The Use of Acoustic Emission Intensity Analysis for the Assessment of Cover Crack Growth in Corroded Concrete Structures

In this paper, acoustic emission (AE) intensity analysis was utilized to assess the concrete cover cracking due to steel corrosion in reinforced concrete structures. A total of 30 reinforced concrete prism samples were tested under an accelerated corrosion test coupled with continuous AE monitoring using attached AE sensors. The samples were cast with three concrete cover thicknesses (20, 30 and 40 mm) around steel bars and were exposed to five percentages of steel mass loss: 1, 2, 3, 4, and 5 %. The cover cracking was monitored daily by visual inspection to detect and measure crack widths. Different AE signal parameters were continuously recorded during the tests, including number of hits, signal strength, energy, and amplitude. The acquired AE events were subjected to an intensity analysis of signal strength to estimate historic index (H (t)) and severity (\(S_r)\). In addition, a b value analysis was conducted on all AE data and the results were compared to those obtained from the intensity analysis. The results showed that increasing the cover thickness had no significant impact on AE parameters (number of hits, cumulative signal strength, cumulative energy, amplitude, H (t), and \(S_{r})\) at similar values of crack growth. Nonetheless, varying the cover thickness from 20 to 40 mm resulted in lower crack widths and slightly higher b values at the same levels of steel mass loss. It was also found that both H (t) and \(S_r\) showed a more evident correlation with the values of crack growth than did b values, regardless of cover thickness or percentage of steel mass loss. Finally, an intensity classification chart was developed to quantify the cover crack growth based on the values of H (t) and \(S_{r}\).     

Effect of initial composition on phase selection in Ni–Si powder blends processed by mechanical alloying

The effect of initial powder blend composition on the synthesis and formation mechanism of nickel silicide phases was investigated by mechanical alloying in Ni-60 and Ni-66.7?at.% Si powder blends. It was noted that the equilibrium NiSi phase started to form in the early stages of milling and that the amount of the NiSi phase in the milled powder increased with increasing milling time. Even though, under equilibrium conditions, a mixture of both the NiSi and NiSi₂ phases was expected to be present in the Ni-60?at.% Si composition and the stoichiometric NiSi₂ phase in the Ni-66.7?at.% Si composition, the NiSi phase was present in both the compositions investigated. However, while only the NiSi phase was present homogeneously in the Ni-60?at.% Si powder blend, both the NiSi phase and a very small amount of unreacted Si were present in the powder blend of Ni-66.7?at.% Si composition. This unexpected phase constitution in the milled powders was attributed to a partial loss of Si during mechanical alloying of the powder blends, confirmed by energy dispersive X-ray spectrometer analyses, and explained on a thermodynamic basis.     

Assessing abrasion performance of self-consolidating concrete containing synthetic fibers using acoustic emission analysis

The key objective of this investigation was to evaluate the abrasion resistance of self-consolidating concrete (SCC) with and without synthetic fibers (SynFs). The abrasion resistance of normal concrete was also investigated in this study for comparison. The abrasion test was performed on concrete specimens according to the rotating-cutter method along with continuous monitoring of acoustic emission (AE) using attached AE sensors. The effects of changing concrete type and incorporating various types (flexible and semi-rigid) and lengths of SynFs on the abrasion behaviour were investigated with the aid of AE analysis. AE signal characteristics such as amplitude, signal strength, number of hits, and duration were gathered during testing. Furthermore, the collected AE data was used to complete b-value analysis as well as intensity analysis resulting in three additional parameters: b-value, severity (S_r), and historic index (H(t)). The results showed that the AE parameters were directly correlated with the abrasion damage in all tested mixtures. Adding SynFs to all SCC mixtures enhanced their abrasion resistance. The flexible fibers variety exhibited better abrasion performance on average than the semi-rigid fibers. Meanwhile, longer fibers showed lower abrasion resistance than the shorter ones with the same type. The results also indicated that AE intensity analysis was able to determine the ranges for H(t) and S_r that identify the extent of damage due to abrasion of SynF-reinforced SCC.     

