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Data-driven workflow improves cost savings by 15% and carbon impact reduction by 25% through predictive modeling and design sampling.
In this research, I have developed a data-driven workflow that recommends sustainable building solutions based on lifecycle cost ($) and carbon impact (kgCO2eq). The workflow can analyze conceptual geometries in the early design stage when there is limited information. By following recommended ranges of numerical attributes and options of categorical attributes, the workflow helps save on average 15% on cost and 25% on carbon impact. Cutting-edge technologies are applied including design sampling, predictive modeling, data analysis and visualization.
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Jingyi Liu (2025) studied this question.
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