Data Processing and AI for Predictive and Adaptive Co-Design in Prefabrication and On-Site Construction

Research Project 9-2 (RP 9-2)

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DATA PROCESSING AND AI FOR PREDICTIVE AND ADAPTIVE CO-DESIGN IN PREFABRICATION AND ON-SITE CONSTRUCTION

A significant obstacle to the cluster’s co-design approach is integrative data management across all phases of building processes. While this obstacle has been targeted from different perspectives, utilizing data models and formats specific to different domains (e.g., building engineering, mechanical and plant engineering), it has not yet been overcome. The models typically represent the flow alongside the process chain and have limited interoperability. A broader view based on data combined from different phases and back-flow of information are not well supported. This project addresses these shortcomings by creating a central data management (CDM) platform that integrates data from multiple phases of the building process, supports co-design based on artificial intelligence (AI) by jointly analyzing these data, and returning data-derived feedback to various parties involved. Having defined the CDM software architecture and implemented a first co-design application in RP9-1, we now focus on data integration algorithms embedded into predictive and adaptive co-design that apply AI methods to prefabrication and on-site construction.

 

PRINCIPAL INVESTIGATORS

Prof. Dr. rer. nat. Melanie Herschel
Institute for Parallel and Distributed Systems (IPVS), University of Stuttgart
Tenure-Track Prof. Dr. Thomas Wortmann
Chair for Computing in Architecture, Institute for Computational Design and Construction (ICD), University of Stuttgart 
Prof. Dr.-Ing. Cristina Tarín Sauer (ISYS)
Institute for System Dynamics, ISYS, University of Stuttgart

TEAM

Gili Ron (ICD)
Lior Skoury (ICD)
Charlotte Stein (ISYS)

 

 

PEER-REVIEWED PUBLICATIONS

  1. 2023

    1. Gazzarri, L., & Herschel, M. (2023). Progressive Entity Resolution over Incremental Data. In J. Stoyanovich, J. Teubner, N. Mamoulis, E. Pitoura, & J. Mühlig (Eds.), Proceedings 26th International Conference on Extending Database Technology,              EDBT 2023, Ioannina, Greece, March 28-31, 2023 (pp. 80--91). OpenProceedings.org. https://doi.org/10.48786/edbt.2023.07
  2. 2022

    1. Lässig, N., Herschel, M., Reichle, A., Ellwein, C., & Verl, A. (2022). The ArchIBALD Data Integration Platform: Bridging Fragmented Processes in the Building Industry. Intelligent Information Systems - Forum, International Conference on Advanced Information Systems Engineering (CAiSE), 45--54. https://doi.org/10.1007/978-3-031-07481-3\_6
    2. Lässig, N., Oppold, S., & Herschel, M. (2022). Metrics and Algorithms for Locally Fair and Accurate Classifications using Ensembles. Datenbank-Spektrum, 22(1), 23–43. https://doi.org/10.1007/s13222-021-00401-y
    3. Skoury, L., Amtsberg, F., Yang, X., Wagner, H. J., Menges, A., & Wortmann, T. (2022). A Framework for Managing Data in Multi-actor Fabrication Processes. In C. Gengnagel, O. Baverel, G. Betti, M. Popescu, M. R. Thomsen, & J. Wurm (Eds.), Towards Radical Regeneration (pp. 601–615). Springer International Publishing. https://doi.org/10.1007/978-3-031-13249-0_47
    4. Wortmann, T., Herschel, M., Staab, S., & Tarín, C. (2022). AI for AEC: KI für Bauplanung und Bau. Bautechnik, 99(10), Article 10. https://doi.org/10.1002/bate.202200070
  3. 2021

    1. Diestelkamper, R., Lee, S., Glavic, B., & Herschel, M. (2021). Debugging Missing Answers for Spark Queries over Nested Data with Breadcrumb. Proceedings of the VLDB Endowment (PVLDB).
    2. Ellwein, C., Reichle, A., Herschel, M., & Verl, A. (2021). Integrative data processing for cyber-physical off-site and on-site construction promoting co-design. Procedia CIRP, 100, 451–456. https://doi.org/10.1016/j.procir.2021.05.103
    3. Gazzarri, L., & Herschel, M. (2021). End-to-end Task Based Parallelization for Entity Resolution on Dynamic Data. Proceedings of the IEEE International Conference on Data Engineering (ICDE).
    4. Lässig, N., Oppold, S., & Herschel, M. (2021). Using FALCES against bias in automated decisions by integrating fairness in dynamic model ensembles. In Proceedings of Database Systems for Business, Technology, and Web (BTW).
  4. 2019

    1. Diestelkämper, R., & Herschel, M. (2019). Capturing and Querying Structural Provenance in Spark with Pebble. In ACM International Conference on Management of Data (SIGMOD), 1893–1896. https://doi.org/10.1145/3299869.3320225

OTHER PUBLICATIONS

    DATA SETS

        

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