Dieser Dummy Akkordeon wird über einen CSS code versteckt, da elementor standardmäßig den ersten Akkordeon ausklappt (was ich nicht möchte) GaLiGrü Moritz
Lauble, S., Geiger, A., Zielke, P.
Abstract:
To advance the concept of a circular built environment, it is essential to collate and examine material data from existing structures. Material passports—digital documents detailing materials, components, and their properties—are vital in this process. However, generating material passports from building stock documents remains underexplored, presenting challenges such as inefficient information exchange and the labour-intensive digitizing of building stock documents. This paper introduces a novel AI-enabled workflow to address these challenges by automating the extraction, organization, and enhancement of product and material information from building stock documents. Unlike traditional manual methods, the workflow leverages artificial intelligence to improve data extraction, streamline standardization, and integrate publicly available datasets into a centralized digital database. The processed data can be retrieved via the database in standardized data formats (e.g. Excel, CSV, building models) or via an API to facilitate digital data exchange and enable an assessment of the circular economy. The workflow’s practical application is demonstrated in a German building case study, highlighting challenges in data quality and standardization during implementation. By automating and enhancing data handling, the workflow reduces manual effort, increases efficiency, and ensures higher data reliability. This approach enables the streamlined creation of material passports, empowering property managers with actionable insights for renovation and maintenance decisions while contributing to the circularity goals of the built environment.
Part of the Central Europe towards Sustainable Building 2025
Zielke, P., Lauble, S.
Abstract:
The urgent need for building renovation is growing, yet the availability of machine-readable data remains limited, hindering the decision-making process for building owners. This paper addresses this gap by identifying key elements in document digitization, data enrichment, and data analysis that are essential to fostering trust in data processing. To address this, we developed a mock-up based on design thinking principles that aims to consolidate existing building information into a central, accessible location. This platform provides users with a comprehensive overview of building data. We conducted a user study with 44 participants to evaluate the usability of the platform using the System Usability Scale (SUS). The results showed a high SUS score, reflecting strong usability and positive feedback. Participants highlighted the value of centralizing building data, which significantly supports renovation decision-making. The results underscore the platform’s potential to drive digital transformation in the building sectors, marking a critical step forward in renovation planning.
Part of the Central Europe towards Sustainable Building 2025
Lauble, S., Wu, B., Eisen, K., Seibel, H.
Abstract:
Accurate early-stage cost forecasting in renovation projects helps property owners make informed decisions, conserving resources and enhancing efficiency. However, estimates are challenging due to project complexity. Traditional methods, often intuitive and dataset-limited, lack precision. This study analyzes 104 projects using decision trees, finding that regression achieves a lower mean absolute percentage error (MAPE) of 7 %, versus 15 % for classification. A combined approach, integrating external data from Google Earth and Google Solar API, improves accuracy and traceability, emphasizing the potential for reliable, data-driven decisions in renovation projects.
Part of the European Conference on Computing in Construction 2025
Bonekämper, H.
Erschienen auf dem 9. Kolloquium Erhaltung von Bauwerken, 2025
Wu, Bin
Erschienen auf dem 9. Kolloquium Erhaltung von Bauwerken, 2025
Dieser Dummy Akkordeon wird über einen CSS code versteckt, da elementor standardmäßig den ersten Akkordeon ausklappt (was ich nicht möchte) GaLiGrü Moritz
Lauble, S., Bonekämper, H., Zielke, P., & Wu, B
Abstract:
The built environment significantly impacts global sustainability due to its contribution to CO2 emissions, with the building sector being a major source. Energy retrofit measures are essential for enhancing sustainability and achieving resource efficiency. Although many assessment tools are being developed, the availability of existing documents and the relevance of sustainability criteria in practice must also be considered. This paper collects and organizes stakeholder knowledge to identify pertinent documents and evaluation criteria for sustainability assessments in the German building stock. The research draws on insights from 27 expert interviews and 53 survey responses from key AEC industry stakeholders, including building owners, consultancies, developers, planners, and software companies. The findings indicate three primary challenges in sustainability assessment. Firstly, many relevant documents are either unavailable or underutilized. For instance, extracting information from annotated paper floor plans requires labor-intensive manual effort. Secondly, while energy efficiency and managing harmful substances are crucial, other sustainability criteria are often overlooked. Lastly, stakeholders see potential in Artificial Intelligence (AI) to streamline data digitalization and support decision-making processes during preliminary assessments. The study recommends prioritizing the digitalization of underused documents and developing automated assessments incorporating relevant sustainability criteria through AI. Addressing these gaps can enhance the effectiveness and efficiency of sustainability assessments, contributing to more sustainable built environments.
