Young-Jin Cha

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Civil Engineering
Structural Health Monitoring
Computer vision based structural health monitoring, damage detection and condition assessment, real time monitoring and nonlinear system identification, structural control: Passive, active, semi-active, and hybrid (damage mitigations)
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Structural Health Monitoring:

Tall buildings are ubiquitous in major cities and house the homes and workplaces of many individuals. However, few studies have been carried out to study the dynamic characteristics of tall buildings based on field measurements. In this research, the dynamic behavior of the Green Building, a unique 20-story tall structure located on the campus of the Massachusetts Institute of Technology (MIT), was characterized and modeled as a simplified lumped-mass beam model (SLMM), using data from a network of accelerometers. The accelerometer network was used to record structural responses due to ambient vibrations, blast loading, and the October 16, 2012 earthquake near Hollis Center, Maine. Spectral and signal coherence analysis of the collected data was used to identify natural frequencies, modes, foundation rocking behavior, and structural asymmetries. A relation between foundation rocking and structural natural frequencies was also found. Natural frequencies and structural acceleration from the field measurements were compared with those predicted by the SLMM found to have good agreement.

Computer vision based health monitoring:

Video cameras offer the unique capability of collecting high density spatial data from a distant scene of interest. They can be employed as remote monitoring or inspection sensors for structures because of their commonplace availability, simplicity, and potentially low cost. An issue is that video data is difficult to interpret into a format familiar to engineers such as displacement. A methodology called motion magnification has been developed for visualizing exaggerated versions of small displacements with an extension of the methodology to obtain the optical flow to measure displacements. These methods are extended to modal identification in structures and the measurement of structural vibrations.

Damage detections and condition assessments:

In this research, a novel damage detection approach using hybrid multi-objective optimization algorithms based on modal strain energy is proposed to detect damages in various 3-dimensional (3-D) steel structures. Minor damages have little effect in the difference of the modal properties of the structure, and thus such damages with multiple locations in a structure are difficult to detect using traditional damage detection methods based on modal properties. Various minor damage scenarios are created for the 3-D structures to investigate the newly proposed multi-objective approach. The proposed hybrid multi-objective genetic algorithm detects the exact locations and extents of the induced minor damages in the structure. Even though the proposed method uses incomplete mode shapes lacking any measured information on the damaged element, it detects damages well. The robustness of the proposed method is investigated by adding 5% Gaussian random white noise as a noise effect to mode shapes which are used in the calculation of modal strain energy (Cha and Buyukozturk 2015).

Discrete wavelet transformation (DWT) method is applied to detect changes of the structural frequency contents due to damage of the structures. The information provided from the sudden peak value of detail signal from the high pass filter of the DWT is used to find time and location of the damage in the system.

Real-time monitoring and Nonlinear system identifications:

A non-contact damage detection methodology is proposed by integrating a computer vision based algorithm to measure structural dynamic responses and a nonlinear recursive filter. A motion magnification inspired technique using phase-based optical flow is used to measure structural displacements, and the unscented Kalman filter is used to predict structural properties such as stiffness and damping coefficient and detect structural damage. In order to detect structural damage using measured displacements from video, an unscented Kalman filter (UKF) is used to remove noise from the displacement measurement and simultaneously detect damage by identifying the current stiffness and damping coefficient values assuming known mass information. To validate the proposed damage detection method, dynamic equations of motion are derived for a steel cantilever beam excited by impact hammer, and state-space equations are also derived using the dynamic equation of motion without using external excitation input (Cha et al 2015).

1. Buyukozturk O., Long J., Mohamadi Ghazi R., Chen J., Cha Y.J., Smit D., “Structural Health Monitoring: A Quest towards the use of combined approaches,” Structural Health Monitoring, in preparation.

2. Cha Y.J. and Bai J.-W., “Estimation of structural fragility using multi-demand models for performance-based semi-active control design of nonlinear system,” Earthquake Engineering & Structural Dynamics, in preparation.

3. Cha Y.J. Chen J.G. and Buyukozturk O., “Computer vision based damage detection using motion magnification and unscented Kalman filter,” , in preparation.

4. Cha Y.J., Trocha P.A., and Buyukozturk O., “Field measurements based system identification of tall building using simplified lumped-mass model,” Structural Control & Health Monitoring, in preparation.

5. Cha Y.J., “Multi-performance-based decentralized semi-active control designs of moment-resisting frame buildings,”Computer-Aided Civil and Infrastructure Engineering, under review.

6. Cha Y.J. and Bai J.-W., “Seismic fragility estimates of high-rise buildings controlled by MR dampers using performance-based design,” Engineering Structures, under review (ENGSTRUCT-S-14-00740).

7. Temimi H., Ben-Romdhane M., El-Borgi S., Cha Y.J., Time-Delay Effects on Controlled Seismically Excited Linear and Nonlinear Structures,” International Journal of Structural Stability and Dynamics, in press (IF:1.059).

8. Mitchell R., Cha Y.J., and Kim Y., Mahajan A.A. (2015), “Active control of highway bridges subject to a variety of earthquake loads,” Earthquake Engineering and Engineering Vibration, Springer, 14(2): 253-263 (IF:0.475).

9. Chen J., Wadhwa N., Cha Y.J., Durand F., Freeman F., and Buyukozturk O. (2015), “Modal identification of simple structures with high-speed video using motion magnification,” Journal of Sound and Vibration, 345: 58-71 (IF: 1.857).

10. Cha Y.J. and Buyukozturk O. (2015), “Structural Damage Detection Using Modal Strain Energy And Hybrid Multi-Objective Optimization,” Computer-Aided Civil and Infrastructure Engineering, 30:347-358 (IF: 5.625).

11. Kim Y.J., Wakeel S.A., Gaddafi A. and Cha Y.J. (2015), “In-situ performance of severely deteriorated pervious concrete: A Case Study,” ACI Materials Journal, 112(2):295-304 (IF: 1.123).

12. Cha Y.J. and Agrawal A.K., “Robustness studies of sensor faults and noises for semi-active control strategies using large-scale MR dampers,” Journal of Vibration and Control, in press (IF: 4.355).

13. Friedman A., Dyke S.J., Phillips B., Ahn R., Dong B., Chae Y., Castaneda N., Jiang Z., Zhang J., Cha Y.J., Ozdagli A.I., Spencer B.F., Ricles J., Christenson R., Agrawal A., and Sause R. (2015), “Large-scale real-time hybrid simulation for evaluation of advanced damping system performance,” Journal of Structural Engineering (ASCE), 141(6), 04014150. (IF: 1.994)

14. Cha Y.J., Agrawal A.K., Friedman A., Phillips B., Ahn R., Dong B., Dyke S.J., Spencer B.F. Jr., Ricles J., and Christenson R. (2014), “Performance validations of semi-active controllers on a large-scale moment resisting frame equipped with a 200 kN MR damper using real- time hybrid simulations,” Journal of Structural Engineering (ASCE), 140(10), 04014066. (IF: 1.994)

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