| dc.description.abstract | Steel reinforcement corrosion has been regarded as one of the major causes of
reinforced concrete (RC) structures failing prematurely, posing a serious structural
durability problem worldwide. Detailed assessment of corrosion-induced damage and
its effects on RC structures is critical for sustaining structural reliability and safety. This
study develops and examines the feasibility of acoustic emission (AE) monitoring and
data analysis methodologies to characterise corrosion-induced damage in RC
members, followed by an evaluation of the effect of corrosion on load behaviour.
Experimental investigations were conducted on a series of specimens of different
configurations, namely concrete cubes with steel bars for pull-out tests and RC beams
of different dimensions to be subjected to static and cyclic loading regimes. Focusing
on developing evaluation methods based on AE monitoring and data analysis, a
summary of work completed, and the associated findings are given as follows.
Characterisation of the concrete cracking using parametric and waveform analysis
was conducted to investigate the effect of corrosion on steel-concrete bond behaviour
in the pull-out tests of concrete cubes. It was found that a small amount of corrosion
(approximately 6%) could slightly increase the bond strength as a result of the rust
expansion and reactionary confinement of concrete. Corrosion was also found to be
able to mitigate the damage caused by cyclic loading. AE signal analysis indicates that
the concrete cracking mode during the steel-concrete de-bonding process has
changed as a result of steel corrosion.
Characterisation of load behaviour and failure mode of corroded RC beams was
conducted by flexural load tests aided by AE monitoring and digital image correlation
(DIC). The DIC strain mapping results and AE signal features revealed that corrosion
has an influence on the concrete cracking mechanism of the beam specimens.
Corrosion has also altered the failure mode of a shear-critical beam specimen series
to flexure owing to the change of steel-concrete bond behaviour.
Numerical simulation of AE wave front propagation in RC media and tomographic
evaluation of internal damage was implemented on one group of RC beam specimens
tested in this study. The numerical model of the specimens was discretised using
finite-difference grid meshing, and the different acoustic properties of steel and
concrete were defined. On this basis, simulation of AE wave front propagation
considering concrete cover cracking and steel rust layer formation was carried out
using the fast-marching method. The effect of corrosion-induced damage on the AE
rays was studied by examining non-linear ray tracing in the simulation. A tomographic
reconstruction approach that solved by the quasi-Newton method provided a potential
way to quantitatively evaluate the internal damage of RC beams using AE monitoring
data.
A novel method was developed for assessing the corrosion level in RC beams using
a data-driven approach. Normalization of AE data was applied using principal
component analysis to minimise variations in AE signal features caused by differences
in the geometrical and material properties of RC beams as well as in the AE monitoring
instrumentation setup. The machine learning models, including k-nearest neighbours
(KNN) and support vector machines (SVM), were trained using the normalised AE
features. The trained KNN models were found effective at predicting the corrosion
level in RC beams using the secondary AE signals as input, which could be acquired
from the cyclic loading of beams.
Key words: Steel Corrosion, Concrete cracking, Steel-Concrete Bond, Reinforced
Concrete Beam, Load Behaviour, Acoustic Emission, Digital Image Correlation,
Tomographic Reconstruction, Data-driven. | en |
