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Complex Dynamics Smart Sensing and Hazard Mitigation Lab

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Louisiana State University
Complex Dynamics Smart Sensing and Hazard Mitigation
Professor Chao Sun

November 2018

Problems

Civil infrastructures nowadays are more frequently impacted than before by multiple hazards including earthquakes, winds, ocean waves, storm surges, flooding, current and so on. Meanwhile, structures will exhibit considerable nonlinearity under extreme single/multiple loading conditions. The combined multiple loading effect and structural nonlinearity render the structural dynamic characteristics more complex than usual, which will inevitably undermine the structural performance, damage the structural integrity and cause potential loss of properties and lives. To identify and prevent such damage and disasters, three key issues need to be addressed: (a) understanding the complex dynamic characteristics of structures, (b) mitigating the multiple hazards-induced excessive dynamic response and (c) sensing and damage diagnosis of critical civil infrastructures and mechanical systems for decision-making. 

Addressing key issue (a) requires development of methodologies to model the complex dynamic behavior under single/multiple hazards and provide physics insight into the complex dynamic process. Specifically, structures under multiple hazards/extreme loading will exhibit nonlinear and stochastic characteristics due to structural nonlinearity, hazard interaction and cascading effects. As a result, the structures will suffer from amplified dynamic response, dynamic instability, chaos, bifurcations and jump.

Addressing key issue (b) requires development of effective control strategies and the associated devices that can accommodate the time-varying environmental effects, the nonlinear and stochastic characteristics of target structures. To achieve this, our research group focuses on developing adaptive /passive control methods to mitigate the nonlinear, stochastic and three dimensional vibration of civil/mechanical structures exhibiting complex dynamic characteristics. Benefit of nonlinear stiffness and nonlinear damping is also being explored for vibration control.     

Addressing key issue (c) requires development of a novel paradigm for damage diagnosis. Given the limitations of model-based methods for damage diagnosis, our research group is focusing on developing a new paradigm on the basis of machine learning (deep learning) and computer vision techniques. The paradigm under development has the potential to enable automatic and intelligent damage diagnosis for a wide range of civil/mechanical/aerospace structures.  


Approach

To address key issue (a), our research group combines the state-of-art analytical methods, i.e. the multi-scale perturbation method and computational method to model the complex dynamic behavior of structures. Experimental study is also implemented to verify the analytical and computational results. 

To address key issue (b), our research group focuses on developing adaptive/passive control methods to mitigate the nonlinear, stochastic and three dimensional vibration of civil/mechanical infrastructures. Self-adaptive nonlinear stiffness and nonlinear damping is being explored for vibration control and produced satisfactory results.

To address key issue (c), our research group is focusing on developing a new paradigm on the basis of machine learning (deep learning) approaches and the emerging computer vision techniques for damage diagnosis. Preliminary results indicate that the new method has the potential to overcome data incompleteness issues for damage diagnosis.


Findings

Findings from this research will advance the understanding of the complex dynamic characteristics of civil/mechanical structures with considerable nonlinearity and under multiple loading effect. The control techniques under development will effectively preserve the structural performance and integrity as well as prevent potential loss of properties and lives, thereby achieving a more secure and resilient community. The machine learning based paradigm under development has the potential to enable automatic and intelligent damage diagnosis for a wide range of civil/mechanical/aerospace structures.

Selected Publications

  1. C. Sun, V. Jahangiri. (2018), Bi-directional Vibration Control of Offshore Wind Turbines Using a 3D Pendulum Tuned Mass Damper. Mechanical System and Signal Processing , 105: 338-360.
  2. Z. Zhang, C. Sun, R. Bridgelall, M. Sun, (2018). Application of a Machine Learning Method to Evaluate Road Roughness from Connected Vehicles. Journal of Transportation Engineering, Part B: Pavements , 144(4)
  3. C. Sun, (2017). Semi-active Control of Offshore Wind Turbines under Multi-Hazards. Mechanical System and Signal Processing , 99: 285-305.
  4. C. Sun, (2017). Mitigation of Offshore Wind Turbines under Wind-wave Load: Considering Soil Structure Interaction and Damage. Structural Control and Health Monitoring , 25(3): 1-22.
  5. C. Sun, S. Nagarajaiah, A. J. Dick, (2015). Experimental Investigation of Vibration Attenuation Using Nonlinear Tuned Mass Damper and Pendulum Tuned Mass Damper in Parallel. Nonlinear Dynamics , 78(4): 2699-2715.
  6. C. Sun, S. Nagarajaiah, (2014). Study on Semi-active Tuned Mass Damper with Variable Damping and Stiffness under Seismic Excitations. Structural Control and Health Monitoring , 21(6): 890-906
  7. C. Sun, S. Nagarajaiah, A. J. Dick, (2014). Family of Smart Tuned Mass Dampers with Variable Frequency under Harmonic Excitations and Ground Motions: Closed-Form Evaluation. Smart Structures and Systems, 13(2): 319-341.
  8. C. Sun , R. P. Eason, S. Nagarajaiah, A. J. Dick (2013). Hardening Dűffing Oscillator Attenuation Using a Nonlinear TMD, a Semi-active TMD and Multiple TMD. Journal of Sound and Vibration , 332(4): 674-686.


Core Competencies

  • Nonlinear Dynamics Modeling
  • Adaptive/Passive Control Strategies of Civil/Mechanical/Aerospace Structures
  • Multiple Hazards Loading Effect (Wind Loading, Wave-/Storm Surge-Structure Interaction) Modeling and Mitigation
  • Machine Learning Methodologies for Damage Diagnosis to Overcome Data Incompleteness
  • Signal Processing Techniques

EMI RGP Dec 2019 Dynamics Chao Sun
Complex Dynamics Modeling, Smart Sensing and Hazard Mitigation

Researchers

  • Chao Sun (Faculty)
  • Zhiming Zhang (PhD Student)
  • Vahid Jahangiri (PhD Student)
  • Tianqi Ma (PhD Student)
  • Patrick Duffy (M.S. Student)
  • Juan D Amador (U.G. Student)
  • Lauren Mills (U.G. Student)
  • Hannah Pittman (U.G. Student)
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