Experimental investigation on the stability of bride girder against tsunami forces

Bridges are the most useful part in the transportation network. Any disruption of the bridge structures may hamper the whole transportation system. In the last recent tsunamis, numerous number of bridges were structurally damaged by the tsunami waves. Lack of proper provisions of tsunami forces in the design guidelines also contributes to the augmentations of the damage level. Therefore, proper evaluation of the tsunami forces on the bridge girder should be introduced in the design promptly. In this study, laboratory experiments were carried out to reveal the damage mechanism of the bridge girder by assessing the exerted tsunami forces. Both broken and unbroken waves were considered for the analysis. The results showed that measured forces were larger for broken waves than those of unbroken waves. Maximum force of the broken waves was 4.59 times as large as the hydrostatic pressure. Also, waves reached the peak value more rapidly for larger wave heights than those of smaller ones. Additionally, a girder that is placed at a higher position is much vulnerable to tsunami hazard.     

Experimental investigation and finite element modelling of the effects of flow velocities on a skewed integral bridge

This paper presents finite element modelling of the effects of different flow velocities on the structural behaviour of a skewed integral bridge. Flow velocities affect the scour depths at the piles of a bridge and thus affect its structural behaviour. Laboratory tests on a scaled-down hydraulic model along with numerical modelling were performed to simulate the structural behaviour of the scoured integral bridge. A finite element package was used for the numerical modelling work, and the displacements and strains corresponding to the measured locations on the physical model were extracted. The experimental and numerical results for the case of maximum scour depths were compared.     

An experimental investigation on bending stiffness and neutral axis depth variation of over-reinforced high strength concrete beams

The present work is an attempt to study the neutral axis variation and the evolution of the moment inertia with the loading of over reinforced high strength concrete sections in conjunction with ACI 318-05. In this sense, four high strength concrete beams, having different tension reinforcement quantities expressed as proportions of the balanced steel ratio (0.75ρ_b, 0.85ρ_b, ρ_b, 1.2ρ_b) were tested. Measurements of the deflection and the reinforcement and concrete strains of all specimens were made during the loading process. The load-neutral axis depth variation and the load-section stiffness curves were drawn. The slope of the line connecting the origin of the first crack to the initial yielding of the failure point in the neutral axis depth-load graphs shows the rate of ductility; ductile behaviour in the beam increases as the slope becomes steeper. Based on the results of this study, it is recommended that the modulus of elasticity of concrete E_c be reviewed and evaluated at a stress higher than 0.5f^′c for the determination of the cracked moment of inertia.     

A systematic literature review on wearable health data publishing under differential privacy

International Journal of Information Security - Wearable devices generate different types of physiological data about the individuals. These data can provide valuable insights for medical...     

A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis

The recognition of pathological voice is considered a difficult task for speech analysis. Moreover, otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysphonia that are caused by voice alteration of vocal folds and their accuracy is between 60%–70%. To enhance detection accuracy and reduce processing speed of dysphonia detection, a novel approach is proposed in this paper. We have leveraged Linear Discriminant Analysis (LDA) to train multiple Machine Learning (ML) models for dysphonia detection. Several ML models are utilized like Support Vector Machine (SVM), Logistic Regression, and K-nearest neighbor (K-NN) to predict the voice pathologies based on features like Mel-Frequency Cepstral Coefficients (MFCC), Fundamental Frequency (F₀), Shimmer (%), Jitter (%), and Harmonic to Noise Ratio (HNR). The experiments were performed using Saarbrucken Voice Database (SVD) and a privately collected dataset. The K-fold cross-validation approach was incorporated to increase the robustness and stability of the ML models. According to the experimental results, our proposed approach has a 70% increase in processing speed over Principal Component Analysis (PCA) and performs remarkably well with a recognition accuracy of 95.24% on the SVD dataset surpassing the previous best accuracy of 82.37%. In the case of the private dataset, our proposed method achieved an accuracy rate of 93.37%. It can be an effective non-invasive method to detect dysphonia.