Measuring and modelling the thermal performance of the Tamar Suspension Bridge using a wireless sensor network

Abstract

A study on the thermal performance of the Tamar Suspension Bridge deck in Plymouth, UK, is presented in this paper. Ambient air, suspension cable, deck and truss temperatures were acquired using a wired sensor system. Deck extension data were acquired using a two-hop wireless sensor network. Empirical models relating the deck extension to various combinations of temperatures were derived and compared. The most accurate model, which used all the four temperature variables, predicted the deck extension with an accuracy of 99.4%. Time delays ranging from 10 to 66 min were identified between the daily cycles of the air temperature and of the structural temperatures and deck extension. However, accounting for these delays in the temperature–extension models did not improve the models' prediction accuracy. The results of this study suggest that bridge design recommendations are based on overly simplistic assumptions which could result in significant errors in the estimated deck movement, especially for temperature extremes. These findings aim to help engineers better understand the important aspect of thermal performance of steel bridges. This paper also presents a concise study on the effective use of off-the-shelf wireless technology to support structural health monitoring of bridges.

Keywords::

• monitoring
• structural design
• suspension bridges
• thermal effects
• structural health monitoring
• wireless sensor network

Acknowledgements

The authors acknowledge the assistance given by National Instruments in providing some of the WSN equipment used in this investigation and the on-site support given by Steve Rimmer, Richard Cole and David List from the Tamar Bridge and Torpoint Ferry Joint Committee.

Notes

^1. Email: [email protected]

^2. Email: [email protected]

^3. Email: [email protected]

^4. Higher prediction accuracy standard deviation implies lower prediction precision. Prediction precision is a measure of reproducibility, i.e. the level of consistency in the model's accuracy from one prediction to another.

Additional information

Funding

FundingThis research was supported by EPSRC grant EP/F035403/1: Novel data mining and performance diagnosis systems for structural health monitoring of suspension bridges. The first author was supported by the joint A*STAR Research Attachment Program between the Agency for Science, Technology and Research (A*STAR) in Singapore and The University of Sheffield, UK.