@inproceedings{SchmailzlSaffertKaramaraetal., author = {Schmailzl, Marc and Saffert, Anne-Sophie and Karamara, Merve and Linner, Thomas and Eder, Friedrich and Hoeng, Simon Konrad and Obergrießer, Mathias}, title = {Enhancing Decision-Making for Human-Centered Construction Robotics: A Methodological Framework}, series = {Proceedings of the 41st International Symposium on Automation and Robotics in Construction (ISARC 2024), 2024, Lille, France}, booktitle = {Proceedings of the 41st International Symposium on Automation and Robotics in Construction (ISARC 2024), 2024, Lille, France}, publisher = {IAARC}, isbn = {978-0-6458322-1-1}, doi = {10.22260/ISARC2024/0083}, pages = {637 -- 644}, abstract = {While the Architecture, Engineering, and Construction (AEC) industry is increasingly aware of the rising demands for productivity and human-centered construction improvements, the holistic adoption of robotics as a fundamental strategy to address these challenges has not yet reached comprehensive fruition. This paper therefore introduces a methodological framework aiming to address the industry's pressing need for a systematic approach for assessing the feasibility of integrating robotics into human-centered construction processes. It aims to enhance decision-making regarding the degree of automation in human-centered construction processes, ranging from partial to full robotization or non-robotization. The framework is characterized by a more holistic end-to-end data-/workflow and therefore adopts a multifaceted approach, leveraging BIM-based planning methodologies and integrating new technologies [e.g., Motion Capturing (MoCap), work process simulation software incorporating Digital Human Models (DHM), self-developed conversion/interfacing software and more] that have not been widely used in the industry to date. Subsequently, the framework is evaluated in a real-life bricklaying construction process to ensure a more application-based approach. Overall, the framework advances current construction processes with a more inclusive and conscious technology infill to empower construction professionals with the workflow and corresponding tools necessary for the practical integration of robotics into human-centered construction processes.}, language = {en} } @incollection{BiersackSchmailzlLinneretal., author = {Biersack, Stefan and Schmailzl, Marc and Linner, Thomas and Eder, Friedrich and Obergrießer, Mathias}, title = {Von der Handarbeit zur Hochtechnologie im Reallabor: Die M{\"o}glichkeit der BIM-basierten Planung und effizienten Produktion von Ziegelw{\"a}nden durch Roboter}, series = {Mauerwerk-Kalender 2025}, volume = {2025}, booktitle = {Mauerwerk-Kalender 2025}, editor = {Schermer, Detleff and Brehm, Eric}, edition = {1. Aufl}, publisher = {Ernst \& Sohn}, isbn = {978-3-433-03445-3}, doi = {10.1002/9783433612019.ch7}, subject = {Mauerwerk}, language = {de} } @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} }