Publications and data
The Clifton Suspension Bridge as a demonstrator for smart monitoring of existing infrastructures
Rapid deployment of a WSN on the Clifton Suspension Bridge, UK
The Clifton Suspension Bridge is an emblematic structure in Bristol, UK. In this paper, a rapid deployment of a structural health monitoring (SHM) system for short-term monitoring is described. The system, deployed in a single day, integrates wireless SHM and open-source data management systems to gather valuable information about the bridge use (loading). The deployed system can be used to inform structural response models as well as studies for traffic engineering purposes. The use of open-source software was critical to the successful deployment.
Estimation of vehicle counts from the structural response of bridge
In this paper an offline method for counting vehicles travelling across a bridge through its structural response under loading is developed. Readings from a single vertically oriented accelerometer fixed to the bridge are normalised and then summarised by instantaneous amplitude envelopes. The envelopes for a single vehicle have a profile that is log-normal in appearance. Least squares fitting is used in conjunction with the Akaike information criterion to fit the envelope time series as the superposition of log-normal functions (other functional forms are also considered). It is hypothesised that this fit describes vehicles travelling across the bridge. This method is applied to data from a previous study on rapid deployment of structural health monitoring systems undertaken on the Clifton Suspension Bridge (CSB) in Bristol. The data from a single strain gauge-based accelerometer installed as part of a wireless sensor network (WSN) on the bridge is used and demonstrates the value added by this method to pre-existing asset monitoring systems. A prediction accuracy of 74% is achieved on a labelled test set.
Public dataset: https://data.bris.ac.uk/data/dataset/1mu6d6ybfo3mt2w3aewmtznc3s
Bristol Community Energy Campus
TBC
Moving in shared spaces
Using smartphone accelerometry to assess the relationship between cognitive load and gait dynamics during outdoor walking
Research has demonstrated that an increase in cognitive load can result in increased gait variability and slower overall walking speed, both of which are indicators of gait instability. The external environment also imposes load on our cognitive systems; however, most gait research has been conducted in a laboratory setting and little work has demonstrated how load imposed by natural environments impact gait dynamics during outdoor walking. Across four experiments, young adults were exposed to varying levels of cognitive load while walking through indoor and outdoor environments. Gait dynamics were concurrently recorded using smartphone-based accelerometry. Results suggest that, during indoor walking, increased cognitive load impacted a range of gait parameters such as step time and step time variability. The impact of environmental load on gait, however, was not as pronounced, with increased load associated only with step time changes during outdoor walking. Overall, the present work shows that cognitive load is related to young adult gait during both indoor and outdoor walking, and importantly, smartphones can be used as gait assessment tools in environments where gait dynamics have traditionally been difficult to measure.
Published dataset: https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-019-39718-w/MediaObjects/41598_2019_39718_MOESM1_ESM.csv
Water and the built environment
Water quality monitoring in smart city: A pilot project
A smart city is an urban development vision to integrate multiple information and communication technology (ICT), “Big Data” and Internet of Things (IoT) solutions in a secure fashion to manage a city's assets for sustainability, resilience and liveability. Meanwhile, water quality monitoring has been evolving to the latest wireless sensor network (WSN) based solutions in recent decades. This paper presents a multi-parameter water quality monitoring system of Bristol Floating Harbour which has successfully demonstrated the feasibility of collecting real-time high-frequency water quality data and displayed the real-time data online. The smart city infrastructure – Bristol Is Open was utilised to provide a plug & play platform for the monitoring system. This new system demonstrates how a future smart city can build the environment monitoring system benefited by the wireless network covering the urban area. The system can be further integrated in the urban water management system to achieve improved efficiency.
Full paper: https://www.sciencedirect.com/science/article/pii/S0926580517305988?via%3Dihub
Published data: Please contact corresponding author for data
Hydroinformatics of Smart Cities: Real-time water quality monitoring and prediction
This project focuses on the condition of surface water in urban areas, an important aspect of smart cities. Water quality monitoring has an important role in health and environmental management. This study aims at creating a prediction model for water quality based on real-time data which will help policy and decision makers.
Published dataset: Please contact corresponding author for data
Citizen sensing
Residential Damp Detection with Temperature and Humidity Urban Sensing
Residential damp can be detected by measuring the temperature and relative humidity in a given space. With these values, one can infer the dew point, which is an accurate indicator for condensation. By installing sensor networks that can take these relevant measurements, urban sensing systems could be created that help tackle the problem of residential damp. This paper centres in on this concept. Potential urban sensing solutions relevant to damp are surveyed. Three existing initiatives were found as well as a variety of potential solutions, demonstrating the feasibility of such a network to be installed. A sensor - known as the ‘Frogbox’ – developed from an existing initiative is then deployed in a student residence over a two-week period. From the deployment, it was found that the average humidity was at 56%, which is above the recommended amount. Improvements to the Frogbox could be made by adding a real-time visualisation feature. Given findings from the review and case study, a conceptual urban sensing model for a university student population is then developed. This body of work can be seen as a pre-cursor to further research required in this field.
Published data: https://data.bris.ac.uk/data/dataset/2svuk8fzzqj5n2h5h26huho4sp