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Research Programs


High-Rate Structural Health Monitoring

Our research program on high-rate structural health monitoring (HR-SHM) investigates how fast we can estimate the health of a system. The research is applicable to high-rate systems, which are defined as engineering systems experiencing high-rate (<100 ms) and high-amplitude (acceleration > 100 gn) events such as a blast or impact. Examples of such systems include hypersonic vehicles, advanced weaponry, and blast mitigation systems. The interested reader can refer to the following introductory paper on the field of HR-SHM pdf.

LSTM neural network learning a nonlinear time series Example of a high-rate dynamic system (video from the AFRL)

Funded research efforts include:

  • Development of high-rate observers for unknown high-rate systems, funded by the AFOSR.
  • Study of real-time machine learning algorithms to learn non-stationary dynamics on-the-fly, funded by the NSF.
  • Investigation of data-based strategies for real-time prognostic of agricultural machinery health, funded by the NSF.

Key collaborators to the program include:

  • Chao Hu (Iowa State University)
  • Jacob Dodson (AFRL/RW)
  • Austin Downey (USC)
  • Jason Bakos (USC)

Sensing Skin for Structural Health Monitoring

Our research program on sensing skin for SHM studies soft sensing technologies that emulate biological skin to enable local sensing over global areas. The soft sensing technology is centered around a soft elastomeric capacitor (SEC) behaving as a strain gauge and capable of covering large areas at low costs (see key publication on the SEC pdf). Recent research developments have demonstrated the SEC technology in a network configuration to detect and localize fatigue cracks on steel bridges (see key publication on the application pdf).

Highly stretchable SEC materials (up to 500% elastic) SEC in network configuration measuring strain fields

Funded research efforts include:

  • Development of sensing skin for fatigue crack detection and localization, funded by USDOT through a pooled-fund initiative.
  • Development of soft sensing technologies to measure soft tissue deformations, funded by a joint ISU - U. of Iowa initiative.
  • Development and demonstration of sensing skin capability at monitoring wind turbine blades, funded by the IEC.
  • Development and characterization of the SEC, funded by the ASNT and IAWIND.

Key collaborators to the program include:

  • Matthias Kollosche (Harvard)
  • Filippo Ubertini (U. of Perugia)
  • Jian Li (U. of Kansas)
  • Caroline Bennett (U. of Kansas)
  • William Collins (U. of Kansas)
  • Hongki Jo (U. of Arizona)
  • Eric Zellner (ISU)
  • Iris Rivero (RIT)

High-Performance Control Systems

Our research program on high-performance control systems (HPCS) studies novel strategies to reduce vibrations in civil structures caused by natural and/or man-made hazards. These strategies include novel mechanical damping devices, intelligent control algorithms, and framework to financially assess the benefits of HPCSs. In particular, we have studied novel rotary variable friction devices (see key publication pdf), novel semi-active cladding connection (see key publication pdf), intelligent controllers (see key publication pdf), and surrogate models for life-cycle analysis of HPCS (see key publication pdf).

Cam damper's hysteresis being characterized Variable friction cladding connection for multi-hazard mitigation

Funded research efforts include:

  • Study of a novel rotary friction device, funded by the NSF.
  • Study of a novel semi-active variable friction cladding connection, funded by the NSF.
  • Formulation of a probabilistic-based performance based design procedure for buildings equipped with HPCS, funded by the NSF.
  • Evaluation of capillary systems embedded in shear walls to mitigate wind-induced vibrations, funded by the NSF.

Key collaborators to the program include:

  • Jim Ricles (Lehigh)
  • Liang Cao (Lehigh)
  • Austin Downey (USC)

Multi-functional Materials

Our research program on multi-functional materials studies novel methods to augment conventional structural materials with additional functionalities, in particular self-sensing. For instance, we are studying how we can alter the electrical properties of self-sensing materials through the use of nano- and micro-fillers, including carbon nanotubes (see key publication pdf) and carbon black (see key publication pdf). We have explored the use of self-sensing cementitious materials to create smart pavements (see key publication pdf). Other examples of self-sensing materials that we have studied include smart clay bricks (see key publication pdf) and smart carbon-fiber reinforced polymers (see key publication pdf). Our group has produced a tutorial publication on multi-functional materials (pdf). Our research program on multi-functional materials is conducted in collaboration with the Professor Filippo Ubertini at the University of Perugia. Professor Ubertini is leading efforts on smart cementitious materials.

Smart cement paste in action (test conducted at the University of Perugia) Smart carbon fiber-reinforced polymer (CFRP) in action

Funded research efforts include:

  • Development of smart pavements, funded by the European Commission.
  • Development of smart bricks, funded by the Italian Ministry of Education.
  • Study of a new multi-functional shear wall for energy distribution and hazard mitigation, funded by the NSF.
  • Study of smart concrete for NDE, funded by the ASNT.

Key collaborators to the program include:

  • Filippo Ubertini (U. of Perugia)
  • Antonella D'Alessandro (U. of Perugia)
  • Austin Downey (USC)