Degree Type

Thesis

Date of Award

2016

Degree Name

Master of Science

Department

Civil, Construction, and Environmental Engineering

Major

Civil Engineering

First Advisor

Simon Laflamme

Second Advisor

Kejin Wang

Abstract

Structural health monitoring has emerged as an important branch of civil engineering in recent times, with the need to automatically monitor structural performance over time to ensure structural integrity. More recently, the advent of smart sensing materials has given this field a major boost. Research has shown that smart sensing materials fabricated with conductive filler at a concentration close to the percolation threshold results in high sensitivity to strain due to the piezoresistive effect. Of particular interest to this research are cementitious sensors fabricated using carbon black fillers. Carbon black is considered because of its widespread availability and low cost over other conductive fillers such as carbon nanotubes and carbon nanofibers. A challenge in the fabrication of these sensors is that cementitious materials require a significant amount of carbon black to percolate, resulting in a loss in mechanical properties. This research investigates a new method to accelerate percolation of the materials, enabling cementitious sensors with fewer carbon black particles. A carbon black-based conductive paint that allows earlier percolation by facilitating conducting networks in cementitious sensors is used. The conductive paint consists of a block copolymer, SEBS (styrene-co-ethylene-co-butylene-co-styrene), filled with carbon black particles. The percolation thresholds of sensors fabricated both with and without conductive paint are, as well as their strain sensing characteristics and compressive strength. The study found that SEBS could successfully reduce the percolation threshold by 42%, and that samples with SEBS showed better electrical responses in dynamic conditions. Despite showing lower compressive strength, cementitious sensors fabricated with this novel conductive paint show promise for real time health monitoring applications.

DOI

https://doi.org/10.31274/etd-180810-5621

Copyright Owner

Irvin Jude Joseph Pinto

Language

en

File Format

application/pdf

File Size

76 pages

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