TY - THES A1 - Eder, Friedrich T1 - Entwicklung eines Software-Tools zur automatisierten Ableitung eines Struktur- und Volumenmodells in eine SQLite Datenbank mit Hilfe der Revit API N2 - Die Verfügbarkeit 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 auszuschöpfen. 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 über ein entsprechendes Application Programming Interface aus einem objektorientierten BIM-Modell eine relationale Datenbank für Volumen- und Strukturmodelle abgeleitet werden kann. Ziel ist es, über die relationale Datenbank geometrisch-topologische Daten für unterschiedliche Autoren-Software bereitzustellen. Die Arbeit soll die Vorzüge einer redundanzfreien Vorhaltung geometrisch-topologischer und semantischer Bauteilinformationen in einer relationalen Datenbank zeigen. KW - SQLite KW - Revit KW - C# Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-5660 CY - Regensburg ER - TY - JOUR A1 - Höng, Simon A1 - Eder, Friedrich A1 - Kraus, Michael A. A1 - Obergrießer, Mathias T1 - Entwicklung einer synthetischen Datenpipeline zum domänen-spezifischen Lernens eines neuronalen Netzes im Bauwesen [in progress] JF - Bauingenieur Y1 - 2023 SN - 1436-4867 VL - 98 PB - VDI-Fachmedien CY - Düsseldorf ER - TY - CHAP A1 - Eder, Friedrich A1 - Euringer, Thomas A1 - Obergrießer, Mathias ED - Sternal, Maximilian ED - Ungureanu, Lucian-Constantin ED - Böger, Laura ED - Bindal-Gutsche, Christoph T1 - Ansatz zur automatisierten Ableitung eines objektorientierten BIM-Modells in eine relationelle Datenbank T2 - 31. Forum Bauinformatik: 11. bis 13. September 2019 in Berlin, proceedings KW - Bauinformatik KW - BIM Y1 - 2019 SN - 978-3-7983-3105-1 U6 - https://doi.org/10.14279/depositonce-8763 SP - 133 EP - 139 PB - Universitätsverlag der TU Berlin CY - Berlin ER - TY - CHAP A1 - Schmailzl, Marc A1 - Spitzhirn, M. A1 - Eder, Friedrich A1 - Krüll, Georg A1 - Obergrießer, Mathias A1 - Linner, Thomas A1 - Albalkhy, Wassim A1 - Lafhaj, Zoubeir T1 - Towards interfacing human centered design processes with the AEC industry by leveraging BIM-based planning methodologies T2 - 40th International Symposium on Automation and Robotics in Construction (ISARC 2023): Chennai, India, July 3–9, 2023 N2 - 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. KW - Architecture Engineering and Construction (AEC) industry KW - Building Information Modeling (BIM) KW - Interoperability KW - Digital Human model (DHM) Y1 - 2023 SN - 978-0-6458322-0-4 U6 - https://doi.org/10.22260/ISARC2023/0045 SN - 2413-5844 SP - 325 EP - 332 PB - I.A.A.R.C. ER - TY - CHAP A1 - Thelen, Simon A1 - Eder, Friedrich A1 - Melzer, Matthias A1 - Nunes, Danilo Weber A1 - Stadler, Michael A1 - Rechenauer, Christian A1 - Obergrießer, Mathias A1 - Jubeh, Ruben A1 - Volbert, Klaus A1 - Dünnweber, Jan T1 - A Slim Digital Twin For A Smart City And Its Residents T2 - SOICT '23: Proceedings of the 12th International Symposium on Information and Communication Technology, 2023, Hi Chi Minh, Vietnam N2 - 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. KW - smart city KW - AI KW - digital twin KW - artificial intelligence KW - urban planning KW - BIM KW - portal system Y1 - 2023 SN - 979-8-4007-0891-6 U6 - https://doi.org/10.1145/3628797.3628936 SP - 8 EP - 15 PB - ACM ER - TY - CHAP A1 - Wiederer, Jonas A1 - Höng, Simon A1 - Eder, Friedrich ED - Stührenberg, Jan ED - Al-Zuriqat, Thamer ED - Chillon Geck, Carlos T1 - Konzept zur KI-gestützten parametrischen Brückenmodellierung für ressourcen- und kostenoptimierte Bauwerksentwürfe T2 - Tagungsband 35. Forum Bauinformatik, 2024, Hamburg N2 - Brückenbauwerke stellen eine wesentliche Komponente einer soliden Infrastruktur dar. Wegen vernachlässigter Instandsetzungsmaßnahmen und sich verändernder Anforderungen sind für eine Vielzahl dieser Bauwerke in Deutschland Ersatzneubauten notwendig. Diese Arbeit adressiert die Herausforderungen der Ressourcenverschwendung durch wiederholten Arbeitsaufwand bei individuellen Brückenentwürfen für 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 ermöglichen. Zudem wird eine effektive Methode zur Anbindung an die Kostenermittlungssoftware RIB iTWO vorgestellt, die eine nachgelagerte Kostenkalkulation nach DIN 276 ermöglicht. 