Real-time Big-Data Visualization

In order to make informed decision about structural integrity or building operations, it is important to search, organize, and visualize vast amounts of vibration data effectively. We are using the highly-instrumented Goodwin Hall Smart Building as a platform for real-time data visualization to address some of the fundamental and practical challenges in this field.

Collaborators: Zhiwu Xie (Virginia Tech)

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

ISWIM Award and Newletter Feature

The VIBEs Team’s research in integrated Structural Health Monitoring and Bridge Weigh in Motion earned Amin Moghadam an International Society of Weigh in Motion (ISWIM) Young Researcher Award and featured in their December, 2020 Newsletter.

VT CEE Feature: Amin Moghadam

VIBEs Lab PhD Student Amin Moghadam featured in the Virginia Tech Civil and Environmental Engineering website for his research and acheivements. Full Article Here: https://cee.vt.edu/News-Menu/CEE_Article_Cache/Instrumentedbridges.html