193 publications from this institution
Big data (BD) in structural health monitoring for civil engineers has become possible because of recent advances in sensor networks, computing, information, and data acquisition systems technologies (SHM). The time-frequency analysis-based data-driven method provides...
Energy harvesting from piezoelectric materials is quite common and has been studied for the past few decades. But recently, there have been a lot of new advancements in harnessing energy via piezoelectric materials. In this regard, several studies were carried out in analytical chemistry. This paper provides a detailed review of different piezoelectric materials, their structures, their fabrication processes, and their applications in analytical chemistry. Detection of the various gases percentage in ambient air is a valuable analytical chemistry technique. Additionally, the benefits of using piezoelectric materials, i.e., crystal for gas and liquid chromatography, virus detection including COVID-19 virus detection, water determination, trace metal analysis and the ability to measure micro weights with quartz crystal with some other applications are also described in this review. Energy harvesting is incredibly important and must be implemented on a large scale. So, developing self-powering devices can resolve the problems, and piezoelectric materials are gaining interest day by day because these materials help in energy generation.
Undoubtedly, infrastructure is the backbone of the world's economies. This includes transportation networks, bridges, tunnels, subways, railways, shipyard cranes, water delivery systems, utilities, dams, various pipeline networks, power transmission, communication network, government centers, and large business centers, to cite a few examples. Resilience is fundamentally a theoretical concept. Yet ongoing and warranted reflection regarding this concept in the context of disaster and emergency management and mitigation, crisis management, and the protection of critical infrastructure, for instance, has brought this it into the policy making arena, where considerations concerning its practical applications are becoming important. While difficult, given the complexity of resilience, and its definitional ambiguity, the ability to assess such a concept helps to bridge the gap between theory and application, and between academic and policy circles. This chapter introduces an overview of the intelligent and resilient urban infrastructures to support smart cities. It discusses and compares different definitions of the resilience infrastructure and emerging and relevant topics such as big data (BD) and data mining (DM), methodologies for their implementation and the opportunities, challenges, and benefits of incorporating resilience using BD applications for smart cities. In addition, it endeavors to identify the requirements that support the implementation of resilience applications for smart city services. The review reveals that several opportunities are available to apply to the resilience of infrastructure in smart cities. However, there are still many issues and challenges to be addressed to achieve a better utilization of this concept, and the application of new methodologies for the evaluation of resilient systems that can provide specific and satisfactory results for resilience evaluation.
The objective of this study is to predict the fatigue life of carbon fibre/epoxy composite laminate sheets involving 12 balanced woven bidirectional layers with the same orientation angle [0/90°]. The composite sheets considered are subjected to variable amplitude block loadings with different negative and positive stress ratios. This objective is accomplished by designing an efficient artificial neural network (ANN) architecture, with taking into account effect of the residual strength from spectrum loading. The number of cycles to failure (N) is related to the residual strength of the structure for constant amplitude loading. A simple first order model is postulated that determines the residual strength at any point during the fatigue life as a function of the static strength and stress ratio by applying the two-parameter Weibull probability density distribution. Two neural network structures, a feed-forward neural network (FFNN) and a radial basis neural network (RBNN), are applied, trained and tested to predict the fatigue life based on four groups of data considered. These data include the maximum stress (σmax) and the stress ratio (R), the Smith-Watson-Topper (SWT) parameter, fatigue strength ratio (Ψ), or failure criterion with the fibre orientation. On the other hand, the validity of the SWT, Ψ and selected suitable failure criterion for present study including material, loading and orientation were checked and modified before they were used for training the designed ANNs. The results show improvement when using one input data (SWT, or Ψ, or failure criterion) instead of two input data (σmax and R) and for the case of one input data the best prediction is observed for failure criterion condition, followed by Ψ and SWT respectively. Moreover, the RBNN demonstrates better results as compared with those obtained by the FFNN.
