Bayesian Inference for Modelling Uncertainty in Non-Standard Building Systems

Fabian Kannenberg (Corresponding author), Marta Gil Pérez, Tim Schneider, Steffen Staab, Jan Knippers, Achim Menges

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.
Original languageEnglish
Title of host publicationScalable Disruptors
Subtitle of host publicationDesign Modelling Symposium Kassel 2024
EditorsPhilipp Eversmann, Christoph Gengnagel, Julian Lienhard, Mette Ramsgaard Thomsen, Jan Wurm
Place of PublicationCham
PublisherSpringer
Pages69-80
Number of pages12
ISBN (Electronic)978-3-031-68275-9
ISBN (Print)978-3-031-68274-2, 978-3-031-68277-3
DOIs
Publication statusPublished - 30 Aug 2024
EventDesign Modelling Symposium Kassel 2024 - Kassel, Germany
Duration: 16 Sept 202418 Sept 2024

Publication series

NameDMS: Design Modelling Symposium Berlin

Conference

ConferenceDesign Modelling Symposium Kassel 2024
Country/TerritoryGermany
CityKassel
Period16/09/2418/09/24

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