Evaluation of compressive damage in concrete using ultrasonic nonlinear coda wave interferometry.

This study investigates the feasibility of nonlinear coda wave interferometry (NCWI) for evaluating compressive damage in concrete, with a particular focus on the interference caused by the compressive stress-induced slow dynamics. Slow dynamics refers to a phenomenon in which the stiffness of concrete immediately decreases after moderate mechanical conditioning and then logarithmically evolves back to its initial value over time. A series of experiments were conducted to validate this concept.

Fiber Optic-Based Durability Monitoring in Smart Concrete: A State-of-Art Review.

Concrete is the most commonly used construction material nowadays. With emerging cutting-edge technologies such as nanomaterials (graphene, carbon nanotubes, etc.), advanced sensing (fiber optics, computer tomography, etc.

A Novel Epilepsy Detection Method Based on Feature Extraction by Deep Autoencoder on EEG Signal.

Electroencephalogram (EEG) signals are the gold standard tool for detecting epileptic seizures. Long-term EEG signal monitoring is a promising method to realize real-time and automatic epilepsy detection with the assistance of computer-aided techniques and the Internet of Medical Things (IoMT) devices. Machine learning (ML) algorithms combined with advanced feature extraction methods have been widely explored to precisely recognize EEG signals, while among which, little attention has been paid to high computing costs and severe information losses.

Diffuse Ultrasonic Wave-Based Damage Detection of Railway Tracks Using PZT/FBG Hybrid Sensing System.

Damage detection of railway tracks is vital to ensure normal operation and safety of the rail transit system. Piezoelectric sensors, which are widely utilized to receive ultrasonic wave, may be disturbed in the railway system due to strong electromagnetic interference (EMI). In this work, a hybrid ultrasonic sensing system is proposed and validated by utilizing a lead-zirconate-titanate (PZT) actuator and a fiber Bragg grating (FBG) sensor to evaluate damage conditions of the railway tracks.

Grouting Quality Evaluation in Post-Tensioning Tendon Ducts Using Wavelet Packet Transform and Bayes Classifier.

The grouting quality of tendon ducts is very important for post-tensioning technology in order to protect the prestressing reinforcement from environmental corrosion and to make a smooth stress distribution. Unfortunately, various grouting defects occur in practice, and there is no efficient method to evaluate grouting compactness yet. In this study, a method based on wavelet packet transform (WPT) and Bayes classifier was proposed to evaluate grouting conditions using stress waves generated and received by piezoelectric transducers.