Jin Yan

Research

Real-Time Structural Health Monitoring

Real-time SHM contains continuous updating the model from sensory data to inform decision making.

High-Rate Dynamic System Identification

The research on high-rate dynamic system health monitoring investigates how fast we can idnetify the system changes under large uncertainties. High-rate dynamic systems are defined as engineering systems experiencing dynamic events of typical amplitudes higher than 100 gn for a duration of less than 100 ms.

  1. Jin Yan, Simon Laflamme, Jonathan Hong, Jacob Dodsona "Online Parameter Estimation under Non-Persistent Excitations for High-Rate Dynamic Systems." doi.
  2. Jin Yan, Simon Laflamme, Premjeet Singh, Ayan Sadhu, Jacob Dodsona "A Comparison of Time-Frequency Methods for Real-time Application to High-Rate Dynamic Systems." doi.

Sensor Development

Advances in intelligent infrastructure can be achieved through the use of novel materials for increased system-level efficiency and multifunctionality. Sensor development continues to be an area of science and technology with great growth potential for innovation at Structural Health Monitoring.

Carbon Fiber Reinforced Polymer Self-Sensing Capacitor

Carbon Fiber-Reinforced Polymer (CFRP) has been widely used in strengthening, rehabilitating, and retrofitting of existing structures because of its speed of deployment, low maintenance requirement, and high strength-to-weight ratio. In this work, the authors propose a novel method to augment CFRP with self-sensing capabilities. The sensor consists of two CFRP layers separated by a titania-filled epoxy dielectric layer, therefore forming a parallel plate capacitor. Sensing capability can be achieved through variations in the sensor's capacitance provoked by strain, therefore providing an additional function that could be leveraged for structural health monitoring and structural health management purposes.
  1. Jin Yan, Austin Downey, An Chen, Simon Laflamme, Sammy Hassan "Capacitance-Based Sensor with Layered Carbon-Fiber Reinforced Polymer and Titania-Filled Epoxy." Composite Structures. Volume 227, 1 November 2019, 111247.url

Soft Elastomeric Capacitor for Structural Health Monitoring

One of the research involves the development of a soft-elastomeric capacitive (SEC)-based thin film sensor for monitoring strain on mesosurfaces. Arranged in a network configuration, the sensing system is analogous to a biological skin, where local strain can be monitored over a global area. The sensor output contains the additive strain measurement, in capacitance, of the two principal strain components over the monitored surface. The SEC sensor provides a low-cost, durable and robust sensing technology for the monitoring of mesoscale structures e.g. wind turbine blades, civil structures and aerospace structures.

  1. Jin Yan, Austin Downey, Alessandro Cancelli, Simon Laflamme, An Chen, Jian Li, and Filippo Ubertini, "Concrete Crack Detection and Monitoring Using a Capacitive Dense Sensor Array." Sensors, 2019.url
  2. Detection and Monitoring of Cracks in Reinforced Concrete Using an Elastic Sensing Skin. Jin Yan, Austin Downey, Alessandro Cancelli, Simon Laflamme, An Chen. Structures Congress 2019.url
  3. Austin Downey, Jin Yan, Eric Zellner, Karl Kraus, Iris Rivero, and Simon Laflamme, "Use of flexible sensor to characterize biomechanics of canine skin." BMC Veterinary Research, 2019. url


Structural Health Assessment

There are four critical components in the structural health assessment:

  • Detecting the existence of the damage on the structure
  • Locating the damage
  • Identifying the types of damage
  • Quantifying the severity of the damage

    • Model Assisted Validation and Probability of Detection

    Recent advances in sensing are empowering the deployment of inexpensive dense sensor networks (DSNs) to conduct structural health monitoring (SHM) on large-scale structural and mechanical systems. There is a need to develop methodologies to facilitate the validation of these DSNs. Such methodologies could yield better designs of DSNs, enabling faster and more accurate monitoring of states for enhancing SHM. First, an approximate physical representation of the system, termed the physics-driven surrogate, is created based on the sensor network configuration. The representation consists of a state-space model, coupled with an adaptive mechanism based on sliding mode theory, to update the stiffness matrix to best match the measured responses, assuming knowledge of the mass matrix and damping parameters. Second, the physics-driven surrogate model is used to conduct a series of numerical simulations to map damages of interest to relevant features extracted from the synthetic signals that integrate uncertainties propagating through the physical representation. The capacity of the algorithm at detecting and localizing damages is quantified through probability of detection (POD) maps. It follows that such POD maps provide a direct quantification of the DSNs’ capability at conducting its SHM task.

    1. Computational Framework for Dense Sensor Network Evaluation Based on Model Assisted Probability of Detection. Jin Yan, Simon Laflamme, Leifur Leifsson. Materials Evaluation Volume 78, Issue 5, pgs. 573 - 583 url
    2. Model-assisted validation of a strain-based dense sensor network. Jin Yan, Xiaosong Du, Simon Laflamme, Leifur Leifsson, Chao Hu, An Chen. Proceedings Volume 10970, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019. url