Structural Health Monitoring of a steel truss railway bridge studying its low frequency response
- 25 September 2023
- Vol. 6 (5) , 876-885
Abstract
In the framework of direct Structural Health Monitoring approaches, this paper presents a low frequency range analysis performed on a steel truss bridge designed in 1946 and built up in Northern Italy. The bridge is instrumented with a permanent structural health monitoring system, that guarantees a continuous flux of data and information from a heterogeneous set of sensors. In this analysis, static and quasistatic bridge responses are considered. These experimental data are merged with the analogous outputs obtained from numerical simulations, performed on FE models representative of the actual bridge, with the purpose of identifying damage‐sensitive indexes. Simulations are performed considering the healthy structure and modelling different damage scenarios. The static analysis consists in the construction of a regressive model able to estimate sensor output responses starting from environmental measurements, with the primary purpose to reduce the effect of temperature over measured static response. This procedure allows to relate the damages simulated by means of a numerical model with the real data, so that the monitoring rules can effectively defined.Keywords
This publication has 15 references indexed in Scilit:
- Progressive numerical model validation of a bowstring-arch railway bridge based on a structural health monitoring systemJournal of Civil Structural Health Monitoring, 2021
- An efficient instance selection algorithm for k nearest neighbor regressionNeurocomputing, 2017
- Sustainable Restoration Criteria for a Historical Steel Railway BridgeProcedia Environmental Sciences, 2017
- On vibration-based damage detection by multivariate statistical techniques: Application to a long-span arch bridgeStructural Health Monitoring, 2016
- A Bayesian approach based on a Markov-chain Monte Carlo method for damage detection under unknown sources of variabilityEngineering Structures, 2014
- A review of unsupervised feature learning and deep learning for time-series modelingPattern Recognition Letters, 2014
- A Study on Multiple Linear Regression AnalysisProcedia - Social and Behavioral Sciences, 2013
- Vibration based structural health monitoring of an arch bridge: From automated OMA to damage detectionMechanical Systems and Signal Processing, 2012
- Standard errors for bagged and random forest estimatorsComputational Statistics & Data Analysis, 2009
- An Overview of Intelligent Fault Detection in Systems and StructuresStructural Health Monitoring, 2004