The shaft is one of the important parts that make up the machine. Its main function is to support the transmission parts (such as gears, worm gears, etc.) for rotary motion and to transmit motion and power. According to the different loading conditions of the shaft, it can be divided into three types: the rotating shaft, the transmission shaft, and the mandrel. The structural design of the shaft includes determining a reasonable shape and all structural dimensions of the shaft. When designing the shaft, it is necessary to make its structure easy to process, measure, disassemble, and maintain, and strive to reduce the amount of labor required and improve labor productivity. The diameter required for each shaft section is related to the magnitude of the load on the shaft. This chapter examines shaft dimension design examples, and discusses the shaft design process from the aspects of force analysis and stress analysis, to increase the reader's understanding of correct shaft dimension design. Examples and solved/unsolved problems for computational shaft design are also presented. This chapter can be used as teaching material for courses in machine design for mechanical, industrial, and materials engineering majors in colleges and universities, and can also be used as a reference for scientific and technical personnel engaged in scientific and engineering calculations.
Sliding bearings are bearings that work under sliding friction, used in fast rotating rotor machines or planetary gears. The sliding bearing works smoothly, reliably, and without noise. Under the condition of liquid lubrication, the sliding surface is separated by lubricating oil without direct contact, which can also greatly reduce friction loss and surface wear, and the oil film also has a certain ability to absorb vibration. The part of the shaft supported by the bearing is called the journal, and the part that matches the journal is called the bearing bush. The theoretical models of the bearing should precisely describe real operating conditions of the bearing. In initial calculations, simplified models are often used. This chapter describes the design process for sliding bearings, depicting the particular design stages in the form of a structural chart with calculation examples, as well as design examples and solved/unsolved problems. This chapter can be used as teaching material for courses in machine design for mechanical and industrial engineering majors in colleges and universities, and can also be used as a reference for scientific and technical personnel engaged in scientific and engineering calculations.
The acoustic muffler development depends on optimizing its volume for high performance, it is of great importance in the industrial field to obtain a reduction in duct noise economically and efficiently. The main aim of this work is to optimize analytically the...
Studying the changes of the natural frequencies due to intermediate elastic support of laminated composites is usually need a lot of computational processes or difficult to estimate. The present study employs a new high performance method for natural frequency estimating in basalt fiber reinforced polymer (FRP) laminated, variable thickness plates with intermediate elastic support based on the finite strip transition matrix (FSTM) with response surfaces (RS) method. Author has found that the FSTM method is very effective. However, a large error of estimation remains for estimation of natural frequency due to the large number of an iteration implemented in FSTM algorithm to estimate the natural frequency. In the present study, a new data processing procedure is proposed to improve performance of estimations of natural frequency. The estimation responses for four of classical boundary conditions at the plate ends with different elastic restraint coefficients (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mrow><mml:mi>K</mml:mi></mml:mrow><mml:mrow><mml:mi>T</mml:mi></mml:mrow></mml:msub></mml:math>) are computed to obtain the first six frequency parameters (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="normal">Ω</mml:mi></mml:math>). As a result, the method reveals excellent performance of estimations of natural frequencies.
Modern machine tools with high speed machining capabilities could place rotating shafts, gears, and bearings under extreme thermal, static, and impact stresses, potentially increasing their failure rates. In this research, a gearbox damage detection strategy based on discrete wavelet transform (DWT), wavelet packet transform (WPT), support vector machine (SVM), and artificial neural networks (ANN) is presented. Three case studies are conducted to compare the classification performance of SVM kernel functions and ANN. First, a fault detection analysis based on DWT and WPT is carried out to extract the damage information from the gearbox’s raw vibration signal. In this step, wavelet coefficients obtained from DWT are characterized using statistical calculations. Energy characteristics of the gearbox signal are acquired using WPT and their statistical characteristics are also computed. These three sets of information extracted from wavelet transforms are utilized as the input to SVM and ANN classifiers. Secondly, the improved distance evaluation technique (IDE) is implemented to select the sensitive input features for SVM and ANN. The penalty parameter C and kernel parameter γ in SVM are also optimized using the grid-search method. Finally, the optimized features and parameters are input into SVM and ANN algorithms to detect gearbox damage. The result shows that gearbox damage detection using energy characteristics extracted from WPT (Case 2) or their statistical values as input features (Case 3) to the learning algorithms produces higher classification accuracies than using statistical values of the DWT coefficients as inputs (Case 1). In addition, RBF-SVM has the best classification performance in Case 2 and 3 while Linear-SVM has the best classification accuracy rate in Case 1 in damage detection average.
