BeeView: Multi-sensor Monitoring of Structure, Measurement, Inference, and Visualization

“Beeview” is an interdisciplinary project to monitor the condition of large-scale infrastructure systems by analyzing motions of the structure using data acquired from multiple sensors deployed throughout the structure. The project has multiple components; three components that constitute the primary thrust areas are: the instrumentation of the sensors, sensor placement planning, and structural analysis from sensor data. A fourth component explores the visualization of structural motions from video data in a technique called motion magnification. BeeView is a joint research project between MIT’s Civil and Environmental Engineering Department (CEE), Computer Science and Artificial Intelligence Lab (CSAIL), Draper Labs, and Shell.

 

Key contributions

Interactive low power wireless sensor system and optimal task allocation for smart sensing

 

 

Motion Magnification: Video-based vibration measurements

 

 

Pairwise Graphical Models for damage detection

 

 

Conditional classifiers and Boosted conditional Gaussian mixture model for novelty detection

 

 

Kernel dependence analysis for novelty detection

 

 

Seismic interferometry-based structural monitoring

 

Media

• Cardno, Catherine A. (2017) “Technique to Monitor Building ‘Health’ Utilizes Ambient Vibrations” Civil Engineering: The Magazine of the American Society of Civil Engineers. February 2017.
• Chu, Jennifer. October 18, 2016. “With new model, buildings may “sense” internal damage“. MIT News.
• MIT researchers make buildings smart enough to ‘sense’ internal damage over time, Fox News, 2016.
• Intelligent building may ‘sense’ internal damage after earthquake, Yahoo! News, 2016.
• With new model, buildings may ‘sense’ internal damage, Science Daily, 2016.
• New ‘smart’ buildings may sense internal damage in real time, Business Standard, 2016.
• Intelligent building may ‘sense’ internal damage after earthquake, The Economic Times Science, 2016.
• With New Model, Buildings May 'Sense' Internal Damage, Science Newsline Technology.
• Intelligent building may sense internal damage after earthquake, The Hans India.
• Digital Nervous System Would Let Buildings Detect Their Own Weaknesses, Popular Mechanics.
• MIT Monitors Building Health, Durability + Design.
• New smart buildings may sense internal damage in real time, India Today.
• Buildings of future may 'sense' internal damage after earthquake, Earthquake News.
• Intelligent building may sense internal damage after earthquake, I4U News.
• New 'smart' buildings may sense internal damage in real time, Press Trust of India News.
• Intelligent building may 'sense' internal damage after earthquake, NewsX.
• MIT researchers develop sensing system to spot structural weakness in buildings, Construction Dive.
• Future buildings could 'sense' internal damage after earthquake, says research, India TV News.
• With new model, buildings may "sense" internal damage, Universities News.
• New smart buildings may sense internal damage in real time, The Free Press Journal.
• With New Computational Model, Buildings May “Sense” Internal Damage, Tech Explorist.
• New Smart Buildings able to Sense Internal Damage in Real Time, Tech Facts Live.
• Conner-Simons, Adam and Gordon, Rachel. August 2, 2016. “Reach in and touch objects in videos with 'Interactive Dynamic Video'”. MIT News.
• Calechman, Steve. April 25, 2016. “Designing a better built environment”. MIT News.
• Knapschaefer, Johanna. December 3, 2015. “High-Speed Video System Tested for Structure Monitoring”. Engineering News-Record.
• Damrad, Kelsey. August 27, 2015. “Observing invisible vibrations”. MIT News.
• Hardesty, Larry. May 21, 2015. “Gauging materials' physical properties from video”. MIT News.
• Akst, Daniel. May 15, 2015. “Monitoring Tiny Vibrations to Avert Big Problems”. The Wall Street Journal.
• Chu, Jennifer. April 23, 2015. “Magnifying vibrations in bridges and buildings”. MIT News.

 

Publications

R. Mohammadi-Ghazi, R.E. Welsch and O. Buyukozturk. Kernel dependence analysis and graph structure morphing for novelty detection with high-dimensional small size dataset. Mechanical Systems and Signal Processing 2020; 143: 106775

J. Long and O. Buyukozturk. Collaborative duty cycling strategies in energy harvesting sensor networks. Computer Aided Civil and Infrastructure Engineering, 2020; 35(6): 534-548.

