TY - CHAP A1 - Höng, Simon K. A1 - Wiederer, Jonas A1 - Eder, Friedrich A1 - Obergriesser, Mathias A1 - Linner, Thomas T1 - Towards AI-enhanced facade planning : integrating human expertise with machine learning-driven parametric modeling T2 - EC³ & CIB W78 : 2025 European Conference on Computing in Construction & 42nd CIB W78 IT in Construction Conference N2 - 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. Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-84862 SN - 978-9-083451-31-2 SN - 2684-1150 VL - 6 SP - 192 EP - 199 PB - European Council for Computing in Construction ER - TY - CHAP A1 - Saffert, Anne-Sophie A1 - Wiederer, Jonas A1 - Höng, Simon A1 - Linner, Thomas A1 - Obergrießer, Mathias A1 - Neumann, Patrick T1 - Towards AI-based optimization of human-centered and robot-assisted construction processes T2 - Proceedings of the 42nd International Symposium on Automation and Robotics in Construction (ISARC), Montreal, Canada July 28-31, 2025 N2 - This research explores an innovative AI-driven approach to optimizing construction processes with a focus on humancentered design, addressing key challenges in the construction industry, such as skilled labor shortages and ergonomic risks associated withwork-related musculoskeletal disorders. By integrating process design with AI-based algorithms into simulation tools, various construction process layout variants including robot-assisted scenarios can be simulated and evaluated based on user-specific key performance indicators (e.g., ergonomic score, layouting parameters) to identify optimized solutions. A data processing algorithm automates the process, eliminating the need for manual simulation variations and resulting in increased operational productivity. The AI-based system evaluates and optimizes process layouts by adjusting control parameters. A case study on a brick laying process serves as an exemplary use case, highlighting the necessity and impact of adopting process optimization. The findings emphasize the transformative potential of automated process optimization within simulation environments to rethink existing construction practices, enhance worker well-being, and boost operational productivity. KW - AI-based Learning KW - Simulation KW - Process optimization KW - Human Factors KW - Parametric Design Automation Y1 - 2025 SN - 978-0-6458322-2-8 U6 - https://doi.org/10.22260/ISARC2025/0112 SN - 2413-5844 N1 - Präsentationsvideo: https://www.youtube.com/watch?v=QL-1mIuy-_M SP - 861 EP - 868 PB - IAARC ER -