals (SDGs), Circular Economy (CE), and Environmental, Social, and Governance (ESG) metrics are fast-paced requirements in real estate due diligence. Enhancing sustainability benchmarks involves extending building component lifespans to reduce CO2 emissions and waste. Integrating components into a closed loop demands efficient reuse potential evaluation within dynamic market trends, policy shifts, and technological advancements. Traditional reuse assessments rely on scarce, slowly trained experts, and there is a lack of scalable digital means to extend their expertise application.
The main research question in this thesis is whether it is possible to develop a Reuse Viability Index (RVI), an AI-driven decision-making tool to measure and predict the sustainable viability of building component reuse, classifying components by their reusability drawing upon resource assessments, open data, market value, and cost-effectiveness without continuous expert interaction.
Key words: circularity metric, composite index, ESG, graph database, multicriteria assessment, reusable building components, reuse, reuse assessment, reuse classification, reuse index, reuse market, reuse potential, reverse logistics, SDG, sustainability, sustainability benchmark, sustainability metrics