Effect of Mn-Bearing Dispersoids on Tensile Properties and Recrystallization Resistance of Al-3Mg-0.8Mn Rolled Alloy

Herein, the precipitation behavior of Mn-bearing dispersoids in Al-3Mg-0.8Mn (AA5454) alloy subjected to different heat treatments is investigated. The effects of Mn-bearing dispersoids on the tensile properties and recrystallization resistance of the abovementioned alloy during hot/cold rolling and postrolling annealing are evaluated. The results show that a low-temperature three-step heat treatment (275 °C/12 h + 375 °C/48 h + 425 °C/12 h) generates a higher number density of Mn-bearing dispersoids with finer sizes compared with the typical high-temperature heat treatment (430 °C/2 h + 480 °C/2 h + 525 °C/2 h), thus resulting in significantly improved tensile strengths of hot/cold-rolled sheets. After annealing at 300 °C, the yield strength (YS) of the alloy reached 196 MPa after hot rolling and 237 MPa after cold rolling, showing an improvement of 30%–32% over samples subjected to high-temperature heat treatment. In addition, the low-temperature heat treatment provides a higher recrystallization resistance after hot and cold rolling owing to the higher number density of Mn-bearing dispersoids and the lower fraction of dispersoid-free zones. The YS contributions of various strengthening components after hot and cold rolling are discussed based on constitutive models. The predicted yield strengths agree well with the experimental values.     

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching (HM), Histogram Equalization (HE), fuzzy technique, fuzzy type Π, and Contrast Limited Histogram Equalization (CLAHE). The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality. Different realistic datasets of different modalities and diseases are tested and implemented. Also, real datasets are tested in the simulation analysis.     

Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

With the increasing and rapid growth rate of COVID-19 cases, the healthcare scheme of several developed countries have reached the point of collapse. An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients, in such a way that positive patient can be treated and isolated. A chest radiology image-based diagnosis scheme might have several benefits over traditional approach. The accomplishment of artificial intelligence (AI) based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems. This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System (IFFA-DTLMS). The proposed IFFA-DTLMS model majorly aims at identifying and categorizing the occurrence of COVID19 on chest radiographs. To attain this, the presented IFFA-DTLMS model primarily applies densely connected networks (DenseNet121) model to generate a collection of feature vectors. In addition, the firefly algorithm (FFA) is applied for the hyper parameter optimization of DenseNet121 model. Moreover, autoencoder-long short term memory (AE-LSTM) model is exploited for the classification and identification of COVID19. For ensuring the enhanced performance of the IFFA-DTLMS model, a wide-ranging experiments were performed and the results are reviewed under distinctive aspects. The experimental value reports the betterment of IFFA-DTLMS model over recent approaches.     

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.     

An Optimal Algorithm for Resource Allocation in D2D Communication

The number of mobile devices accessing wireless networks is skyrocketing due to the rapid advancement of sensors and wireless communication technology. In the upcoming years, it is anticipated that mobile data traffic would rise even more. The development of a new cellular network paradigm is being driven by the Internet of Things, smart homes, and more sophisticated applications with greater data rates and latency requirements. Resources are being used up quickly due to the steady growth of smartphone devices and multimedia apps. Computation offloading to either several distant clouds or close mobile devices has consistently improved the performance of mobile devices. The computation latency can also be decreased by offloading computing duties to edge servers with a specific level of computing power. Device-to-device (D2D) collaboration can assist in processing small-scale activities that are time-sensitive in order to further reduce task delays. The task offloading performance is drastically reduced due to the variation of different performance capabilities of edge nodes. Therefore, this paper addressed this problem and proposed a new method for D2D communication. In this method, the time delay is reduced by enabling the edge nodes to exchange data samples. Simulation results show that the proposed algorithm has better performance than traditional algorithm.