A sound understanding of a structure's normal condition, including its response to normal environmental and operational variations is desirable for structural health monitoring and necessary for performance monitoring of civil structures. The current paper outlines the extensive monitoring campaign of the Tamar suspension bridge as well as analysis carried out in the attempt to understand the bridge's normal condition. Specifically the effects of temperature, traffic loading and wind speed on the structure's dynamic response are investigated. Finally, initial steps towards development of a structural health monitoring system for the Tamar Bridge are addressed.

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.

This study presents an impedance-based structural health monitoring (SHM) technique considering temperature effects. The temperature variation results in significant impedance variations, particularly a frequency shift in the impedance, which may lead to erroneous diagnostic results of real structures, such as civil, mechanical, and aerospace structures. In order to minimize the effect of the temperature variation on the impedance measurements, a previously proposed temperature compensation technique based on the cross-correlation between the reference-impedance data and a concurrent impedance data is revisited. In this study, cross-correlation coefficient (CC) after an effective frequency shift (EFS), which is defined as the frequency shift causing two impedance data to have the maximum correlation, is utilized. To promote a practical use of the proposed SHM strategy, an automated continuous monitoring framework using MATLAB® is developed and incorporated with the current hardware system. Validation of the proposed technique is carried out on a lab-sized steel truss bridge member under a temperature varying environment. It has been found that the CC values have shown significant fluctuations due to the temperature variation, even after applying the EFS method. Therefore, an outlier analysis providing the optimal decision limits under the inevitable variations has been carried out for more systematic damage detection. It has been found that the threshold level shall be properly selected considering the daily temperature range and the minimum target damage level for detection. It has been demonstrated that the proposed strategy combining the EFS and the outlier analysis can be effectively used in the automated continuous SHM of critical structural members under temperature variations.

Structural temperature is an important form of loading for bridges, particularly for long-span steel structures. In this study, the temperature distribution of the Humber Bridge in United Kingdom is investigated based on numerical simulation and field measurements. A 2D fine finite element (FE) model of a typical section of the box girder of this long-span suspension bridge is constructed. The time-dependent thermal boundary conditions are determined based on the field meteorological measurements with external surface heat convection coefficients varying according to differing local wind speeds they experience. Pre-analysis is adopted to determine the initial thermal condition of the model, then transient heat-transfer analysis is performed and the time-dependent temperature distribution of the bridge is obtained leading to numerical temperature data at different locations in different time that are in good agreement with the measured counterparts. The vertical and transversal temperature differences of the box girder are also investigated. Both measured and numerical results show that the transversal temperature variation across the streamlined girder is significant. The effects of the box girder shape, pavement of the upper webs, and bridge orientation on the transversal temperature difference are finally investigated.

A wide range of vibrating structures are characterized by variable structural dynamics resulting from changes in environmental and operational conditions, posing challenges in their identification and associated condition assessment. To tackle this issue, the present contribution introduces a stochastic modeling methodology via Gaussian Process (GP) time-series models. In the presently introduced approach, the vibration response is represented by means of a random coefficient time-series model, whose coefficients comply with a GP regression on the environmental and operational parameters. The approach may be implemented in conjunction to any type of linear-in-the-parameters time-series model, ranging from simple AR models to more complex non-linear or nonstationary time-series models. The obtained GP time-series modeling approach provides an effective and compact global representation of the vibrational response of a structure under a wide span of environmental and operational conditions. The effectiveness of the postulated GP time-series models is demonstrated through two case studies: the first involves the identification of the vertical vibration response of the Humber bridge, evaluated over a period of three years; the second considers the long-term simulated vibration response of a wind turbine featuring non-stationary dynamics stemming from the rotor speed. In both cases, the variation of the average wind speed is the main driver of uncertainty, while, through application of the proposed GP time-series models, it is possible to track the resulting variation in modal quantities.