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Long-term static and dynamic monitoring to failure scenarios assessment in steel truss railway bridges: A case study.
- Source :
-
Engineering Failure Analysis . Oct2023, Vol. 152, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- • A case study for failure scenarios assessment in a steel truss railway bridge is described. • Monitoring results are given in terms of deflections and modal frequencies. • Finite Element Analysis (FEA) was validated to simulate failure scenarios. • The most severe failure scenarios were identified. • Long-term monitoring recommendations for failure detection is proposed. The latest studies on failures in steel truss-type bridges found that they are highly vulnerable to damage and thus prone to potential local or total collapse. Many authors recommend monitoring the critical elements in the existing steel truss-type bridges in real time to anticipate any failures in local members. Although mechanical strain is the most frequently used variable for this purpose, this method also happens to be the most expensive monitoring strategy. This paper describes a case study of failure scenarios assessment in a steel truss-type railway bridge after extensive long-term monitoring conducted by the authors, based on measuring vertical deflections and modal frequencies. The structure has both an isostatic and a hyperstatic configuration, and was assessed by means of a combination of: (i) long-term monitoring results, and (ii) a finite element analysis to simulate several failure scenarios. A sensitivity study of the different failure scenarios has been carried out, identifying those that can be detected. The results are used to define practical recommendations for failure detection by measuring vertical deflections and modal frequencies. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13506307
- Volume :
- 152
- Database :
- Academic Search Index
- Journal :
- Engineering Failure Analysis
- Publication Type :
- Academic Journal
- Accession number :
- 171827245
- Full Text :
- https://doi.org/10.1016/j.engfailanal.2023.107435