Monitoring and Maintenance of Civil Infrastructure
Monitoring and maintenance of buildings and the aging infrastructure network are emerging as critical engineering applications, especially within the context of the Smart Cities movement. While data may be increasing in availability, with the long-promised era of ubiquitous sensing on the horizon, structural monitoring has little value without the means to translate it into knowledge that supports decision-making.
This translation, achieved through structural identification, can provide key understanding of structural performance and is necessary for improving predictive capabilities and ultimately for equipping engineering with the knowledge to build longer, taller, and more sophisticated structures. In parallel, advanced diagnosis and data-driven decision support tools are critical for prioritizing actions and managing the aging civil infrastructure.
Success in these efforts requires advanced statistical and Bayesian inference tools and a rational uncertainty quantification framework that can use the assimilated data to make intelligent prognosis and guide decisions.
Optimal sensor placement plays an additional critical role in this framework, so that value of obtained information is maximized and a truly risk-based prioritization of maintenance efforts can be delivered. Only through such advances can structural identification be effectively leveraged to realize the full potential of the Smart Cities movement and provide the critical feedback to advance state-of-the-art design.