March 2022

Monitoring Technologies for Smart Cities and Civil Infrastructure Systems

To Be Presented:  March 23rd, 11:00 AM Central Time
Speaker: Professor F. Necati Catbas, University of Central Florida

ABSTRACT

The proportion of urban population in the world is expected to increase from 54% currently to 70% by 2050. A majority of Americans also reside in urban regions - according to the 2010 census 80% of Americans reside in urban areas. Given the large number of urban citizens in the world (and US) it is imperative that we identify solutions to improve the quality of life for urban residents and economic vitality of our cities. Studies to address and fulfill the needs of envisioned future smart city infrastructure should successfully integrate a range of engineering, humanities and sociological fields such as emerging communication technologies, Internet of Things (IoT), cyber security, cloud computing, intelligent transportation, infrastructure monitoring, analyzing tourism, theorizing structures of government and bureaucracy, project financing, public policy development and implementation. In this talk, we will first three overarching themes: (1) technologically advanced infrastructure with sensing and communication capability, (2) urban operations and services improved with better decisions using multilayered “big data”, and (3) utilization of technology for social, public policy, planning and governance to improve urban quality of life. Next, we will present a sampling of relevant U.S. research and education achievements in structural control and monitoring as compiled by U.S. Panel that are envisioned as concepts for smart cities. Finally, we will present our recent work at UCF CITRS in the area of structural health monitoring where novel technologies such as computer vision, deep learning have been developed for our existing and next generation of smart city infrastructure. 

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April 2022

STRUCTURAL HEALTH MONITORING: FROM VIRTUAL SHAKERS TO MACHINE LEARNING

To Be Presented:  April 29th, 10:00 AM Central Time
Speaker: Professor Ahsan Kareem, University of Notre Dame

ABSTRACT

Structural health monitoring (SHM) is a critical means of assessing the performance of aging civil infrastructure. Earlier studies used heavy dynamic shakers to assess the dynamics of structures, which are unwieldy and reliance has been shifted to ambient vibration of structures to extract dynamic features. This study introduces a “virtual shaker” concept that effectively replaces the physical shaker and provides all the desired features for SHM. Examples are provided along with an App that facilitates the use of this concept to identify dynamic features. A traditional structural health monitoring (SHM) operation requires a wired system, which is often termed “hub and spoke” because the sensors are located throughout the structure and then wired to a central data acquisition server. To avoid issues associated with long cables, a unique prototype system in SHM, SmartSync, an “IoT”, with “edge computing,” which utilizes the building's existing Internet backbone as a system of “virtual” instrumentation cables and limited computations at the sensor location has been developed. Since the system is modular and largely “plug-and-play”, the units can be rapidly deployed at any location with access to power and an Internet connection and has been implemented in the Burj Khalifa, the tallest building in the world. For rural footbridges remotely located, the citizen sensing approach has been used to monitor their response in storms and to identify their dynamic feature. An example from Nicaragua will be presented. In an age of unprecedented sensing technology that allows for greater volumes of climate and infrastructure-related data to be collected and analyzed places a new demand. This proliferation of data has led to building data-driven models to better assess our infrastructure and implement solutions oriented towards sustainability and resiliency. The seminar will address new developments in system identification involving non-stationary observations and their real-time monitoring. Machine learning is becoming ubiquitous in this context and is enabling data-to-model and automated feature extraction from SHM observations. The use of various machine learning schemes embedded with Hilbert, Wavelet and Shapelet transforms will be presented with examples from Burj Khalifa, Sutong Yangtze River Bridge and the European Union’s surface wind monitoring in the Port of Genoa, Italy. 

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