| Summary: | In recent decades, there has been a growing interest in the performance of
buildings and infrastructure when subjected to severe loading conditions. Due to the
fact that concrete is a material that is frequently employed in the construction
industry, its performance under severe loading conditions, such as impact loading,
has been the subject of multiple investigations. Concrete structures may experience
localised impact from small, high-velocity projectiles resulting from blast-induced
fragments or flying objects produced by forces of nature such as tornadoes or
volcanoes. These missiles exhibit significant variations in their shapes and sizes, as
well as in their velocities, stiffness, and orientation upon impact. Consequently, they
cause a diverse range of damage to the structure. When concrete structures are
subjected to high-velocity impact loading, small fragments can be generated from
the back face spalling due to the low tensile strength of concrete. As a result,
of the impact event, these fragments have the potential to move at a high
velocity, which poses a risk to the safety of the occupants of the structure as well as
those who are in the surrounding area. Therefore, improving the strength of concrete
elements can reduce the hazards related to debris and thereby reducing the local
damage. Conventional concrete can behave in various different ways when subjected
to severe loading according to its brittle characteristics, tensile strength, and capacity
to absorb energy. Thus, it is necessary to investigate new types of protective
materials with the tensile capacity and ductility to absorb the impact energy and
therefore resist high-velocity impact loads. This research aims to investigate the
performance of textile-reinforced concrete (TRC) panels under high-velocity impact
loading.
The research method is comprised of a laboratory experimental and
numerical programme to investigate varying parameters on the impact performance
of TRC panels. In the experimental part, a comprehensive experimental programme
was performed to investigate the effect of different influential parameters (e.g.,
strength of concrete, velocity of projectile, and type of textile) on the impact
performance of TRC panels. A hundred and eight control and TRC panels were
subjected to high-velocity impact loads from a non-deformable hemispherical steel
projectile, which was fired from a compressed air gun, travelling with initial impact
velocities ranging from ∼60 m.s-1 to ∼160 m.s-1. The effect of flexural toughness on
impact resistance was also investigated by testing fifty-four control and TRC beams
under a four-point bending test.
Numerical analysis was performed using Abaqus/CAE software. The models
were first validated and compared with the experimental results, and then a parametric study was conducted to identify the effect of different design parameters
such as compressive strength of concrete, tensile strength of textile, thickness, and
grid spacing of textile on the impact resistance of concrete panels. The results
obtained from the parametric analysis were employed to develop an empirical model
to predict the penetration depth of TRC panels under high-velocity impact loading on
the basis of the empirical formula proposed by the US National Defence Research
Committee.
In the last phase of this research, a comparative investigation of six diverse
machine learning models, including Decision Tree (DT), Extreme Gradient Boosting
(XGB), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbours
(KNN), and Adaptive Boosting (ADB) was performed with the aim of optimising for
developing an accurate predictive model to predict the penetration depth of
concrete panels subjected to high-velocity projectile impact loading. The models
combined the experimental results of 195 samples based on experimental results
obtained from this research and the literature.
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