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• W4321495795 abstract "The durability of concrete structures is essential for reliable infrastructure. Although many deterioration models are available, they are rarely applied in situ. For existing structures in need of repair or durability assessment, this is also the case for Building Information Modeling (BIM). However, both BIM and durability modeling hold great potential to both minimize expended resources and maximize the reliability of structures. At the Institute for Building Materials Research (ibac) at RWTH Aachen University, a novel approach to the calibration of deterioration models using Bayesian inference iteratively in a BIM model enriched with machine-readable diagnosis data to achieve a predictive decision support tool is being developed. This paper demonstrates the digital workflow, validates the proposed approach, and expresses the added value for the planning of repair measures." @default.
• W4321495795 created "2023-02-23" @default.
• W4321495795 creator A5014429914 @default.
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• W4321495795 date "2023-02-21" @default.
• W4321495795 modified "2023-10-05" @default.
• W4321495795 title "Predictive BIM with Integrated Bayesian Inference of Deterioration Models as a Four-Dimensional Decision Support Tool" @default.
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• W4321495795 doi "https://doi.org/10.3390/civileng4010012" @default.
• W4321495795 hasPublicationYear "2023" @default.
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