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 Künstlichen Intelligenz angestrebt. Die vorgestellte Methodik soll die Wirtschaftlichkeitsbewertung und Entscheidungsfindung für die Realisierung der Brückenentwürfe erleichtern und somit zu einer effizienteren Nutzung von Ressourcen im Brückenbau beitragen. KW - Building Information Modeling KW - Parametric Modeling KW - Artificial Intelligence KW - IFC KW - Industry Foundation Classes KW - Design Automation KW - BIM Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-77922 SP - 50 EP - 57 PB - Technische Universität Hamburg, Institut für Digitales und Autonomes Bauen CY - Hamburg ER - TY - CHAP A1 - Höng, Simon A1 - Eder, Friedrich T1 - Methode zur Generierung multimodalersynthetischer Daten aus parametrischen BIM-Modellen zur Nutzung in KI-Systemen T2 - Tagungsband 34. Forum Bauinformatik, 06. - 08.09.2023, Bochum N2 - Für die erfolgreiche Implementierung von KI-Systemen ist eine große Menge an relevanten (und gelabelten) Trainingsdaten nötig. Die Datenqualität, Format, Struktur und nicht zuletzt urheberrechtliche Belange schränken 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 für die nachgelagerte Nutzung in diversen KI-Workflows generiert. Die Modelle werden mithilfe einer C#-Anwendung über die API von Autodesk Revit erzeugt und können durch automatisiertes Postprocessing mittels Python zu annotierten Daten in gängigen Austauschformaten aufbereitet werden. Die synthetischen Bauwerksmodelle ermöglichen die Ableitung multimodaler Trainings-Datensätze. Das heißt, sie können in unterschiedlichen Dateiformaten vorgehalten werden. Dadurch lassen sich Datensätze 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 Lösungsansatz wird am Anwendungsbeispiel einer KI-gestützten Objektklassifizierung in 2D-Plänen auf Basis synthetischer Datensätze im COCO-Format durchgeführt. Die künstlich erzeugten Plandaten in Anlehnung an Positionspläne von rasterförmig aufgebauten Skelettbauwerken konnten erfolgreich einem KI-System als Trainingsdaten zugeführt werden. Damit wird die nahtlose Verkettung der einzelnen Prozessschritte (Modellierung, Annotation, Integration) validiert. Die Ergebnisse werden abschließend evaluiert und Optimierungsansätze diskutiert. KW - AI KW - Machine Learning KW - Computer Vision KW - Synthetic Data KW - BIM Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:294-101306 SP - 398 EP - 405 ER - TY - CHAP A1 - Hoeng, Simon Konrad A1 - Eder, Friedrich A1 - Schmailzl, Marc A1 - Obergrießer, Mathias T1 - Exploring the Potential of BIM Models for Deriving Synthetic Training Data for Machine Learning Applications, Montreal T2 - Advances in Information Technology in Civil and Building Engineering, Proceedings of ICCCBE 2024, Volume 2, Simulation and Automation N2 - 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. KW - KI KW - BIM KW - Synthetic Data Y1 - 2025 SN - 9783031873638 U6 - https://doi.org/10.1007/978-3-031-87364-5_5 SN - 2366-2557 SP - 54 EP - 63 PB - Springer Nature CY - Cham ER - TY - CHAP A1 - Eder, Friedrich A1 - Hoeng, Simon Konrad A1 - Schmailzl, Marc A1 - Linner, Thomas A1 - Obergrießer, Mathias T1 - Towards improving data interoperability for the reconstruction of existing buildings T2 - The 20th conference of the International Society for Computing in Civil and Building Engineering (ICCCBE 2024), August 25 to 28, 2024, Montreal N2 - 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. Y1 - 2024 N1 - Conference proceedings erscheinen voraussichtlich bei Springer Nature in der Reihe Lecture Notes in Civil Engineering ER - TY - CHAP A1 - Schmailzl, Marc A1 - Saffert, Anne-Sophie A1 - Karamara, Merve A1 - Linner, Thomas A1 - Eder, Friedrich A1 - Hoeng, Simon Konrad A1 - Obergriesser, Mathias T1 - Enhancing Decision-Making for Human-Centered Construction Robotics: A Methodological Framework T2 - Proceedings of the 41st International Symposium on Automation and Robotics in Construction (ISARC), Lille, France N2 - 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. KW - Decision-Making KW - Framework KW - Workflow KW - AEC Industry KW - Robotics KW - Building Information Modeling (BIM) KW - Human-Centered KW - Motion Capturing Y1 - 2024 SN - 978-0-6458322-1-1 U6 - https://doi.org/10.22260/ISARC2024/0083 SN - 2413-5844 SP - 637 EP - 644 PB - International Association for Automation and Robotics in Construction (IAARC) ER - TY - CHAP A1 - Schmailzl, Marc A1 - Saffert, Anne-Sophie A1 - Karamara, Merve A1 - Linner, Thomas A1 - Eder, Friedrich A1 - Hoeng, Simon Konrad A1 - Obergrießer, Mathias T1 - Enhancing Decision-Making for Human-Centered Construction Robotics: A Methodological Framework T2 - Proceedings of the 41st International Symposium on Automation and Robotics in Construction (ISARC 2024), 2024, Lille, France N2 - 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. Y1 - 2024 SN - 978-0-6458322-1-1 U6 - https://doi.org/10.22260/ISARC2024/0083 SP - 637 EP - 644 PB - IAARC ER - TY - CHAP A1 - Biersack, Stefan A1 - Schmailzl, Marc A1 - Linner, Thomas A1 - Eder, Friedrich A1 - Obergrießer, Mathias ED - Schermer, Detleff ED - Brehm, Eric T1 - Von der Handarbeit zur Hochtechnologie im Reallabor: Die Möglichkeit der BIM-basierten Planung und effizienten Produktion von Ziegelwänden durch Roboter T2 - Mauerwerk-Kalender 2025 KW - Mauerwerk KW - Mauerwerksbau Y1 - 2025 SN - 978-3-433-03445-3 U6 - https://doi.org/10.1002/9783433612019.ch7 VL - 2025 PB - Ernst & Sohn ET - 1. Aufl ER - 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 -