In 2013,the University of Nottingham as a leading organisation was awarded a grant from the Integrated Application Promotion(IAP)scheme of the European Space Agency(ESA)for a feasibility study(FS)entitled GeoSHM.The GeoSHM project uses integrated GNSS and Earth Observation technologies for the structural health monitoring of large bridges.During the GeoSHM FS period a small monitoring system was installed on the Forth Road Bridge in Scotland and the consortium have gathered huge data sets and rich experience regarding the design and implementation of the GeoSHM service according to essential user needs.To fully explore the opportunities of the GeoSHM FS project ESA further sponsored its second phase development which is a €2.3 million demonstration project and led by UbiPOS UK Ltd.The GeoSHM Demo Project focuses on addressing the major drawbacks of the GeoSHM FS Project and developing a smart data strategy to fully reflect the end user needs.However,during the two-year GeoSHM Demo Project,it has still been very difficult to find the right methods to extract the useful information from the enormous amount of data collected by the sensors to support a smart decision-making process.In 2017,a China-UK consortium applied and was awarded the funding from a joint infrastructure Systems Programme supported by Jiangsu Province in China and Innovate UK.The iSHM project(Cloud-based Intelligent Structural Health Monitoring Platform for Long-span Bridges)is developing an innovative SHM data strategy empowered with Big Data analytics and artificial intelligence to extract essential deformation information from a novel sensory system for the understanding of deformation mechanism from a cluster of large bridges of different types.Bridge health status and operational behaviours are precisely determined through precise models that define external/internal loading and bridge response using machine learning and cloud computing technologies.This paper will introduce the results of the GeoSHM Demo Project and the new design of the iSHM Project.The difference between these two projects will be discussed.It will provide useful information regarding the efforts of our SHM work in the past three years.
Structural health monitoring GNSS Long bridge Data strategy Machine learning
Yilin Xie Xiaolin Meng Panos Psimoulis Dinh Tung Nguyen John S.Owen George Ye Laiyi Wu Shuguo Pan Jun Qian Paul Bhatia Bill Valentine Patrick Madden
Faculty of Engineering,University of Nottingham,Nottingham,UK UbiPOS UK Limited,Nottingham,UK China Railway Bridge(Nanjing)Bridge and Tunnel Inspect & Retrofit Co.,Ltd,Nanjing,China School of Instrument Science and Engineering,Southeast University,Nanjing,China Jiangsu Province Water Resource Research Institute,Nanjing,China Geomatics Ventures Ltd,Nottingham,UK Forth Bridge Unit,Amey Consulting,Edinburgh,UK