@inproceedings{e40f58fc2c9147abb68a252883cd0987,
title = "Bayesian Inference for Modelling Uncertainty in Non-Standard Building Systems",
abstract = "This paper introduces a Bayesian inference approach tailored for modelling uncertainty in non-standard building systems. The proposed framework is exemplified through a case study on coreless filament winding, offering insights into the interplay between probabilistic modelling and structural design. By integrating heterogeneous data sources encompassing fabrication parameters, geometry, material properties, and structural response metrics, the proposed methodology offers a comprehensive solution for quantifying uncertainty in novel construction processes. Through probabilistic graphical models and Bayesian inference techniques, this research contributes to advancing the understanding and management of uncertainty in the co-design of non-standard building systems, facilitating informed decision-making for architects and engineers.",
author = "Fabian Kannenberg and {Gil P{\'e}rez}, Marta and Tim Schneider and Steffen Staab and Jan Knippers and Achim Menges",
year = "2024",
month = aug,
day = "30",
doi = "10.1007/978-3-031-68275-9_6",
language = "English",
isbn = "978-3-031-68274-2",
series = "DMS: Design Modelling Symposium Berlin ",
publisher = "Springer",
pages = "69--80",
editor = "Philipp Eversmann and Christoph Gengnagel and Julian Lienhard and {Ramsgaard Thomsen}, Mette and Jan Wurm",
booktitle = "Scalable Disruptors",
note = "Design Modelling Symposium Kassel 2024 ; Conference date: 16-09-2024 Through 18-09-2024",
}