@misc{Eder, type = {Master Thesis}, author = {Eder, Friedrich}, title = {Entwicklung eines Software-Tools zur automatisierten Ableitung eines Struktur- und Volumenmodells in eine SQLite Datenbank mit Hilfe der Revit API}, address = {Regensburg}, doi = {10.35096/othr/pub-566}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-5660}, school = {Ostbayerische Technische Hochschule Regensburg}, pages = {50}, abstract = {Die Verf{\"u}gbarkeit von Modelldaten die reich an geometrischen als auch semantischen Informationen sind, ist eine der Grundvoraussetzungen das enorme Potenzial der Digitalisierung gewinnbringend im Planen, Bauen und Betreiben von Bauwerken auszusch{\"o}pfen. Mittlerweile liegen auch im Bauwesen vom Laserscanning bis hin zu Protokollen von Maschinendaten vielschichtige und objektspezifische Daten vor, die sich hinsichtlich des Building Information Modelling effizient einsetzen lassen. Im Rahmen der hier vorgestellten Arbeit wird ein Ansatz beschrieben, wie {\"u}ber ein entsprechendes Application Programming Interface aus einem objektorientierten BIM-Modell eine relationale Datenbank f{\"u}r Volumen- und Strukturmodelle abgeleitet werden kann. Ziel ist es, {\"u}ber die relationale Datenbank geometrisch-topologische Daten f{\"u}r unterschiedliche Autoren-Software bereitzustellen. Die Arbeit soll die Vorz{\"u}ge einer redundanzfreien Vorhaltung geometrisch-topologischer und semantischer Bauteilinformationen in einer relationalen Datenbank zeigen.}, language = {de} } @article{HoengEderKrausetal., author = {H{\"o}ng, Simon and Eder, Friedrich and Kraus, Michael A. and Obergrießer, Mathias}, title = {Entwicklung einer synthetischen Datenpipeline zum dom{\"a}nen-spezifischen Lernens eines neuronalen Netzes im Bauwesen [in progress]}, series = {Bauingenieur}, volume = {98}, journal = {Bauingenieur}, publisher = {VDI-Fachmedien}, address = {D{\"u}sseldorf}, issn = {1436-4867}, language = {de} } @inproceedings{EderEuringerObergriesser, author = {Eder, Friedrich and Euringer, Thomas and Obergrießer, Mathias}, title = {Ansatz zur automatisierten Ableitung eines objektorientierten BIM-Modells in eine relationelle Datenbank}, series = {31. Forum Bauinformatik: 11. bis 13. September 2019 in Berlin, proceedings}, booktitle = {31. Forum Bauinformatik: 11. bis 13. September 2019 in Berlin, proceedings}, editor = {Sternal, Maximilian and Ungureanu, Lucian-Constantin and B{\"o}ger, Laura and Bindal-Gutsche, Christoph}, publisher = {Universit{\"a}tsverlag der TU Berlin}, address = {Berlin}, isbn = {978-3-7983-3105-1}, doi = {10.14279/depositonce-8763}, pages = {133 -- 139}, language = {de} } @inproceedings{SchmailzlSpitzhirnEderetal., author = {Schmailzl, Marc and Spitzhirn, M. and Eder, Friedrich and Kr{\"u}ll, Georg and Obergrießer, Mathias and Linner, Thomas and Albalkhy, Wassim and Lafhaj, Zoubeir}, title = {Towards interfacing human centered design processes with the AEC industry by leveraging BIM-based planning methodologies}, series = {40th International Symposium on Automation and Robotics in Construction (ISARC 2023): Chennai, India, July 3-9, 2023}, booktitle = {40th International Symposium on Automation and Robotics in Construction (ISARC 2023): Chennai, India, July 3-9, 2023}, publisher = {I.A.A.R.C.}, isbn = {978-0-6458322-0-4}, issn = {2413-5844}, doi = {10.22260/ISARC2023/0045}, pages = {325 -- 332}, abstract = {Digital workflows in the Architecture, Engineering and Construction (AEC) industry have been working with a wide range of software solutions trying to enable a Design-to-Production (DtP) end-to-end data flow. Thereby, state-of-the-art software solutions attempt to streamline the design and production processes accordingly. However, most digital workflows lack in terms of adequate sequential data preparation, agglomeration, and interfacing capabilities for consecutive design phases. These issues result in long, tedious correction loops, a wide range of software solutions and extensions to mitigate the issues. In addition, many digital workflows do not consider or integrate construction, production and machine relevant data holistically (respectively geometry and semantics). In this context, the production relevant data in from of human-centered work process data referring to digital human models (DHM), derived human abilities, safety and ergonomic criteria are often neglected. However, this is essential to interface the construction, human and machine relevant data in a holistic manner. This paper therefore proposes a DtP-workflow which is intended to solve some of the issues by interfacing relevant software solutions incorporating construction, production (including DHM and more) and machine relevant data in a holistic manner using a Building Information Modeling (BIM)-approach (based on the IFC schema). In this regard, the DtP-workflow aims to reverse common top-down digital workflows by considering and integrating the relevant data for consecutive design phases from the beginning. Subsequently, the DtP-workflow should achieve a reduction in planning effort.