KI/ML-gestützte Auswertung und Interpretation der IABSE-Brückeneinsturzdatenbank

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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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