Online Stuctural Health Monitoring

We are designing a framework for structural health monitoring that functions fully online, without any prior information about the structure. As part of this effort, we are exploring several key principles that could help to accomplish this goal:

  1. Machine learning strategies that can handle high dimensional data, in order to avoid information loss due to dimensionality reduction.
  2. The use of Generative Adversarial Networks to overcome data scarcity and data imbalance.
  3. Alarm threshold tuning based on reliability analysis to reduce sensitivity to user-defined parameters.

Related Publications

Journal Articles

A general framework for supervised structural health monitoring and sensor output validation mitigating data imbalance with generative adversarial networks-generated high-dimensional features

Mohammad Hesam Soleimani-Babakamali; Roksana Soleimani-Babakamali; Rodrigo Sarlo

A general framework for supervised structural health monitoring and sensor output validation mitigating data imbalance with generative adversarial networks-generated high-dimensional features Journal Article

Structural Health Monitoring, 2021.

Abstract | Links | BibTeX

Mast Arm Monitoring via Traffic Camera Footage: A Pixel-Based Modal Analysis Approach

Mohammad Hesam Soleimani-Babakamali; A Moghadam; Rodrigo Sarlo; M H Hebdon; P S Harvey

Mast Arm Monitoring via Traffic Camera Footage: A Pixel-Based Modal Analysis Approach Journal Article

Experimental Techniques, 2021, ISBN: 1747-1567.

Abstract | Links | BibTeX

Leave a Reply

Your email address will not be published. Required fields are marked *