}, language = {en} } @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{WiedererHoengEder, author = {Wiederer, Jonas and H{\"o}ng, Simon and Eder, Friedrich}, title = {Konzept zur KI-gest{\"u}tzten parametrischen Br{\"u}ckenmodellierung f{\"u}r ressourcen- und kostenoptimierte Bauwerksentw{\"u}rfe}, series = {Tagungsband 35. Forum Bauinformatik, 2024, Hamburg}, booktitle = {Tagungsband 35. Forum Bauinformatik, 2024, Hamburg}, editor = {St{\"u}hrenberg, Jan and Al-Zuriqat, Thamer and Chillon Geck, Carlos}, publisher = {Technische Universit{\"a}t Hamburg, Institut f{\"u}r Digitales und Autonomes Bauen}, address = {Hamburg}, doi = {10.15480/882.13506}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-77922}, pages = {50 -- 57}, abstract = {Br{\"u}ckenbauwerke stellen eine wesentliche Komponente einer soliden Infrastruktur dar. Wegen vernachl{\"a}ssigter Instandsetzungsmaßnahmen und sich ver{\"a}ndernder Anforderungen sind f{\"u}r eine Vielzahl dieser Bauwerke in Deutschland Ersatzneubauten notwendig. Diese Arbeit adressiert die Herausforderungen der Ressourcenverschwendung durch wiederholten Arbeitsaufwand bei individuellen Br{\"u}ckenentw{\"u}rfen f{\"u}r die angesprochenen Bauwerke. Es wird ein parametrisches Entwurfsmodells in Siemens NX entwickelt, um den Entwurfsprozess zu beschleunigen und eine einfache Anpassung an die entsprechenden Rahmenbedingungen zu erm{\"o}glichen. Zudem wird eine effektive Methode zur Anbindung an die Kostenermittlungssoftware RIB iTWO vorgestellt, die eine nachgelagerte Kostenkalkulation nach DIN 276 erm{\"o}glicht. Aufbauend darauf wird ein Optimierungsworkflow vorgestellt, welcher verschiedene Faktoren einbindet, um einen hochwertigen Bauwerksentwurf zu erzielen. Hierbei wird die Einbindung von Techniken aus dem Bereich der K{\"u}nstlichen Intelligenz angestrebt. Die vorgestellte Methodik soll die Wirtschaftlichkeitsbewertung und Entscheidungsfindung f{\"u}r die Realisierung der Br{\"u}ckenentw{\"u}rfe erleichtern und somit zu einer effizienteren Nutzung von Ressourcen im Br{\"u}ckenbau beitragen.}, language = {de} } @inproceedings{HoengEder, author = {H{\"o}ng, Simon and Eder, Friedrich}, title = {Methode zur Generierung multimodalersynthetischer Daten aus parametrischen BIM-Modellen zur Nutzung in KI-Systemen}, series = {Tagungsband 34. Forum Bauinformatik, 06. - 08.09.2023, Bochum}, booktitle = {Tagungsband 34. Forum Bauinformatik, 06. - 08.09.2023, Bochum}, doi = {10.13154/294-10130}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:294-101306}, pages = {398 -- 405}, abstract = {F{\"u}r die erfolgreiche Implementierung von KI-Systemen ist eine große Menge an relevanten (und gelabelten) Trainingsdaten n{\"o}tig. Die Datenqualit{\"a}t, Format, Struktur und nicht zuletzt urheberrechtliche Belange schr{\"a}nken die unmittelbare Nutzung vorhandener Daten ein und hindern die gewinnbringende Nutzung dieser Technologien in der Praxis. In diesem Beitrag wird ein Ansatz vorgestellt, der auf Basis einer parametrischen bzw. regelbasierten Erstellung von BIM-Modellen synthetische, gelabelte Trainingsdaten f{\"u}r die nachgelagerte Nutzung in diversen KI-Workflows generiert. Die Modelle werden mithilfe einer C\#-Anwendung {\"u}ber die API von Autodesk Revit erzeugt und k{\"o}nnen durch automatisiertes Postprocessing mittels Python zu annotierten Daten in g{\"a}ngigen Austauschformaten aufbereitet werden. Die synthetischen Bauwerksmodelle erm{\"o}glichen die Ableitung multimodaler Trainings-Datens{\"a}tze. Das heißt, sie k{\"o}nnen in unterschiedlichen Dateiformaten vorgehalten werden. Dadurch lassen sich Datens{\"a}tze erzeugen, die auf den jeweiligen Anwendungsfall zugeschnitten sind (z. B.: Rastergrafiken, Punktwolken, CAD- oder BIM-Formate). Die Daten lassen sich somit in entsprechende KI-Systeme integrieren. Dieser L{\"o}sungsansatz wird am Anwendungsbeispiel einer KI-gest{\"u}tzten Objektklassifizierung in 2D-Pl{\"a}nen auf Basis synthetischer Datens{\"a}tze im COCO-Format durchgef{\"u}hrt. Die k{\"u}nstlich erzeugten Plandaten in Anlehnung an Positionspl{\"a}ne von rasterf{\"o}rmig aufgebauten Skelettbauwerken konnten erfolgreich einem KI-System als Trainingsdaten zugef{\"u}hrt werden. Damit wird die nahtlose Verkettung der einzelnen Prozessschritte (Modellierung, Annotation, Integration) validiert. Die Ergebnisse werden abschließend evaluiert und Optimierungsans{\"a}tze diskutiert.}, language = {de} } @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} }