@inproceedings{HoengWiedererEderetal., author = {H{\"o}ng, Simon K. and Wiederer, Jonas and Eder, Friedrich and Obergriesser, Mathias and Linner, Thomas}, title = {Towards AI-enhanced facade planning : integrating human expertise with machine learning-driven parametric modeling}, series = {EC³ \& CIB W78 : 2025 European Conference on Computing in Construction \& 42nd CIB W78 IT in Construction Conference}, volume = {6}, booktitle = {EC³ \& CIB W78 : 2025 European Conference on Computing in Construction \& 42nd CIB W78 IT in Construction Conference}, publisher = {European Council for Computing in Construction}, isbn = {978-9-083451-31-2}, issn = {2684-1150}, doi = {10.35490/EC3.2025.320}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-84862}, pages = {192 -- 199}, abstract = {Planning modern facade systems is complex, requiring optimization across multiple domains.This paper proposes an AI-enhanced workflow for facade planning, harnessing computer vision and human input via a Large Language Model.A generative AI system then guides a parametric model to produce 3D facade designs. Automated checks provide feedback to a Reinforcement Learning system, to iteratively determine optimal solutions.These solutions are verified and finalized by human expertise, ensuring improved outcomes with reduce planning time and effort.The approach illustrates how combining advanced AI methods with human expertise can address the multifactorial challenges of facade design within current industry practices.}, language = {en} }