Modelling of carbon nanotube-based sensors for structural health monitoring applications
File(s)
Author(s)
Leonel David, Quinteros Palominos
Type
Thesis
Abstract
Despite substantial experimental progress, predictive numerical models that quantitatively link micro-scale damage mechanisms in carbon nanotube (CNT) composites to the macroscopic piezoresistive signal required for Structural Health Monitoring (SHM) remain scarce. To close this gap, this thesis develops and validates the micromechanical behaviour and fracture modelling of CNT-based composites, focusing on their self-sensing capabilities and on providing a quantitative modelling framework for SHM. While CNTs exhibit exceptional mechanical and electrical properties, their performance is limited by challenges such as dispersion and agglomeration. Advanced modelling techniques combining mean-field homogenization (MFH), FEM, and phase field fracture models address these issues.
A MFH framework is developed to predict the elastic properties of CNT-reinforced composites, considering CNT geometry, orientation and agglomeration. A two-parameter agglomeration model is utilised to quantify clustering's impact combined with a micromechanical fracture framework, which incorporates CNT pull-out and rupture mechanisms to predict fracture behaviour and toughening effects. Agglomeration decreases toughness by facilitating pull-out through reduced interfacial area and weaker stress transfer.
The thesis explores electrical and self-sensing CNT composites, modelling conductivity and piezoresistivity for structural SHM applications. A new electromechanical phase field fracture framework integrates MFH, piezoresistivity, and phase field methods to predict crack initiation and propagation under electromechanical loading. Computational experiments validate stress–strain and resistance predictions and simulate complex interactions.
Finally, the framework is applied to self-sensing smart concrete in reinforced-concrete structures, integrating piezoresistive equations and phase field fracture modelling with three-dimensional truss elements for electrodes, which can be extrapolated as rebars. Experimental validation confirms its predictive capabilities for SHM applications.
By providing the first validated, fracture–piezoresistive model of CNT composites, this thesis delivers a novel design and assessment tool for SHM systems and advances the broader field of multifunctional materials. Its findings contribute to materials science and structural engineering, advancing the development of smart and efficient composites.
A MFH framework is developed to predict the elastic properties of CNT-reinforced composites, considering CNT geometry, orientation and agglomeration. A two-parameter agglomeration model is utilised to quantify clustering's impact combined with a micromechanical fracture framework, which incorporates CNT pull-out and rupture mechanisms to predict fracture behaviour and toughening effects. Agglomeration decreases toughness by facilitating pull-out through reduced interfacial area and weaker stress transfer.
The thesis explores electrical and self-sensing CNT composites, modelling conductivity and piezoresistivity for structural SHM applications. A new electromechanical phase field fracture framework integrates MFH, piezoresistivity, and phase field methods to predict crack initiation and propagation under electromechanical loading. Computational experiments validate stress–strain and resistance predictions and simulate complex interactions.
Finally, the framework is applied to self-sensing smart concrete in reinforced-concrete structures, integrating piezoresistive equations and phase field fracture modelling with three-dimensional truss elements for electrodes, which can be extrapolated as rebars. Experimental validation confirms its predictive capabilities for SHM applications.
By providing the first validated, fracture–piezoresistive model of CNT composites, this thesis delivers a novel design and assessment tool for SHM systems and advances the broader field of multifunctional materials. Its findings contribute to materials science and structural engineering, advancing the development of smart and efficient composites.
Version
Open Access
Date Issued
2025-01-17
Date Awarded
01/05/2025
License URL
Advisor
Vito, Tagarielli
Emilio, Martinez-Pañeda
Sponsor
Chile
Grant Number
2020 - 72210161
Publisher Department
Department of Civil and Environmental Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)