Paul Fidler

Research background

Paul joined the Department of Engineering in 1995, initially to work on the development of a yield-line analysis program for concrete bridges. In recent years, Paul's research has been on using new sensing technologies such as wireless sensors or fibre-optic strain measurements for civil infrastucture assets.

CDBB projects involved with

Staffordshire Bridges - Long Term Performance Monitoring using Fibre-Optic Sensors

Research ambitions for CDBB

To use advanced sensing technology to gather real-time data on two in-service railway bridges in order to create a real-world demonstration of the potential for Digital Twins to provide useful and meaningful information to asset owners to allow them to manage their inventory.

Biography

Paul graduated from Churchill College, Cambridge in 1994 after reading Natural Sciences for two years and then switching to Computer Science for his final year. He joined the Department of Engineering in 1995. Initially, Paul worked primarily on the development of a computer programs for yield-line analysis of concrete slab and beam-and-slab bridges. He subsequently worked on projects involving the use of single-board ARM-based computers for teaching and robotics. In 2007 Paul worked with a team of researchers from both the Department of Engineering and the Computer Laboratory on the use of Wireless Sensor networks for Structural Health Monitoring of civil infrastructure such as bridges and tunnels. Paul became the Computer Associate for the Cambridge Centrefor Smart Infrastructure and Construction (CSIC) in 2011. In this role he has continued to work on monitoring of civil infrastructure assets, using a variety of sensing techniques including wireless sensors, fibre-optic strain sensors, and tram-mounted accelerometers.

Publications

Key publications:

Rodenas-Herráiz, D., Xu, X., Fidler, P.R.A., and Soga, K. (2019). Power-efficient piezoelectric fatigue measurement using long-range wireless sensor networks. Smart Materials and Structures. 28 (2019) 005004 (17pp).doi:10.1088/1361-665X/ab2c46.
Kariyawasam, K.K.G.K.D., Fidler, P.R.A., Talbot, J.P. and Middleton, C.R. (2019). Field Deployment of an Ambient Vibration-Based Scour Monitoring System at Baildon Bridge, UK In: International Conference on Smart Infrastructure and Construction 2019 (ICSIC): Driving data-informed decision-making. 8-10 July 2019. (DeJong, M.J., Schooling, J.M., Viggiana, G.M.B. eds.) ICE Publishing, London. pp. 711-719. ISBN: 978-0-7277-6466-9. doi:10.1680/icsic.64669.711.
Carrigan, T.D., Fidler, P.R.A., Talbot, J.P. (2019). On the Derivation of Rail Roughness Spectra from Axle-box Vibration: Development of a New Technique In: International Conference on Smart Infrastructure and Construction 2019 (ICSIC): Driving data-informed decision-making. 8-10 July 2019. (DeJong, M.J., Schooling, J.M., Viggiana, G.M.B. eds.) ICE Publishing, London. pp. 549-557. ISBN: 978-0-7277-6466-9. doi:10.1680/icsic.64669.549.
Fidler, P.R.A., Hartley, S., Talbot, J.P. (2017). Project VIMTO: a new system for the vibration and impact monitoring of tram operations. In: Structural Health Monitoring of Intelligent Infrastructure Conference 2017 (Structural Health Monitoring in Real-World Application). 5-8 December 2017. (Chan, T. and Mahini, S.eds.) Queensland University of Technology, Brisbane, Australia. Paper No. RS13-2, pp. 1029-1039. ISBN: 978-1-925553-05-5. doi:10.17863/CAM.15237.
Webb, G.T., Vardanega, P.J., Hoult, N.A., Fidler, P.R.A., Bennett, P.J. and Middleton, C.R. (2017) Analysis of Fiber-Optic Strain-Monitoring Data from a Prestressed Concrete Bridge. Journal of Bridge Engineering ASCE, 22(5) 05017002 doi:10.1061/(ASCE)BE.1943-5592.0000996.
Hoult, N.A., Bennett, P.J., Stoianov, I., Fidler, P.R.A., Maksimović, Č., Middleton, C.R., Graham, N.J.D. and Soga, K. (2009). Wireless Sensor Networks: creating 'Smart Infrastructure'. Proceedings of the Institution of Civil Engineers - Civil Engineering, 162(3), 136-143. doi:10.1680/cien.2009.162.3.136.

Other publications:

http://www3.eng.cam.ac.uk/~praf1/publications.html

Subject:

Artificial Intelligence and Machine Learning

Big Data

Build

Building Information Modelling (BIM)

Information Management Framework (IMF)