J. Long and O. Buyukozturk. A power optimised and re-programmable system for smart wireless vibration monitoring. Structural Control and Health Monitoring 2019; 27(2): e2468.

J. G. Chen, T. M. Adams, H. Sun, E. S. Bell and O. Buyukozturk. Camera-based vibration measurement of the Portsmouth, NH WWI Memorial Bridge. Journal of Structural Engineering, ASCE 2018; 144(11); 04018207.

R. Mohammadi-Ghazi, Y. M. Marzouk and O. Buyukozturk. Conditional classifiers and boosted conditional Gaussian mixture model for novelty detection. Pattern Recognition 2018; 81: 601-614.

H. Sun and O. Buyukozturk. The MIT Green Building benchmark problem for structural health monitoring of tall buildings. Structural Control and Health Monitoring 2018; 25(3): e2115.

A. Mordret, H. Sun, G. A. Prieto, M. N. Toksoz and O. Buyukozturk. Continuous monitoring of high-rise buildings using seismic interferometry. Bulletin of the Seismological Society of America 2017; 107(6): 2759-2773.

J. G. Chen and O. Buyukozturk. A symmetry measure for detecting changes in mode shapes. Journal of Sound and Vibration 2017; 408: 123-137.

N. Wadhwa, J. G. Chen, J. B. Sellon, D. Wei, M. Rubinstein, R. Ghaffari, D. M. Freeman, O. Buyukozturk, P. Wang, S. Sun, S. H. Kang, K. Bertoldi, F. Durand and W. T. Freeman. Motion microscopy for visualizing and quantifying small motions. Proceedings of the National Academy of Sciences 2017; 201703715.

Z. Dzunic, J. G. Chen, H. Mobahi, O. Buyukozturk and J. W. Fisher. A Bayesian state-space approach for damage detection and classification. Mechanical Systems and Signal Processing 2017; 96: 239-259.

R. Mohammadi Ghazi, J. G. Chen and O. Buyukozturk. Pairwise graphical models for structural health monitoring with dense sensor arrays. Mechanical Systems and Signal Processing 2017; 93: 578-592.

J. Long and O. Buyukozturk. Decentralised One Class Kernel Classification Based Damage Detection and Localisation. Structural Control and Health Monitoring 2017; 24(6): e1930.

A. Davis, K.L. Bouman, J.G. Chen, M. Rubinstein, O. Buyukozturk, F. Durand and W.T. Freeman. Visual Vibrometry: Estimating Material Properties from Small Motions in Video. IEEE Transactions on Pattern Analysis and Machines Intelligence 2017; 39(4): 732-745.

Y-J. Cha, J. G. Chen and O. Buyukozturk. Output-only computer vision based damage detection using phase-based optical flow and unscented Kalman filters. Engineering Structures 2017; 132, 300-313. <

H. Sun, A. Mordret, G. A. Prieto, N. Toksoz and O. Buyukozturk. Bayesian characterization of buildings using seismic interferometry on ambient vibrations. Mechanical Systems and Signal Processing 2017; 85: 468-486.

J. G. Chen, A. Davis, N. Wadhwa, F. Durand, W. T. Freeman and O. Buyukozturk. Video camera-based vibration measurement for civil infrastructure applications. ASCE Journal of Infrastructure Systems 2016; in press. DOI: 10.1061/(ASCE)IS.1943-555X.0000348

N. Wadhwa, H-Y. Wu, A. Davis, M. Rubinstein, E. Shih, G. J. Mysore, J. G. Chen, O. Buyukozturk, J. V. Guttag, W. T. Freeman and F. Durand. Eulerian video magnification and analysis. Communications of ACM 2016; 60(1): 87-95.

Y-J. Cha, P. Trocha and O. Buyukozturk. Field measurement based system identification and dynamic response prediction of a unique MIT building. Sensors 2016; 16(7): 1016.

J. G. Chen, N. Wadhwa, Y.-J. Cha, F. Durand, W. T. Freeman and O. Buyukozturk. Modal Identification of Simple Structures with High-Speed Video using Motion Magnification. Journal of Sound and Vibration 2015; 345: 58-71.

 

Project Team

Sponsors

Shell Global through MIT Energy Initiative

 

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