@inproceedings{ThelenEderMelzeretal., author = {Thelen, Simon and Eder, Friedrich and Melzer, Matthias and Nunes, Danilo Weber and Stadler, Michael and Rechenauer, Christian and Obergrießer, Mathias and Jubeh, Ruben and Volbert, Klaus and D{\"u}nnweber, Jan}, title = {A Slim Digital Twin For A Smart City And Its Residents}, series = {SOICT '23: Proceedings of the 12th International Symposium on Information and Communication Technology, 2023, Hi Chi Minh, Vietnam}, booktitle = {SOICT '23: Proceedings of the 12th International Symposium on Information and Communication Technology, 2023, Hi Chi Minh, Vietnam}, publisher = {ACM}, isbn = {979-8-4007-0891-6}, doi = {10.1145/3628797.3628936}, pages = {8 -- 15}, abstract = {In the engineering domain, representing real-world objects using a body of data, called a digital twin, which is frequently updated by "live" measurements, has shown various advantages over tradi- tional modelling and simulation techniques. Consequently, urban planners have a strong interest in digital twin technology, since it provides them with a laboratory for experimenting with data before making far-reaching decisions. Realizing these decisions involves the work of professionals in the architecture, engineering and construction (AEC) domain who nowadays collaborate via the methodology of building information modeling (BIM). At the same time, the citizen plays an integral role both in the data acquisition phase, while also being a beneficiary of the improved resource management strategies. In this paper, we present a prototype for a "digital energy twin" platform we designed in cooperation with the city of Regensburg. We show how our extensible platform de- sign can satisfy the various requirements of multiple user groups through a series of data processing solutions and visualizations, in- dicating valuable design and implementation guidelines for future projects. In particular, we focus on two example use cases concern- ing building electricity monitoring and BIM. By implementing a flexible data processing architecture we can involve citizens in the data acquisition process, meeting the demands of modern users regarding maximum transparency in the handling of their data.}, language = {en} } @inproceedings{HoengEderSchmailzletal., author = {Hoeng, Simon Konrad and Eder, Friedrich and Schmailzl, Marc and Obergrießer, Mathias}, title = {Exploring the Potential of BIM Models for Deriving Synthetic Training Data for Machine Learning Applications, Montreal}, series = {Advances in Information Technology in Civil and Building Engineering, Proceedings of ICCCBE 2024, Volume 2, Simulation and Automation}, booktitle = {Advances in Information Technology in Civil and Building Engineering, Proceedings of ICCCBE 2024, Volume 2, Simulation and Automation}, publisher = {Springer Nature}, address = {Cham}, isbn = {9783031873638}, issn = {2366-2557}, doi = {10.1007/978-3-031-87364-5_5}, pages = {54 -- 63}, abstract = {To increase the efficiency and quality of design and construction tasks, the use of Artificial Intelligence (AI) and Machine Learning (ML) offers a way to automate both repetitive and complex tasks. Many of these ML models rely heavily on large amounts of suitable, machine-readable, and labeled training data. Therefore, a variety of conceivable use cases for ML in the Architecture, Engineering and Construction (AEC) industry are difficult to implement due to a lack of freely and directly usable training data. The process of manually structuring and labeling existing data is time-consuming and needs in some cases skilled personnel to ensure the quality of the labeled data. Due to these factors, approaches for utilizing artificially generated data, referred to as synthetic data, are becoming more prevalent. Since Building Information Models contain a large amount of information, deriving training data from these models presents an obvious route for generation of this data. There are many ML applications whose implementation is inhibited due to a lack of training data, for which model-based synthetic data offer a possible solution approach. The Industry Foundation Classes (IFC) standard provides a powerful exchange format for models independently of their authoring software. Parametric and generative approaches to model creation enable the generation of numerous different building models within a short period of time and with low effort. This paper presents a workflow for automated derivation of synthetic training data from rule-based or parametrically generated models combined with existing IFC datasets as a multimodal data repository. The method is validated by testing automated synthetically labeled image data for a plan detection task, which is carried out with the Object Detection Framework YOLOv8. The suggested workflow has the potential to enhance data accessibility, thereby contributing to the implementation of ML applications in the AEC industry.}, language = {en} } @inproceedings{EderHoengSchmailzletal., author = {Eder, Friedrich and Hoeng, Simon Konrad and Schmailzl, Marc and Linner, Thomas and Obergrießer, Mathias}, title = {Towards improving data interoperability for the reconstruction of existing buildings}, series = {The 20th conference of the International Society for Computing in Civil and Building Engineering (ICCCBE 2024), August 25 to 28, 2024, Montreal}, booktitle = {The 20th conference of the International Society for Computing in Civil and Building Engineering (ICCCBE 2024), August 25 to 28, 2024, Montreal}, abstract = {Digital representations of buildings are the supporting structures of various use-cases in the emerging field of data-driven decision making. From large scale applications in the context of city planning to the detailed evaluation of critical infrastructure they enable specialists to observe problems, interpret relationships, test solutions virtually and apply them in the real world. This is only feasible if the individual underlying digital model meets the requirements imposed by the analysis at hand. In practice, especially models of existing buildings are not easy to come by as the information describing the existing structure is often scattered across multiple different data sources in various formats. Previous research efforts have outlined methodologies which leverage machine learning, computer vision and subsequent semantic enrichment in order to achieve the (re)construction of such building models. However, these methods are generally not integrated with each other, nor do they consider being able to interface with a shared repository of building related data. In this paper we present a methodology which focuses on establishing a common context for all building related data by utilizing the Industry Foundation Classes (IFC) schema. In particular we focus on utilizing readily available geometric and semantic data originating from geographic information systems as a basis, subsequently referencing additional data sources in their corresponding context and finally outlining interfaces with downstream enrichment processes in both directions. Through incorporating contextualized (IFC) data into the early stages of the remodeling workflow, we outline an end-to-end process from the initial component-based data-acquisition to the as-built building information model. In establishing a standardized foundation for data exchange and collaboration it enables all stakeholders to work more seamlessly across different stages of the remodeling project.}, language = {en} } @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), Lille, France}, booktitle = {Proceedings of the 41st International Symposium on Automation and Robotics in Construction (ISARC), Lille, France}, publisher = {International Association for Automation and Robotics in Construction (IAARC)}, isbn = {978-0-6458322-1-1}, issn = {2413-5844}, 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} } @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} } @inproceedings{SaffertWiedererHoengetal., author = {Saffert, Anne-Sophie and Wiederer, Jonas and H{\"o}ng, Simon and Linner, Thomas and Obergrießer, Mathias and Neumann, Patrick}, title = {Towards AI-based optimization of human-centered and robot-assisted construction processes}, series = {Proceedings of the 42nd International Symposium on Automation and Robotics in Construction (ISARC), Montreal, Canada July 28-31, 2025}, booktitle = {Proceedings of the 42nd International Symposium on Automation and Robotics in Construction (ISARC), Montreal, Canada July 28-31, 2025}, publisher = {IAARC}, isbn = {978-0-6458322-2-8}, issn = {2413-5844}, doi = {10.22260/ISARC2025/0112}, pages = {861 -- 868}, abstract = {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.}, language = {en} }