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The present study aims to introduce an innovative entropy-based denoising technique to enhance the accuracy of TDoA (Time Difference of Arrival) in source localization techniques based on acoustic emissions (AE). The approach focuses on the challenging scenario of industrial pipelines in energy and transportation systems characterized by high level of noise. Conventional methods for estimating the TDoA of AE sources, are generally hindered by external interferences in near-industrial scenarios, leading to distorted AE signals. The proposed approach encompasses a comprehensive analysis of signal waveforms, integrating local entropy to effectively compensate for the presents of noise, and employing the Akaike Information Criterion (AIC) to estimate the TDoA. To evaluate the performance of the proposed method, a two-stage experimental campaign was conducted on a pressurized hydraulic circuit. The first stage involved verifying the statistical reliability of the proposed algorithm by employing the Hsu-Nielsen test (pencil lead break) at multiple points along a series of pipes. In the second stage, an ad-hoc system was devised to induce accelerated corrosion on the same piping while capturing raw acoustic emission waveforms. Various levels of Gaussian noise, reflecting distinct signal-to-noise ratios, were added to the recorded raw waveforms to simulate diverse industrial interference scenarios. The findings illustrate significant enhancement in the accuracy and repeatability of AE source localization. The proposed entropy-based denoising technique showcases substantial potential for advancing damage detection and localization within the Process & Power Industry.

In recent years, different research works have been conducted to evaluate the addition of nanometer materials to concrete materials. In this paper, the influence of Nanosilica on compressive strength, abrasive strength, durability, and improvements in the micro-structure of concrete are discussed. The results showed that the compressive strength of concrete samples with Nanosilica and silica fume were higher than the compressive strength of other samples without nanometer materials in all ages, as well as increasing the dosage percentage of Nanosilica led to higher levels of compressive strength. In the mix designs with an equal dosage percentage, samples containing Nanosilica have shown a higher level of strength in comparison to samples containing silica fume. The application of Nanosilica in self-compacting concrete resulted in higher level of compressive strength, flexural strength, abrasive strength, elasticity module, ultrasonic waves permeability velocity (UPV), and lower water absorption compared to samples without Nanoparticles. Despite the evidences which show the improvement in mechanical characteristics of concretes with Nanosilica-particles, further developments for the applicability of Nanoparticles for improving the characteristics of concrete require the right knowledge and higher control over the effective mechanisms of Nanoparticles on concrete’s structure.

Feed-forward control of hysteretic systems is a challenging task due to the hysteresis nonlinearity. Hysteresis models are utilized not only for identification, but also for hysteresis control. The feed-forward control, which is not an error-based (feedback-based) algorithm, plays a significant role in hysteresis control problems. Instead, it works based on knowledge about the process in the form of a mathematical model of the process. In feed-forward control problems, it is important to identify the inverse relationship of the output and input of the system, i.e., determining the mapping of the output and input of the system plays a key role in feed-forward controlling. This paper presents a new feed-forward controller model to control an actuator in a laboratory to tackle the restrictions of feedback control systems. For this purpose, first, a numerical model of a Proportional-Integral-Derivative (PID)-controlled actuator was created, and sets of numerical data of inputs and outputs of the plant were generated. Then, a least-squares support-vector machine (LS-SVM) hysteresis model was trained inversely on the generated data sets of the numerical modeling. Afterwards, to examine the efficacy of the proposed method for real-world hydraulic actuators in the presence of experimental errors and noise, sets of experimental data were obtained from physical modeling at KNTU’s Structural and Earthquake Engineering Laboratory (KSEEL). The results indicate the high performance of the proposed model.