skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Vibration-Based Non-Destructive Evaluation of Concrete Structures

Abstract

Poster discussing the use of vibro-acoustic modulation for detection and localization of alkali silica reaction gel in concrete. The project is sponsored by LWRS. The poster discusses the technique as well as multiple specimens used in experimentation. It also looks at fusing the data for damage prediction using averaging techniques and machine learning.

Authors:
 [1]; ORCiD logo [1]
  1. Idaho National Laboratory
Publication Date:
Research Org.:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org.:
Light Water Reactor Sustainability
OSTI Identifier:
1546718
Report Number(s):
INL/EXP-19-55154-Rev000
DOE Contract Number:  
AC07-05ID14517
Resource Type:
S&T Accomplishment Report
Country of Publication:
United States
Language:
English
Subject:
22 - GENERAL STUDIES OF NUCLEAR REACTORS; Concrete Structures; Non-Destructive Evaluation; Machine Learning; Vibrations; Acoustics

Citation Formats

Miele, Sarah Ann, and Agarwal, Vivek. Vibration-Based Non-Destructive Evaluation of Concrete Structures. United States: N. p., 2019. Web. doi:10.2172/1546718.
Miele, Sarah Ann, & Agarwal, Vivek. Vibration-Based Non-Destructive Evaluation of Concrete Structures. United States. doi:10.2172/1546718.
Miele, Sarah Ann, and Agarwal, Vivek. Thu . "Vibration-Based Non-Destructive Evaluation of Concrete Structures". United States. doi:10.2172/1546718. https://www.osti.gov/servlets/purl/1546718.
@article{osti_1546718,
title = {Vibration-Based Non-Destructive Evaluation of Concrete Structures},
author = {Miele, Sarah Ann and Agarwal, Vivek},
abstractNote = {Poster discussing the use of vibro-acoustic modulation for detection and localization of alkali silica reaction gel in concrete. The project is sponsored by LWRS. The poster discusses the technique as well as multiple specimens used in experimentation. It also looks at fusing the data for damage prediction using averaging techniques and machine learning.},
doi = {10.2172/1546718},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {8}
}