Concrete structures are exposed to aggressive environmental conditions that lead to corrosion of the embedded reinforcement and pre-stressing steel. Consequently, the safety of concrete structures may be compromised, and this requires a significant budgets to repair and maintain critical infrastructure. Prediction of structural safety can lead to significant reductions in maintenance costs by maximizing the impact of investments. The aim of this paper is to establish a framework to assess the reliability of existing post-tensioned concrete bridges. A time-dependent reliability analysis of an existing post-tensioned involving the assessment of Ynys-y-Gwas bridge has been presented in this study. The main cause of failure of this bridge was corrosion of tendons, making it a relevant case study to evaluate the effect of corrosion on bridge safety. Uncertainties associated with material properties, geometry, loads and corrosion parameters are taken into account. The probabilistic models of the uncertainties are combined with a non-linear finite element analysis to study the effect of tendons pitting corrosion on the post-tensioned concrete bridge. The limit state function considered is flexural strength. The Monte-Carlo simulation (MCS) method is used to compute the statistical parameters of the resisting bending moment through a MATLAB code running ABAQUS. It was found that the reliability index for the first year of bridge service life is below the minimum value acceptable for structures. The study confirmed that this bridge was a high risk structure due to its design and location. The proposed framework can be used by engineers and researchers as a tool to support decision for segmental post-tensioned (PT) bridges maintenance since they need for a regular inspection due to their risk to corrosion.
The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi
... Show MoreManganese sulfate and Punica granatum plant extract were used to create MnO2 nanoparticles, which were then characterized using techniques like Fourier transform infrared spectroscopy, ultraviolet-visible spectroscopy, atomic force microscopy, X-ray diffraction, transmission electron microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. The crystal's size was calculated to be 30.94nm by employing the Debye Scherrer equation in X-ray diffraction. MnO2 NPs were shown to be effective in adsorbing M(II) = Co, Ni, and Cu ions, proving that all three metal ions may be removed from water in one go. Ni(II) has a higher adsorption rate throughout the board. Co, Ni, and Cu ion removal efficiencies were 32.79%, 75
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