Structural health monitoring of the Tamar suspension bridge

Abstract

This paper presents experiences and lessons from the structural health monitoring practice on the Tamar Bridge in Plymouth, UK, a 335m span suspension bridge opened in 1961. After 40 years of operations the bridge was strengthened and widened in 2001 to meet a European Union requirement to carry heavy goods vehicles up to 40 tonnes weight, a process in which additional stay cables and cantilever decks were added and the composite deck was replaced with a lightweight orthotropic steel deck. At that time a structural monitoring system comprising wind, temperature, cable tension and deck level sensors was installed to monitor the bridge behaviour during and after the upgrading. In 2006 and 2009 respectively, a dynamic response monitoring system with real time modal parameter identification and a three-dimensional total positioning system were added to provide a more complete picture of the bridge behavior, and in 2006 a one day ambient vibration survey of the bridge was carried out to characterize low frequency vibration modes of the suspended structure. Practical aspects of the instrumentation and data processing & management are discussed and some key response observations are presented. The bridge is a surprisingly complex structure with a number of inter-linked load-response mechanisms evident, all of which have to be characterized as part of a long term structural health monitoring exercise. Structural temperature leading to thermal expansion of the deck, main cables and additional stays is a major factor on global deformation, while vehicle loading and wind are apparently secondary factors. Dynamic response levels and modal parameters show apparently complex relationships among themselves and with the quasi-static load and response. As well as the challenges of fusing and managing data from three distinct but parallel monitoring systems, there is a significant challenge in interpreting the load and response data firstly to diagnose the normal service behavior and secondly to identify performance anomalies.

Empirical Validation of Bayesian Dynamic Linear Models in the Context of Structural Health Monitoring

“…Those authors found that temperature has a dominant effect over wind speed. In a subsequent study, Koo et al (2013) confirmed that the wind speed has a negligible effect on the Bridge frequencies. Cross et al (Cross et al 2013) later created linear-regression-based empirical models using temperature, traffic loading and vertical accelerations.…”

“…Since 2007, natural frequencies of the bridge as well as external effects including temperature and traffic load are monitored. Details of the monitoring system and processing of the data are reported by Koo et al (2013), Cross et al (2013) and Brownjohn et al(2007). The dataset Goulet, J.-A.…”

“…Frequencies were computed from acceleration responses using data-driven stochastic subspace identification (Peeters and De Roeck 1999). Hardware characteristics, sensor locations and data processing were reported by Koo et al (2013). Figure 5 shows the evolution over time of the first natural frequency and blank segments represent missing data.…”

Suspension bridge response due to extreme vehicle loads

“…The performance of the bridge is currently being recorded as part of an ongoing long-term monitoring project, and has been instrumented with a variety of sensors to determine variations in quasi-static and dynamic responses (Koo, Brownjohn, List & Cole, 2012). The general arrangement of the sensors used for the site investigation is shown in Figure 1.…”

Support vector regression for anomaly detection from measurement histories

“…Catbas et al [26] monitored a long-span truss bridge in the USA and observed that the annual peak-to-peak strain differentials for the bridge were ten times higher than the maximum traffic-induced strains. Measurements taken from the Tamar bridge in the UK by Koo et al [27] also showed that thermal variations were the major driver of deformations in the structure. Therefore there has been considerable interest in the SHM community on quantifying the effect of ambient conditions on structural response [28][29][30] and in particular, employ it for damage detection.…”