A critical problem facing data collection in structural health monitoring, for instance via sensor networks, is how to extract the main components and useful features for damage detection. A structural dynamic measurement is more often a complex time-varying process and therefore, is prone to dynamic changes in time-frequency contents. To extract the signal components and capture the useful features associated with damage from such non-stationary signals, a technique that combines the time and frequency analysis and shows the signal evolution in both time and frequency is required. Wavelet analyses have proven to be a viable and effective tool in this regard. Wavelet transform (WT) can analyze different signal components and then comparing the characteristics of each signal with a resolution matched to its scale. However, the challenge is the selection of a proper wavelet since various wavelets with varied properties that are to analyze the same data may result in different results. This article presents a study on how to carry out a comparative analysis based on analytic wavelet scalograms, using structural dynamic acceleration responses, to evaluate the effectiveness of various wavelets for damage detection in civil structures. The scalogram’s informative time-frequency regions are examined to analyze the variation of wavelet coefficients and show how the frequency content of a signal changes over time to detect transient events due to damage. Subsequently, damage-induced changes are tracked with time-frequency representations. Towards this aim, energy distribution and sharing information are investigated. The undamaged and damaged simulated comparative results of a structure reveal that the damaged structure were shifted from the undamaged structure. Also, the Bump wavelet shows the best results than the others.
This paper provides a deep learning-based methods for civil structures monitoring in aerial imagery from unmanned aerial vehicles (UAVs). This algorithm is prepared to increase the level of structures monitoring when combined with UAVs techniques. The case study presented herein is the use of drones to monitor the cracks in concrete structures. The structures monitoring using existing UAV method is mainly aimed at the extraction of structures state maps. At the same time, the influence of UAV self-motion on the detection accuracy of cracks has not been overcome, and it is difficult to meet the application of intelligent monitoring system for concert crack detection. Therefore a concrete crack detection using UAV perception method based on UAV imaging and deep learning algorithm fusion is proposed. First, the development architecture of deep convolutional neural network (CNN) in the field of computer vision for UAV generated image processing are introduced. Then, the existing crack image datasets and model performance evaluation metrics are summarized. Finally, UAV platform for crack detection in civil infrastructure are summarized. The results show that the proposed CNN network has achieved better performance, reaching 96.4%, 93.7%, 91.3%, and 125s in terms of accuracy, regression, F1-score, and training time, respectively, which can realize the automatic extraction of high-dimensional, complex, and abstract features of civil structures, in addition, the detection results can reduce interference, reduce detection error, obtain more completion, and clear the civil structures detection effect.
Pipelines, such as gas and utility pipeline systems and networks, are some of the most critical components of civil infrastructure. During the long-term operation of the pipeline, various types of diseases will occur. Pipeline damage may present serious environmental and economical problems. In order to solve the problem that traditional pipelines crack detection, a pipelines crack detection method based on image processing under complex background is proposed. A pipeline crack image segmentation model based on semantic segmentation is built, and cracks in high-resolution crack images are extracted by using the pipeline image segmentation model. The accuracy rate (P%), recall rate (R%), and F-score (F%) of the proposed method are recorded 89.3%,85.7%, and 80.4%, respectively. The results show that, compared with the existing algorithms, the proposed algorithm has a better detection effect and stronger generalization ability in complex pipeline scenes.
