Date of Award

2016

Document Type

Open Access Dissertation

Department

Civil and Environmental Engineering

Sub-Department

College of Engineering and Computing

First Advisor

Paul Ziehl

Abstract

The prevalence of aging and deteriorating infrastructure in the U.S. has raised concerns regarding its level of serviceability, reliability, and vulnerability to natural disasters. This issue has gained attention recently and efforts are being conducted to accelerate the delivery of enhanced nondestructive testing (NDT) and structural health monitoring (SHM) methods. Acoustic emission (AE) is a strong candidate for these applications due to its high sensitivity and potential for damage detection in different materials. However, several challenges associated with the technique hinder the development of automated, reliable, real-time SHM using AE.

This study aims to advance the use of AE for condition assessment of concrete structures by addressing two main challenges. The first is AE data filtering to exclude irrelevant noise and wave reflections. Effective filtering and data reduction enhances the quality of the data and lowers the cost of its transfer and analysis; ultimately increasing the reliability of the method. The second issue is detecting slow rate material degradation mechanisms in concrete. For example, alkali-silica reaction (ASR) affects civil infrastructure around the nation, and available condition assessment methods for this type of damage are either invasive or not feasible for field conditions. Despite the awareness of ASR concrete deterioration; there is lack of research investigating the ability of AE to detect and assess it. In addition, recent laboratory investigations have shown promising results in detecting and evaluating damage related to corrosion of steel in concrete using AE. However, the results have not been extended to field applications.

This dissertation includes three studies that address the aforementioned issues. In the first study, wavelet analysis was used to study the distribution of energy in AE signals in the time-frequency domain. Criteria to differentiate between AE signals from artificial sources (pencil lead breaks) and wave reflections were developed. The results were tested and validated by applying the developed filters on data collected from actual cracking during load testing of a prestressed concrete beam. The second study presents a laboratory test conducted to assess the feasibility of using AE to detect ASR damage in concrete. Accelerated ASR testing was undertaken with a total of fifteen specimens tested; twelve ASR and three control specimens. The results of this study showed that AE has the potential to detect and classify ASR damage. Relatively good agreement was obtained with standard ASR measurements of length change and petrographic examination. The third study discusses a field application for long-term, remote monitoring of damage due to corrosion of reinforcing steel and potential thermal cracking in a decommissioned nuclear facility. The structure was monitored for approximately one year and AE damage detection and classification methods were successfully applied to assess the damage at the monitored regions. This study also included an accelerated corrosion test conducted on a concrete block cut from a representative structure.

The studies included in this dissertation provide: 1) an innovative approach for filtering AE data collected during cracking of concrete, 2) a proof of concept study on detecting ASR damage using AE, and 3) field application on AE monitoring of corrosion damage in aging structure. The outcomes of this research demonstrate the ability of AE for condition assessment, structural health monitoring, and damage prognosis for in-service structures.

Rights

© 2016, Marwa A. Abdelrahman

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