BeeView 2: Data Interferometry for Field Monitoring: Development and applications in Structural and Crustal Systems

This project aims at developing novel data interferometry techniques for field monitoring of strutural and crustal systems. Additionally we are aiming to develop novel computer vision algoritms for structural inference. The core focus of this project is the city fo Groningen, Netherlands. This city serves as both a social and a technical challenge of resiliency. Groningen is the largest city in northern Netherlands spanning an area of 83.75 km2 with a population of approximately 200,000. It also has the largest gas field in Europe. Due to continuous natural gas production and storage in the area, induced seismicity has been reported, with an increase in number of events in the past decade. This has resulted in building damage. For example, the Martini Tower, a landmark of the region, completed in 1482 and constructed using blocks of sandstone, has shown movements due to the seismic events. Hence, the goal of the project involves ground motion detection and hypocenter localization, probabilistic source mechanism inversion, structural characterization and damage detection and development of a data-driven seismic fragility framework.




M. Uzun, H. Sun, D. Smit, and O. Buyukozturk. Structural identification and damage detection using Bayesian inference and seismic interferometry. Structural Control and Health Monitoring 2019; DOI:10.1002/stc.2445

R. Zhang, Z. Chen, S. Chen, J. Zheng, O. Buyukozturk, and H. Sun. Deep long short-term memory networks for nonlinear structurl seismic response prediction. Computers and Structures 2019; 220: 55-68.



MIT Civil and Environmental Engineering
MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)
MIT Earth and Planetery Sciences
MIT Lincoln Laboratory
Northeaster University, Civil and Environmental Engineering
Shell Global



Shell Global through MIT Energy Initiative



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