Proske, Dirk; Güner, Ismail; Hingorani, Ramon; Tanner, Peter; Syrkov, Anton (2023). KI/ML-gestützte Auswertung und Interpretation der IABSE-Brückeneinsturzdatenbank Beton- und Stahlbetonbau, 118(2), pp. 76-87. Wilhelm Ernst und Sohn 10.1002/best.202200098
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Beton und Stahlbetonbau - 2023 - Proske - KI ML‐gest tzte Auswertung und Interpretation der IABSE‐Br ckeneinsturzdatenbank.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (1MB) | Request a copy |
Statistical analyses of bridge collapse data show that concrete bridges collapse significantly less frequently than bridges made of steel or wood. Since the main causes of bridge collapses worldwide are floods and associated fluvial processes, such as scouring, debris flows, etc. and impacts, it is reasonable to assume that the high dead load of concrete bridges leads to an overall more robust behavior in these events. This paper will examine whether the IABSE collapse database confirms this hypothesis and whether indications of further causes can be identified. For this purpose, the IABSE collapse database is examined using artificial intelligence and machine learning (AI/ML) methods. However, the AI/ML analysis does not confirm the previous thesis. The reasons for the rejection of the thesis, such as the representativeness of the data, are also discussed. An extension of the database for events with large numbers of collapses is recommended.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
School of Architecture, Wood and Civil Engineering School of Architecture, Wood and Civil Engineering > AHB Teaching |
Name: |
Proske, Dirk; Güner, Ismail; Hingorani, Ramon; Tanner, Peter and Syrkov, Anton |
Subjects: |
T Technology > TA Engineering (General). Civil engineering (General) T Technology > TG Bridge engineering |
ISSN: |
0005-9900 |
Publisher: |
Wilhelm Ernst und Sohn |
Language: |
German |
Submitter: |
Dirk Proske |
Date Deposited: |
25 Jan 2023 11:49 |
Last Modified: |
04 Feb 2023 01:30 |
Publisher DOI: |
10.1002/best.202200098 |
Uncontrolled Keywords: |
rücken; Einstürze; Hochwasser; Künstliche Intelligenz; maschinelles Lernen |
ARBOR DOI: |
10.24451/arbor.18778 |
URI: |
https://arbor.bfh.ch/id/eprint/18778 |
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