LIFECYCLE MANAGEMENT, MONITORING AND ASSESSMENT FOR SAFE LARGE-SCALE INFRASTRUCTURES: CHALLENGES AND NEEDS

Many European infrastructures dating back to ’50 and ’60 of the last century like bridges and viaducts are approaching the end of their design lifetime. In most European countries costs related to maintenance of infrastructures reach a quite high percentage of the construction budget and additional costs in terms of traffic delay are due to downtime related to the inspection and maintenance interventions. In the last 30 years, the rate of deterioration of these infrastructures has increased due to increased traffic loads, climate change related events and man-made hazards. A sustainable approach to infrastructures management over their lifecycle plays a key role in reducing the impact of mobility on safety (over 50 000 fatalities in EU per year) and the impact of greenhouse gases emission related to fossil fuels. The events related to the recent collapse of the Morandi bridge in Italy tragically highlighted the sheer need to improve resilience of aging transport infrastructures, in order to increase the safety for people and goods and to reduce losses of functionality and the related consequences. In this focus Structural Health Monitoring (SHM) is one of the key strategies with a great potential to provide a new approach to performance assessment and maintenance over the life cycle for an efficient, safe, resilient and sustainable management of the infrastructures. In this paper research efforts, needs and challenges in terms of performance monitoring, assessment and standardization are described and discussed.The networking support of COST Action TU1402 on ‘Quantifying the Value of Structural Health Monitoring’ and of COST Action TU1406 on ‘Quality specifications for roadway bridges, standardization at a European level (BridgeSpec)

Remote vital signs monitoring using a mm-wave FMCW radar

A vision on the migration from contact standard health monitoring measurement devices to non-contact measurement technologies has gained a tremendous attention in literature and in industry. A promising method for realizing the remote measurement of vital signs is using electromagnetic radars such as frequency modulated continuous wave (FMCW) radars. However, using these radars has challenges to precisely acquire the respiration and heart rates. A solution for higher accurate measurement of the vital signs can be the use of mm-wave frequencies, which gives a high-resolution sensing of displacements in an environment in the order of sub-mm changes. On the other hand, being in mm-wave bands increases both hardware and signal processing designs and implementations. In this work, a mm-wave radar is used to monitor the breathing and the heart rates as well as their waveforms for further clinical diagnostics. To that end, we established a complete analysis of the FMCW radars principles by considering hardware impairments. The analysis considers the effect of antenna coupling, RF cross-talk, stationary clutters, phase noise, IQ imbalances, and the thermal noise. Also, the effect of the individual hardware imperfections on the phase quality is shown by simulations and experiments. The simulations are carried out with a Matlab Simulink model. For the experiments, Texas Instruments (TI) mm-wave FMCW radars have been used. To earn insight into vital signs monitoring, different experiments are designed. In the experiments, the effect of the thermal instability of the RF parts on the phase is shown. In addition, to mimic the behaviour of the chest vibration due to respiration and the heartbeats, a two-pendulum system is designed and tested. Particularly, the pendulum system performance in terms of vibration frequency estimations of the two pendulums versus distance is then measured. In the simulations, the system performance is obtained for different signal to noise ratios (SNR) and different phase noise levels, as well as different stationary clutters. Finally, to test the TI sensors for different directions to the subjects, Hexoskin smart garment is used as a reference sensor, which is a reliable commercial product. Our results show great system improvement in terms of accuracy of the vital signs detection in comparison to other similar research. For different sleep positions, the accuracy of HR and BR are greater than 94\% and 96\%, respectively. In addition to detecting the vital rates, we have shown that their waveforms can also be reconstructed by using an adaptive optimum filter