Statistical Structural Health Monitoring in the Presence of Environmental Variability and Uncertainty

Abstract

The goal of this project is to develop statistical methodologies to tackle the huge data and diverse environmental variability facing the field of structural health monitoring. The primary objectives are:1. Design a statistical framework to handle the massive data generated by SHM systems2. Develop tools to explicate the diverse environmental conditions experienced by SHM systems while maintaining fidelity of damage detection 3. Implement and test the proposed methodology on real-life structures Accomplishments: I would conclude that we completed 2.5 out of the 3 primary goals. The remaining .5 is currently being taken up by colleagues at Los Alamos National Labs who are working on real life SHM systems on which our methodology is being tested.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2018
Accession Number
AD1070635

Entities

People

  • Luke Bornn

Organizations

  • Harvard University

Tags

Communities of Interest

  • Autonomy
  • Biomedical

DTIC Thesaurus Topics

  • Big Data
  • Computations
  • Damage Detection
  • Data Mining
  • Data Science
  • Detection
  • Engineering
  • Gaussian Processes
  • Information Science
  • Machine Learning
  • Monitoring
  • Monte Carlo Method
  • Sampling
  • Signal Processing
  • Statistics
  • Stochastic Processes
  • Structural Health Monitoring

Readers

  • Academic Conference Management
  • Acoustical Oceanography.
  • Systems Analysis and Design