Structural Health Monitoring (SHM) is a relatively new branch of civil engineering that focuses on assessing the health status of infrastructure, such as long-span bridges. Using a broad range of in-situ monitoring instruments, the purpose of the SHM is to help engineers understand the behaviour of structures, ensuring their structural integrity and the safety of the public. Under the Integrated Applications Promotion (IAP) scheme of the European Space Agency (ESA), a feasibility study (FS) project that used the Global Navigation Satellite Systems (GNSS) and Earth Observation (EO) for Structural Health Monitoring of Long-span Bridges (GeoSHM) was initiated in 2013. The GeoSHM FS Project was led by University of Nottingham and the Forth Road Bridge (Scotland, UK), which is a 2.5 km long suspension bridge across the Firth of Forth connecting Edinburgh and the Northern part of Scotland, was selected as the test structure for the GeoSHM FS project. Initial results have shown the significant potential of the GNSS and EO technologies. With these successes, the FS project was further extended to the demonstration stage, which is called the GeoSHM Demo project where two other long-span bridges in China were included as test structures. Led by UbiPOS UK Ltd. (Nottingham, UK), a Nottingham Hi-tech company, this stage focuses on addressing limitations identified during the feasibility study and developing an innovative data strategy to process, store, and interpret monitoring data. This paper will present an overview of the motivation and challenges of the GeoSHM Demo Project, a description of the software and hardware architecture and a discussion of some primary results that were obtained in the last three years.

Considered to be representative of a generic bridge deck geometry and characterised by a highly unsteady flow field, the 5:1 rectangular cylinder has been the main case study in a number of studies including the "Benchmark on the Aerodynamics of a Rectangular 5:1 Cylinder" (BARC). There are still a number of limitations in the knowledge of (i) the mechanism of the vortex-induced vibration (VIV) and (ii) of the turbulence-induced effect for this particular geometry. Extended computational and wind tunnel studies were therefore conducted by the authors to address these issues. This paper primarily describes wind tunnel and computational studies using a sectional model in an attempt to bring more insight into Point (i). By analysing the distribution and correlation of the surface pressure around an elastically mounted 5:1 rectangular cylinders in smooth and turbulent flow, it revealed that the VIV was triggered by the motion-induced leading-edge vortex; a strongly correlated flow feature close to the trailing edge was then responsible for an increase in the structural response.

Implementation of Structural Health Monitoring systems on long-span bridges has become mandatory in many countries to ascertain the safety of these structures and the public, taking into account an increase in usage and threats due to extreme loading conditions. However, the successful delivery of such a system is facing many challenges including the failure to extract damage and reliability information from monitoring data to assist bridge operators with their maintenance planning and activities. Supported by the European Space Agency under the Integrated Applications Promotion scheme, the project ‘GNSS and Earth Observation for Structural Health Monitoring of Long-span Bridges’ or GeoSHM aims to address some of these shortcomings (GNSS stands for Global Navigation Satellite System). In this paper, the background of the GeoSHM project as well as the GeoSHM sensor system on the Forth Road Bridge (FRB) in Scotland will be briefly described. The bridge response and wind data collected over a two-year period from 15 October 2015 to 15 October 2017 will be analysed to demonstrate the high susceptibility of the bridge to wind loads. Close examination of the data associated with an extreme wind event in 2018—Storm Ali—will be conducted to reveal the relationship between the wind speed and some monitored parameters such as the bridge response and modal frequencies.

Vortex induced vibration (VIV) is an important phenomenon which appears in flexible structures immersed in a moving fluid. This oscillation is self-sustained and self-limited, but VIV might cause fatigue damage and affect the structure's serviceability.In the present study, the aerodynamics of the flow fields around a static and vertically free-to-oscillate 4:1 rectangular cylinder are analysed by means of 3D LES simulations, adopting the OneEqEddy viscosity model. Integral parameters, pressure distributions, amplitudes of oscillation, coherences and correlations are obtained and compared with the available experimental data. Aiming to ascertain the impact of the boundary conditions and the grid resolution on the accuracy of results, five cases adopting 3 different meshes including two different spanwise discretisations have been considered. When studying the aerodynamics of the cylinder in static conditions, the influence of the spatial discretisation is very limited, and the agreement with experimental data is fairly good. On the other hand, for the free-to-oscillate cylinder, the structural response is dramatically dependent on the spanwise discretisation. The maximum amplitude of the structural response decreases as the mesh resolution increases, providing a closer fit with the experimental data. Also, the spanwise correlation of pressures is studied, finding remarkable differences depending on the level of spatial discretisation.

AbstractBridges are critical to economic and social development of a country. In order to ensure the safe operation of bridges, it is of great significance to accurately predict displacement of monitoring points from bridge Structural Health System (SHM). In the paper, a CEEMDAN-KELM model is proposed to improve the accuracy of displacement prediction of bridge. Firstly, the original displacement monitoring time series of bridge is accurately and effectively decomposed into multiple components called intrinsic mode functions (IMFs) and one residual component using a method named complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Then, these components are forecasted by establishing appropriate kernel extreme learning machine (KELM) prediction models, respectively. At last, the prediction results of all components including residual component are summed as the final prediction results. A case study using global navigation satellite system (GNSS) monitoring data is used to illustrate the feasibility and validity of the proposed model. Practical results show that prediction accuracy using CEEMDAN-KELM model is superior to BP neural network model, EMD-ELM model and EMD-KELM model in terms of the same monitoring data.