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Abstract:	Statistische Auswertungen von Brückeneinsturzdaten zeigen, dass Massivbrücken signifikant seltener einstürzen als Brücken aus Stahl oder Holz. Da weltweit die Hauptursache für Brückeneinstürze Hochwasser und damit verbundene fluviale Prozesse wie Auskolkung, Muren etc. und Anpralle sind, liegt die Vermutung nahe, dass die hohe Eigenlast der Massivbrücken zu einem insgesamt robusteren Verhalten bei diesen Ereignissen führt. Im Rahmen dieses Beitrags soll geprüft werden, ob die IABSE-Einsturzdatenbank diese These bestätigt und Hinweise auf weitere Ursachen identifiziert werden können. Dazu wird die IABSE-Einsturzdatenbank mittels Verfahren der Künstlichen Intelligenz und des maschinellen Lernens (KI/ML) untersucht. Die KI/ML-Analyse bestätigt die bisherige These allerdings nicht. Im Weiteren werden Ursachen für die Ablehnung der These, wie z. B. die Repräsentativität der Daten, diskutiert. Es wird eine Erweiterung der Datenbank für Ereignisse mit großen Einsturzzahlen empfohlen.KI/ML-based Analysis and Interpretation of the IABSE-Bridge Collapse DatabaseStatistical 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.
Source:	Beton- und Stahlbetonbau 118 (2023), No. 2
Page/s:	76-87
Language of Publication:	German

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