Part of the European Conference on Product and Process Modeling 2024
Zielke, P., Behringer, J., Lauble, S., & Haghsheno, S.
Abstract:
The construction industry faces a persistent challenge of low to stagnant productivity growth, which contrasts with the rapid digitalization observed in other sectors. Comprehensive analysis and productivity enhancement are necessary due to the presence of analog and unstructured data within the construction industry. The potential of Large Language Models (LLMs) such as ChatGPT to facilitate digitization efforts in this context is a subject of inquiry. Moreover, the fragmentation and complexity of the industry often worsen the shortage of programming expertise. This study investigates the role of LLMs in facilitating coding for digitizing information related to existing buildings. A systematic literature review explores the functionality and advancements of three prominent LLM architectures: ChatGPT, PaLM2, and Llama 2. Subsequently, this study establishes a use case for extracting information from environmental product declarations (EPD). It also defines evaluation criteria for the applicability of LLM. The results show that, in the tested use case, only ChatGPT was successful in generating two Python scripts capable of extracting up to 58.47% of the specified dataset information. Although all three LLMs are capable of generating programming codes for document extraction and digitization, their success rate is heavily influenced by document structure and layout. Additionally, adjustments made by IT experts could potentially improve the success rate of extracted information. In conclusion, this study emphasizes the potential of LLMs in supporting digitization efforts within the construction industry. However, the effectiveness of these models depends on document structure and may benefit from expert intervention. These findings contribute to advancing understanding of LLM applications in construction digitization, providing insights for future research and practical implementation.
Part of the European Conference on Product and Process Modeling 2024
Bonekämper, H.
Abstract:
In Deutschland besteht bei Bestandsgebäuden ein erheblicher Bedarf an Sanierungsmaßnahmen, der sich in den kommenden Jahren durch die „Renovation Wave“ voraussichtlich verstärken wird. Aus diesem Grund suchen Immobilienbestandshalter nach effektiven Plänen, um diese Maßnahmen gemäß objektiven Bewertungskriterien umzusetzen. Zu diesem Zweck arbeiten Forscherinnen und Forscher des Karlsruher Instituts für Technologie (KIT) in Kooperation mit der DGNB e.V., CAALA, Concular und Züblin an der Entwicklung einer digitalen Austauschplattform. Diese Plattform verwendet quelloffene Standards und Schnittstellen, um zukünftige Sanierungsmaßnahmen auf nachhaltige und intelligente Weise ableiten zu können.
Erschienen in Grundstücksmarkt und Grundstückswert (GuG), Ausgabe Mai/Juni 2024
Zielke, P., Lauble, S.
Abstract:
Die energetische Sanierung von Bestandsgebäuden stellt eine zentrale Herausforderung auf dem Weg zu einer nachhaltigen Gebäudewirtschaft dar. Der Beitrag beleuchtet, wie künstliche Intelligenz (KI) in diesem Kontext zur Steigerung der Nachhaltigkeit beitragen kann – insbesondere durch die Unterstützung bei der Entscheidungsfindung, der automatisierten Analyse von Gebäudedaten und der Optimierung von Sanierungsstrategien. Anhand praktischer Anwendungsbeispiele wird gezeigt, wie KI-basierte Systeme dabei helfen können, ökologische und ökonomische Zielsetzungen besser zu vereinen. Der Beitrag diskutiert zudem Potenziale und Grenzen des KI-Einsatzes in Sanierungsprojekten und zeigt auf, welche infrastrukturellen und organisatorischen Voraussetzungen für eine erfolgreiche Integration notwendig sind.
Erschienen in Bausubstanz 2/2024
Mortazavi, M. & Wu, B. & Steinbrenner, S. & Haghsheno, S.
Abstract:
With the rapid advancement of digitization, a multitude of tools for as-built modeling has emerged, posing a challenge in choosing the most suitable product. This article presents an evaluation system for identifying and selecting a site scanning method to create a digital twin of existing buildings. The evaluation criteria developed for this purpose were integrated into an evaluation scheme that forms the basis for a quantitative assessment. A case study was conducted to validate the developed evaluation system. Two Operating Systems (OS) applications, a Faro laser scanner, and a handheld LumoScanner were selected to generate a 3D model for a floor with a 500 m2 area. The presented method supports construction and design companies in their decision-making process when selecting scanning methods for practical building applications.
Die Zukunft der intelligenten Sanierung!
© 2022 Created with Royal Elementor Addons
info@nais.tech
Institut für Technologie und Management im Baubetrieb (TMB)
Gotthard-Franz-Str. 3
76131 Karlsruhe
